TEST BANK for Business Statistics For Contemporary Decision Making 11th Edition by Ken Black. ISBN-1 by StudyGuide

Business Statistics, 11e (Black) Chapter 1 Introduction to Statistics 1) Virtually all areas of business use statistics in decision making. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 2) Statistics can be used to predict business in the future. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 3) Statistics are used to market vitamins. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 4) A list of final grades in an introductory class in business is an example of statistics. Answer: FALSE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 5) A graph of purchases made from one store location would be an example of statistics within a business context. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 6) The complete collection of all entities under study is called the sample. Answer: FALSE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics.

7) A portion or subset of the entities under study is called the statistic. Answer: FALSE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 8) A descriptive measure of the population is called a parameter. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 9) A census is the process of gathering data on all the entities in the population. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 10) Statistics is commonly divided into two branches called descriptive statistics and summary statistics. Answer: FALSE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 11) A descriptive measure of the sample is called a statistic. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 12) Gathering data from a sample to reach conclusions about the population from which the sample was drawn is called descriptive statistics. Answer: FALSE Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics.

13) Calculation of population parameters is usually either impossible or excessively time consuming and costly. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 14) The basis for inferential statistics is the ability to make decisions about population parameters without having to complete a census of the population. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 15) A variable is a numerical description of each of the possible outcomes of an experiment. Answer: TRUE Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.3: Explain the difference between variables, measurement, and data. 16) Variables and measurement data are interchangeable terms. Answer: FALSE Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.3: Explain the difference between variables, measurement, and data. 17) Measurements occur when a standard process is used to assign numbers to attributes or characteristics of a variable. Answer: TRUE Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.3: Explain the difference between variables, measurement, and data. 18) One piece of data includes a variety of variables. Answer: FALSE Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.3: Explain the difference between variables, measurement, and data. 19) A variable can take on different values. Answer: TRUE Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.3: Explain the difference between variables, measurement, and data. 3

20) All numerical data must be analyzed statistically in the same way because all of them are represented by numbers. Answer: FALSE Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 21) The manner in which numerical data can be analyzed statistically depends on the level of data measurement represented by numbers being analyzed. Answer: TRUE Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 22) The lowest level of data measurement is the ratio level. Answer: FALSE Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 23) The highest level of data measurement is the ratio level. Answer: TRUE Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 24) Numbers which are used only to classify or categorize the observations represent data measured at the nominal level. Answer: TRUE Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 25) Numbers which are used to rank-order the performance of workers represent data measured at the interval level. Answer: FALSE Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

26) Nominal and ordinal data are sometimes referred to as qualitative data. Answer: TRUE Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 27) Nominal and ordinal data are sometimes referred to as quantitative data. Answer: FALSE Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 28) With interval data, the zero point is a matter of convention and does not mean the absence of the phenomenon under observation. Answer: TRUE Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 29) Interval and Ratio data are sometimes referred to as quantitative data. Answer: TRUE Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 30) Big data refers to a standard set of variables collected from customers, suppliers, and staff. Answer: FALSE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 31) One goal of data visualization is to make complex data easier to understand. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

32) The main objective of business analytics is to transform data into meaningful information for business managers. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 33) Extracting and transforming data are two steps in data visualization. Answer: FALSE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 34) If a manager relies on his/her gut instinct to make critical business decisions, this is an example of business analytics in action. Answer: FALSE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 35) If big data has variety, then it can be said that the data are from several different sources such as videos, retail scanners, and the internet. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 36) Velocity refers to the speed with which data are available to the business for analysis. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 37) The term "garbage in, garbage out" refers to the volume of the data used by a business. Answer: FALSE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.

38) Big data can include unstructured data such as writings and photographs. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 39) Big data should encompass all four characteristics of variety, velocity, virtuous, and volume. Answer: FALSE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 40) Descriptive statistics focuses on what has happened or is happening within the business. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 41) Prescriptive analytics is the second step in big data analysis, following descriptive statistics. Answer: FALSE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 42) Prescriptive analytics is optimal for taking risk and uncertainty into account by looking at the effects of future actions. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 43) Simulation is a mathematical strategy one would expect to find within both predictive and prescriptive analytics. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 44) The three categories of business analytics could be described as describing what has happened, predicting potential relationships among data, and prescribing future decisions under uncertainty. Answer: TRUE Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 7

45) Which of the following statements about business statistics is not true? A) Virtually every area of business uses statistics in decision making. B) Presenting business statistics always requires the use of a specific graph called a bar chart. C) There is a wide variety of uses and applications of statistics in business. D) Business statistics can be used to forecast future values and predict trends. Answer: B Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 46) A book publisher uses statistics in decision-making. Of the following statistics, which would this publisher not consider in their decisions? A) Trends in purchases of hard copy and ebooks B) The cost of paper C) Trends in attendance at book clubs D) Trends in local grocery stores E) Revenue of competitors Answer: D Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 47) Which of the following would be the least helpful type of data to a car manufacturer when making business decisions? A) Economic data B) School attendance data C) Financial data D) Competitor data E) Employment data Answer: B Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.

48) Which of the following is not a graphical example of business statistics? A) A table that lists all customers B) A pie chart of careers at the company C) A graph of profits for the last ten years D) A bar graph of sales by product E) A chart of dividends paid out the past few years Answer: A Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 49) A news report states that sales of U.S. homes declined 3% during the previous month. Which type of business would be most likely to include this information in their business decisions? A) Car manufacturer B) Business attire C) Lumber company D) Deli stores E) Gas and oil companies Answer: C Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 50) If the U.S. Census indicated that in general, the population was moving to the northern states, what business decisions might that information impact? A) How much inventory to hold B) Whether to increase product prices C) Whether to build a new plant D) How much to spend on a new plant E) Where to locate a new plant Answer: E Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.

51) If the U.S. Census indicated that in general, wages having increased, what business decisions might that information impact? A) How much inventory to hold B) Whether to increase product prices C) Whether to build a new plant D) How much to spend on a new plant E) Where to locate a new plant Answer: B Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 52) If data indicated that a new product could serve as a cheaper substitute for a company's product, the CEO of the latter company might . A) increase prices B) decrease costs C) look for a new, unique use of his/her product D) try to put the competitor out of business E) close several production plans Answer: C Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 53) Many companies sponsor local sporting teams and events. What data is least likely to be part of the decision to sponsor that event? A) Whether that event is somehow tied to their product B) Whether costs will be decreased through the sponsorship C) The overlap of event attendees and the company's customers D) The perceived goodwill in the community of such a sponsorship E) Potential sales that could occur at the event Answer: B Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.

54) Rebecca Sear, Marketing Director of a regional restaurant chain, is directing a study to identify and assess the in-dining experience of the customers at one of the restaurants. She directs her staff to design a web-based market survey for distribution to all of the restaurant's 1265 customers who enjoyed a meal during the past 6 months. For this study, the set of 1265 customers is . A) a parameter B) a sample C) the population D) a statistic E) the frame Answer: C Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 55) Rebecca Sear, Marketing Director of a regional restaurant chain, is directing a study to identify and assess the in-dining experience of the customers at one of the restaurants. She directs her staff to design a web-based market survey for distribution to 100 of all the restaurant's customers who enjoyed a meal during the past 6 months. For this study, the set of 100 customers is . A) a parameter B) a sample C) the population D) a statistic E) the frame Answer: B Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 56) Sue Taylor, Director of Global Industrial Sales, is concerned by a deteriorating sales trend. Specifically, the number of industrial customers is stable at 1,500, but they are purchasing less each year. She orders her staff to search for causes of the downward trend by surveying all 1,500 industrial customers. For this study, the set of 1,500 industrial customers is . A) a parameter B) a sample C) the population D) a statistic E) the frame Answer: C Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 11

57) Sue Taylor, Director of Global Industrial Sales, is concerned by a deteriorating sales trend. Specifically, the number of industrial customers is stable at 1,500, but they are purchasing less each year. She orders her staff to search for causes of the downward trend by selecting a focus group of 40 industrial customers. For this study, the set of 40 industrial customers is . A) a parameter B) a sample C) the population D) a statistic E) the frame Answer: B Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 58) Miguel Hernandez, Senior Vice President of Human Resources at Memorial Hospital, is exploring the usage of nursing overtime hours in the emergency department during the last operating year (January 1, 2021, through December 31, 2021). Miguel intends to survey the emergency department nurses regarding their perception of overtime needs. For this survey the set of all emergency department nurses who worked at Memorial Hospital during the last operating year is . A) a parameter B) a sample C) the population D) a statistic E) the frame Answer: C Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 59) Miguel Hernandez, Senior Vice President of Human Resources at Memorial Hospital is exploring the usage of nursing overtime hours in the emergency department during the last operating year. Staffing records and emergency department visits for 20 days between the period of January 1, 2021, and December 31, 2021, are selected for analysis. For this study, the group of 20 days is a . A) parameter B) sample C) population D) statistic E) frame Answer: B Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 12

60) When a person collects information from the entire population, this is called a . A) parameter B) sample C) population D) census E) statistic Answer: D Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 61) Miguel Hernandez, Senior Vice President of Human Resources at Memorial Hospital is exploring the usage of nursing overtime hours in the emergency department during the last operating year. Staffing records and emergency department visits for all 365 days between the period of January 1, 21, and December 31, 2021, are selected for analysis. Miguel's collection method can best be classified as a . A) statistic B) census C) sample D) sorting E) parameter Answer: B Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 62) Sue Taylor, Director of Global Industrial Sales, is concerned by a deteriorating sales trend. Specifically, the number of customers is stable at 1,500, but they are purchasing less each year. She orders her staff to search for causes of the downward trend by surveying all 1,500 industrial customers. Sue is ordering a . A) statistic from the industrial customers B) census of the industrial customers C) sample of the industrial customers D) sorting of the industrial customers E) parameter of the industrial customers Answer: B Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics.

63) Sue Taylor, Director of Global Industrial Sales, is concerned by a deteriorating sales trend. Specifically, the number of customers is stable at 1,500, but they are purchasing less each year. She orders her staff to search for causes of the downward trend by selecting a focus group of 40 industrial customers. Sue is ordering a . A) statistic from the industrial customers B) census of the industrial customers C) sample of the industrial customers D) sorting of the industrial customers E) parameter of the industrial customers Answer: C Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 64) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system, and orders an inspection of "each and every payroll voucher issued since January 1, 2013." Pinky is ordering a . A) statistic from the payroll vouchers B) census of the payroll vouchers C) sample of the payroll vouchers D) sorting of the payroll vouchers E) parameter of the payroll vouchers Answer: B Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 65) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system, and orders an inspection of "every tenth payroll voucher issued since January 1, 2013." Pinky is ordering a . A) statistic from the payroll vouchers B) census of the payroll vouchers C) sample of the payroll vouchers D) sorting of the payroll vouchers E) parameter of the payroll vouchers Answer: C Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics.

66) On discovering an improperly adjusted drill press, Jack Joyner, Director of Quality Control, ordered a 100% inspection of all castings drilled during the evening shift. Jack is ordering a . A) statistic from the castings B) census of the castings C) sample of the castings D) sorting of the castings E) parameter of the castings Answer: B Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 67) On discovering an improperly adjusted drill press, Jack Joyner, Director of Quality Control, ordered an inspection of every fifth casting drilled during the evening shift. Jack is ordering a . A) statistic from the castings B) census of the castings C) sample of the castings D) sorting of the castings E) parameter of the castings Answer: C Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 68) The process of summarizing sample data is called . A) inferential statistics B) nominal data C) descriptive statistics D) deferential statistics E) nonparametric statistics Answer: C Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics.

69) A cancer research group was interested in determining the percentage of women 40 years or older that have regularly scheduled mammograms. To accomplish this, they surveyed 500 women in this age group and based on the 155 women that responded affirmatively, estimated the percentage of all women in this age group that have regularly scheduled mammograms. This process is an example of . A) nonparametric statistics B) nominal data C) descriptive statistics D) inferential statistics E) census Answer: D Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 70) A local manufacturing plant randomly selected 200 items from a production run and 9 of them are defective. The proportion of defective items in this sample is a . A) parameter B) sample C) population D) statistic E) frame Answer: D Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 71) Using data from a group to generalize to a larger group involves the use of . A) descriptive statistics B) inferential statistics C) population derivation D) sample persuasion E) relative level data Answer: B Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics.

72) A student makes an 82 on the first test in a statistics course. From this, she estimates that her average at the end of the semester (after other tests) will be about 82. This is an example of . A) descriptive statistics B) inferential statistics C) population derivation D) sample persuasion E) relative level data Answer: B Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 73) Jessica Salas, President of Salas Products, is reviewing the warranty policy for her company's new model of automobile batteries. Life tests performed on a sample of 100 batteries indicated an average life of seven years under normal usage. Jessica recommended a six-year warranty period for the new model. This is an example of _. A) descriptive statistics B) executive forecasting C) population derivation D) sample persuasion E) inferential statistics Answer: E Diff: 3 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 74) On discovering an improperly adjusted drill press, Jack Joyner, Director of Quality Control, ordered an inspection of every fifth casting drilled during the evening shift. Less than 1% of the sampled castings were defective; so, Jack released the evening shift's production to assembly. This is an example of . A) nonparametric statistics B) nominal data C) descriptive statistics D) inferential statistics E) judgmental statistics Answer: D Diff: 3 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics.

75) A new sales person is paid a commission on each sale. This person made $2,000 in commission his first month on the job. From this he concludes that he will make $24,000 during his first year. This is an example of . A) inferential statistics B) nominal data C) descriptive statistics D) deferential statistics E) nonparametric statistics Answer: A Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 76) A market researcher is interested in determining the average income for families in Duval County, Florida. To accomplish this, she takes a random sample of 400 families from the county and uses the data gathered from them to estimate the average income for families in the entire county. This process is an example of . A) nonparametric statistics B) nominal data C) descriptive statistics D) inferential statistics E) census Answer: D Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 77) The Universal Pulp Company has a plant in Portland, Oregon. Management wants to determine the average number of sick days taken per worker in this plant in 2021. To do this, management gathers records on all the workers in the plant and averages the number of sick days taken in 2021 by each worker. This process is using . A) nonparametric statistics B) nominal data C) descriptive statistics D) inferential statistics E) a census Answer: E Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics.

78) The Magnolia Swimming Pool Company wants to determine the average number of years it takes before a major repair is required on one of the pools that the company constructs. The president of the company asks Rick Johnson, a company accountant, to randomly contact fifty families that built Magnolia pools in the past ten years and determine how long it was in each case until a major repair. The information will then be used to estimate the average number of years until a major repair for all pools sold by Magnolia. The average based on the data gathered from the fifty families can best be described as a . A) parameter B) sample C) population D) statistic E) frame Answer: D Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 79) The Chamber of Commerce wants to assess its membership's opinions of the North American Free Trade Agreement. One-hundred of the 2,000 members are randomly selected and contacted by telephone. Seventy-five reported an overall favorable opinion, and twenty-five reported an overall unfavorable opinion. The proportion, 0.75, is a . A) parameter B) statistic C) population D) sample E) frame Answer: B Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 80) What proportion of San Diego's registered voters favors trade restrictions with China? In an effort to determine this, a research team calls every registered voter in San Diego and contacts them. The proportion determined from the data gathered is a . A) parameter B) sample C) population D) statistic E) frame Answer: A Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 19

81) A researcher wants to know the average variation in the altimeters of small, privately owned airplanes. The task of determining this is expensive and time consuming, if even possible, given the large number of such airplanes. The researcher decides to use government records to randomly locate the owners of ten such planes and then get permission to test the altimeters in those planes. When the researcher is done, he will use the data gathered from the group of ten to reach conclusions about all small, privately owned airplanes. This process can best be described as . A) data statistics B) research statistics C) descriptive statistics D) inferential statistics E) nonparametric statistics Answer: D Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 82) A researcher wants to know the average variation in the altimeters of small, privately owned airplanes. The task of determining this is expensive and time consuming, if even possible, given the large number of such airplanes. The researcher decides to use government records to randomly locate the owners of ten such planes and then get permission to test the altimeters in those planes. When the researcher is done, he will use the data gathered from the group of ten to reach conclusions about all small, privately owned airplanes. The average variation computed using the data gathered on the group of ten airplanes is best described as a _ . A) measurement B) data C) statistic D) parameter E) census Answer: C Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 83) Which of the following is not a random variable when flipping a coin? A) Assigning 1 when Tail and 0 when Head B) Assigning 0 when Head and 1 when Tail C) The list of outcomes Head and Tail D) The number of Heads E) Assigning 1 when Tail or Head Answer: E Diff: 3 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.3: Explain the difference between variables, measurement, and data. 20

84) Which of the following measurement processes is least likely to yield usable data? A) Counting the number of shoppers entering the department store between 12 pm and 2 pm. B) Studying cell phone bills and recording the number of text messages sent per month. C) Performing a consumer survey of preferences in fast food chains. D) Asking students to list three things that are important to them. E) Calculating the percent of college students who work at least 20 hours while attending school. Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.3: Explain the difference between variables, measurement, and data. 85) Which of the following statements is correct? A) Business researchers rarely give attention to collecting meaningful data. B) Variables are data that can be directly used for decision making. C) Valid data are the lifeblood of business statistics. D) Measurements never need to be defined by the business researcher. E) Business statistics are extremely complex and hard to use for decision making. Answer: C Diff: 3 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.3: Explain the difference between variables, measurement, and data. 86) The lowest level of data measurement is . A) interval level B) ordinal level C) nominal level D) ratio level E) minimal level Answer: C Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 87) Which of the following operations is meaningful for processing nominal data? A) Addition B) Multiplication C) Ranking D) Counting E) Division Answer: D Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

88) Which scale of measurement has these two properties: linear distance is meaningful and the location of origin (or zero point) is arbitrary? A) Interval level B) Ordinal level C) Nominal level D) Ratio level E) Minimal level Answer: A Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 89) Which scale of measurement has these two properties: linear distance is meaningful and the location of origin (or zero point) is absolute (or natural)? A) Interval level B) Ordinal level C) Nominal level D) Ratio level E) Relative level Answer: D Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 90) Sue Taylor, Director of Global Industrial Sales, is concerned by a deteriorating sales trend. Specifically, the number of customers is stable at 1,500, but they are purchasing less each year. She orders her staff to search for causes of the downward trend by surveying all 1,500 industrial customers. One question on the survey asked the customers: "Which of the following best describes your primary business: a. manufacturing, b. wholesaler, c. retail, d. service." The measurement level for these possible responses is . A) interval level B) ordinal level C) nominal level D) ratio level E) relative level Answer: C Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

91) A question in a survey of microcomputer users asked: "Which operating system do you use most often: a. Apple OS 7, b. MS DOS, c. MS Windows 95, d. UNIX." The measurement level for these possible responses is . A) nominal level B) ordinal level C) interval level D) ratio level E) relative level Answer: A Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 92) Which of the following operations is meaningful for processing ordinal data, but is meaningless for processing nominal data? A) Addition B) Multiplication C) Ranking D) Counting E) Division Answer: C Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 93) Sue Taylor, Director of Global Industrial Sales, is concerned by a deteriorating sales trend. Specifically, the number of customers is stable at 1,500, but they are purchasing less each year. She orders her staff to search for causes of the downward trend by surveying all 1,500 industrial customers. One question on the survey asked the customers: "How many people does your company employ? The measurement level for this question is . A) interval level B) ordinal level C) nominal level D) relative level E) ratio level Answer: E Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

94) A consumer has been asked to rank five cars based upon their desirability. This level of measurement is _. A) interval level B) ordinal level C) nominal level D) ratio level E) relative level Answer: B Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 95) Morningstar Mutual Funds analyzes the risk and performance of mutual funds. Each mutual fund is assigned an overall rating of one to five stars. One star is the lowest rating, and five stars is the highest rating. This level of measurement is . A) ordinal level B) interval level C) nominal level D) ratio level E) relative level Answer: A Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 96) A level of data measurement that has an absolute zero is called . A) interval level B) ordinal level C) nominal level D) ratio level E) relative level Answer: D Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

97) A person has decided to code a particular set of sales data. A value of 0 is assigned if the sales occurred on a weekday, and a value of 1 means it happened on a weekend. This is an example of . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: C Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 98) Members of the accounting department's clerical staff were asked to rate their supervisor's leadership style as either (1) authoritarian or (2) participatory. This is an example of . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: C Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 99) A market research analyst has asked consumers to rate the appearance of a new package on a scale of 1 to 5. A 1 means that the appearance is awful while a 5 means that it is excellent. The measurement level of this data is . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: B Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

100) The social security number of employees would be an example of what level of data measurement? A) Interval level data B) Ordinal level data C) Nominal level data D) Ratio level data E) Relative level data Answer: C Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 101) Sales of a restaurant (in dollars) are an example of what level of data measurement? A) Interval level data B) Ordinal level data C) Nominal level data D) Ratio level data E) Relative level data Answer: D Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 102) Grades on a test range from 0 to 100. This level of data is . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: D Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

103) If it were not for the existence of an "absolute zero," ratio data would be considered the same as . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: A Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 104) Scholastic Aptitude Test scores are an example of what type of measurement scale? A) Interval level data B) Ordinal level data C) Nominal level data D) Ratio level data E) Relative level data Answer: A Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 105) Which types of data are normally called metric data? A) Interval or ratio level data B) Ordinal or nominal level data C) Nominal or ratio level data D) Ratio or ordinal level data E) Relative or ratio level data Answer: A Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

106) Which types of data are normally called nonmetric data? A) Interval or ratio level data B) Ordinal or nominal level data C) Nominal or ratio level data D) Ratio or ordinal level data E) Relative or ratio level data Answer: B Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 107) How much inventory do Christmas tree sales lots keep? A researcher goes from location to location around the city counting the number of trees in each lot. These numbers most likely represent what level of data? A) Interval level B) Ordinal level C) Nominal level D) Ratio level E) Relative level Answer: D Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 108) During the Valentine's season, different offices in a company are encouraged to decorate their doors. A committee then goes around and ranks the doors according to how well the doors are decorated. The best door gets a ranking of "1"; the second best gets a ranking of "2", etc. The numbers of these rankings represent which level of data? A) Interval level B) Ordinal level C) Nominal level D) Ratio level E) Relative level Answer: B Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

109) A large manufacturing company in Indianapolis produces valves for the chemical industry. According to specifications, one particular valve is supposed to have a five-inch opening on the side. Quality control inspectors take random samples of these valves just after the hole is bored. They measure the size of the hole in an effort to determine if the machine needs to be adjusted. The measurement of the diameter of the hole represents which level of data? A) Interval level B) Ordinal level C) Nominal level D) Central level E) Ratio level Answer: E Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 110) A marketing demographic survey is undertaken to determine the market potential for a new product. One of the questions asked is: What type of residence do you live in? Respondents are offered several possible answers including: house, apartment, or condominiums. In order to computerize the survey answers, the responses are coded as a 1 if the answer is "house", a 2 if the answer is an "apartment", and a 3 if the answer is a "condominium". These numbers, 1, 2, and 3, are examples of which level of data? A) Interval level B) Ordinal level C) Nominal level D) Ratio level E) Relative level Answer: C Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

111) A marketing survey is conducted to ascertain the market potential of several new products. A series of focus groups is used to conduct this survey. At the end of one of the sessions, the group members are asked to rank the remaining eight products in order of desirability. A one indicates the most favored product and an eight is awarded to the least desirable. These numbers are examples of which level of data? A) Interval level B) Ordinal level C) Nominal level D) Ratio level E) Relative level Answer: B Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 112) A business is attempting to find the best small town in the United States in which to relocate. As part of the investigation, the elevations of all small towns in the United States are researched. Some towns are located high in the Rockies with elevations over 8,000 feet. There are even some towns located in the south central valley of California with elevations below sea level. These elevations can best be described as which level of data? A) Interval level B) Ordinal level C) Nominal level D) Ratio level E) Relative level Answer: A Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. 113) A manager was asked to rate the performance of his employees on a scale of 1 to 6. A 1 means that the performance is awful while a 6 means that it is excellent. The measurement level of this data is . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: B Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.

114) One of the main ways to organize the study of statistics is to divide it in two branches. These two branches are _ statistics and statistics. A) positive; normative B) descriptive; normative C) positive; inferential D) descriptive; inferential E) positive, macro Answer: D Diff: 1 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. 115) You are the owner of a camping site that has a small pond with fish, and you want to know approximately how many fish are currently in the pond. For this purpose, you catch 30 fish and mark them with a special ink that will take a few days to be washed away. The ink doesn't affect the fish in any way. The fish are returned promptly to the pond after being marked. The next day at the same time of day you return and catch 30 fish, and you find out that 5 of these fish are marked. A) This is an example of descriptive statistics, because you are describing the number of fish in the pond. B) This is not an example of statistics. C) This is an example of inferential statistics, because you will use the catch on the second day to infer the population of fish. D) This is an example of descriptive statistics as the goal was to determine the proportion marked out of the 30 caught the second day. E) This procedure would not allow you to estimate the population of fishes. Answer: C Diff: 3 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context. Bloom's level: Application

116) You are the owner of a camping site and want to estimate the average age of your customers. For this purpose, you select a representative sample of your clients and offer them a discount on their next visit as compensation for filling out a short questionnaire that includes relevant age intervals. The average age of your customers being estimated through these responses is . A) a measurement B) data C) a statistic D) a parameter E) a census Answer: D Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. Bloom's level: Application 117) You are the owner of a camping site and want to estimate the average age of your customers. For this purpose, you select a representative sample of your clients and offer them a discount on their next visit as compensation for filling out a short questionnaire that includes relevant age intervals. The average age of the customers who fill out the questionnaire is . A) a measurement B) data C) a statistic D) a parameter E) a census Answer: C Diff: 2 Response: See section 1.1 Basic Statistical Concepts Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. Bloom's level: Application

118) You are the owner of a camping site and want to estimate the average age of your customers. For this purpose, you select a representative sample of your clients and offer them a discount on their next visit as compensation for filling out a short questionnaire that includes relevant age intervals: "Your age is (a) 30 or younger, (b) 30 to 40, (c) 40 to 50, (c) 50 to 60, (d) 60 or older." This is an example of . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: B Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. Bloom's level: Application 119) You are the owner of a camping site and want to estimate the level of customer satisfaction among your clients. For this purpose, you select a representative sample of your clients and offer them a discount on their next visit as compensation for filling out a short questionnaire. One question specifically says, "How satisfied are you with your experience, on a scale from (1) to (5), where (1) is 'very dissatisfied' and (5) is 'very satisfied'?" This is an example of . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: B Diff: 1 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. Bloom's level: Application

120) You are the owner of a camping site and want to evaluate the feasibility of opening earlier during the year. For this analysis, you obtain the average maximum and minimum local daily temperatures for early spring. This is an example of . A) interval level data B) ordinal level data C) nominal level data D) ratio level data E) relative level data Answer: A Diff: 2 Response: See section 1.2 Data Measurement Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio. Bloom's level: Application 121) A business manager is looking to hire someone who can use large data sets to create business models that can then be used to help the manager make better decisions. The manager is looking to hire someone with . A) mathematics skills B) science skills C) business analytics skills D) qualitative analysis skills E) business decision skills Answer: C Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 122) A business manager has access to a large data set that includes complex information that would be difficult to process with traditional data management. These data would be referred to as . A) big data B) business data C) mega data D) business analytics E) mined data Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

123) Data visualization is a strategy used to . A) help analysts see the data points B) convey information as a visual object C) arrange data so that it can be imported into a statistical analysis program D) plan a research project E) transform data into inferential statistics Answer: B Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 124) Which of the following would not be a potential source of data for a furniture manufacturing business? A) Timber production B) Social media C) Customer purchases D) Operations data E) Dietary data Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 125) By transforming data from social media and competitors, an analyst was able to identify a relationship between an increase in the number of positive online comments about the company and negative marketing ads from its competitors. This relationship was most likely found through . A) the process of data visualization B) the production of data C) the process of data elimination D) the process of data mining E) the managing of data mining Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

126) Big data can be seen as a large amount of either organized or unorganized data that is analyzed to . A) confirm and justify a decision B) disprove and refute an assumption C) make an informed decision or evaluation D) structure and design a methodology E) process and eliminate Answer: B Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 127) An international social media company stores approximately 300 billion images and 1.2 trillion posts. Given just this information, which of the vectors of big data is the most likely focus of this company's data collection? A) Volume B) Velocity C) Variety D) Veracity E) Visualization Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 128) The need for new methodologies and processing techniques regarding business data has arisen because . A) big data sources have become too large and complex B) privacy issues related to business data sources C) national security concerns regarding data sources D) the lack of industry needs for big data sources E) decrease in availability of data Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.

129) The CIO for a large hospitality business has been inundated with information and data regarding customer needs and spending preferences. However, her in-house data analytics team has advised her that what she really needs is to develop an understanding of relationships. With this knowledge she should will be able to recognize new patterns and undiscovered trends. This category of analytics is called . A) predictive B) prescriptive C) descriptive D) statistical inference E) production Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 130) Online retailers are consistently pursuing new ways to improve market share and establish better relations with customers. One of the most common methods is to provide recommendations when customers place an order based upon similar or complementary items. These most likely patterns in purchases would most be an example of what type of analytics . A) predictive B) data mining C) financial D) descriptive E) time sensitive Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 131) A company is concerned that the data it has acquired has become so large that its practical use is severely limited) They decide to extract patterns from its present data, increasing its essential value) This would be an example of . A) data visualization B) business optimization C) data mining D) volume analysis E) predictive mining Answer: C Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

132) A CEO wants to depict information in creative ways by effectively using bars charts and line graphs. This strategy is an example of . A) data extraction B) data mining C) data interpretation D) data visualization E) data elimination Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 133) Future Video, a gaming company, is awaiting a marketing survey that will tell them how well and in what markets their latest game console is selling. The VP of Marketing has to develop contingency plans for the investors based upon this information. What type of analytics would the VP most likely be using? A) Descriptive B) Visual C) Prescriptive D) Time-series E) Volume Answer: C Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 134) Which is not a primary goal of data mining? A) Turning raw data into useful information B) Discovering and interpreting useful information C) Converting data into useful forms D) Making date accessible to business analytics users E) Deriving results that build a consensus Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

135) Which of the following is not true regarding data mining? A) Most major industries utilize data mining. B) Data mining is performed to confirm a preconceived hypothesis. C) Data mining is often performed through a database management system. D) Data mining should be used in making future business decisions. E) The first three steps of data mining is extract, transform and load. Answer: B Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 136) The last three steps of data mining are . A) extract, transform and load B) manage, extract and transform C) store, extract and load D) extract, manage and load E) load, manage, make available to others Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 137) The ultimate goal of data mining is . A) to make data accessible and usable to the business analyst B) to provide visual representation of data C) to develop a database management system D) to study frequency distributions E) to draw a distinction between variety and velocity Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

138) The CFO of a mid-level investment firm reports that the company has lost thousands of dollars through its data mining process due to poor data accuracy and quality. She is addressing an issue related to . A) veracity B) volume C) business intelligence D) descriptive analytics E) sampling distribution Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 139) In a speech, the COO commented on the changes that had taken place in the industry, specifically the amount of information decision makers have available. This is primarily referring to an area called . A) descriptive analytics B) application processing C) data mining D) big data E) data visualization Answer: D Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 140) Bubble charts where the size of each bubble refers to that item's size compared to the various other items shown, are examples of . A) mining visualization B) statistical analysis C) data extract D) descriptive analytics E) data visualization Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

141) If a company is concerned that the data they have received may contain some false information, they are concerned about the of the data. A) variety B) veracity C) volume D) velocity E) valorous Answer: B Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 142) If a company receives a lot of data within a short amount of time, then the data has both and . A) variety; velocity B) velocity; veracity C) veracity; volume D) velocity; volume E) variety; veracity Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 143) A company has collected data that they believe will help them identify the characteristics of their top customers. After some analysis, it is discovered that they need data on the age and income of these customers. Therefore, the company needs data that has more . A) variety B) velocity C) customers D) veracity E) purchases Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.

144) If an analyst needed to explain the primary differences between data velocity and veracity, they would most likely say that velocity was related to while veracity was related to . A) reliability; size B) speed; different sources C) size; reliability D) different sources; reliability E) speed; reliability Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 145) If an analyst needed to explain the primary differences between data variety and volume, they would most likely say that variety was related to while volume was related to . A) reliability; speed B) speed; different sources C) size; reliability D) different sources; size E) speed; reliability Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 146) Which best describes veracity? A) The data are credible B) The amount of information is sufficient C) The data originates from a variety of sources D) The data are current E) The data represent a specific industry Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.

147) During her lecture Professor Lewis stated that extracting and storing data will not ensure business value. The use of big data requires that the user . A) design flashy graphs and charts B) extrapolate important insights from the data C) incorporate data from executive staff D) place a time limit on the collection of data E) use data collection methods used by your competition Answer: B Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 148) Volume, variety, velocity and veracity are four important characteristics associated with . A) big data B) descriptive analytics C) predictive analytics D) metric analytics E) business analytics Answer: A Diff: 1 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them. 149) A database management software system is needed for big data to enable users to . A) define, create, maintain, and control access to the database B) identify, communicate, and compete with the competition C) redefine, test, and retool products D) maintain, define, and extract data on the competition E) transform, load, and manage changes in regulations Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

150) After acquiring a major investment firm, managers of the acquiring company needed a process to transform the mountains of the acquired company's data into useful business information. This can be done with . A) extracting management B) visualization analytics C) data mining D) tableau-produced analytics E) spectrum mining Answer: C Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 151) Which if the following best defines predictive modeling? A) A process used to determine descriptive statistics of each variable B) A process used by doctors to choose prescriptions C) A process used to determine relationships among variables D) A process used to determine if data have veracity E) A process used to collect data from various sources Answer: C Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 152) The business phrase "garbage in, garbage out" would most likely be attributed to A) veracity of data B) spectrum mining of data C) volume of data D) exponential growth of data E) employment opportunities in data Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.

153) A software package defined as a category of computer graphics products used to create graphical displays and interfaces for software applications would be used in . A) data monitoring B) measuring the veracity of data C) data visualization D) data mining E) prescriptive analytics Answer: C Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 154) A company wishes to establish a system that can continually and automatically process new data to improve recommendations and provide better decision options, you are likely dealing in the area of . A) prescriptive analytics B) extracting analytics C) visualization D) tableau-produced bar analytics E) descriptive analytics Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 155) Consumer information, financial reports, supply chain and human resource information are examples of . A) data visualization B) velocity analytics C) descriptive analytics D) statistical inference E) big data Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics.

156) The potential misuse of statistical data relates to the area of . A) statistical inference B) computer interpretation C) descriptive analytics D) business ethics E) nonparametric behavior Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 157) Utilizing visual technology to convey information to a diverse audience with a wide range of backgrounds would be an example of . A) data mining B) prescriptive visualization C) data visualization D) velocity analytics E) network analysis Answer: C Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 158) The process of finding data from numerous sources can be defined as . A) descriptive analytics B) data extraction C) data visualization D) big data E) artificial intelligence Answer: B Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 159) Most traditional introductory statistics courses focus instruction in the area of A) network functioning B) device mobility C) descriptive analytics D) discrete visualization E) visualized forecasting Answer: C Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 46

160) Simulation, statistical modeling, time-series and regression are topics in . A) predictive analytics B) prescriptive analytics C) data visualization D) network analytics E) information graphics Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 161) The categories of business analytics are most commonly done in which order? A) Predictive, prescriptive, then descriptive B) Descriptive, prescriptive, then predictive C) Prescriptive, descriptive, then predictive D) Descriptive, predictive, then prescriptive E) Predictive, descriptive, then prescriptive Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 162) Data visualization is most commonly used in which category of business analytics? A) Descriptive B) Predictive C) Metric D) Prescriptive E) Mining Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 163) A company uses business analytics to focus on the best course of action within specific circumstances. This would fit within which category of business analytics? A) Descriptive B) Predictive C) Metric D) Prescriptive E) Mining Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics.

164) Predictive analytics focus on how past patterns might occur in the future. These analyses often rely on patterns that are the future. A) repeated into B) decreasing in C) increasing in D) steady in E) extrapolated into Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 165) The category of business analytics that is often used to optimize the performance of a system in the business would be . A) descriptive B) prescriptive C) metric D) predictive E) mining Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 166) A new CEO of has just completed a first data mining project. The analysis did not provide much useful information because not all departments reported and because of an overall smaller than expected sample size. To which characteristics of big data do these issues refer? A) Variety and volume B) Veracity and descriptive analysis C) Value and veracity D) Predictive and visualization E) Tableau software analysis and input Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics.

167) To address the challenges of analyzing big data, several popular statistical software packages are . A) NeXtgen, Hyfi and Domac B) Minitab, Excel and Tableau C) Inright, Outgoing and Hyper D) Logright, Aslate and Numbers E) Statforce, Statcom and NASA Answer: B Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 168) What new field of business was established to help business decision makers meet the challenges, opportunities and potentialities presented by big data? A) Volume analytics B) Spectrum analytics C) Ethical analytics D) Business analytics E) Mining analytics Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 169) The large growth in the numbers and types of data available to researchers, data scientists, and business decision makers is most commonly referred to as . A) business mining B) plethora business C) complex datasets D) big data E) operations research Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

170) In order to help decision makers, business analytics uses a variety of techniques in order to big data. A) add value to B) infer statistics from C) graph information from D) identify the data in E) mine Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 171) Data are typically by removing corrupt or incorrect records and identifying incomplete, incorrect, or irrelevant parts of data. A) extracted B) simulated C) stratified D) classified E) cleaned Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 172) To improve usability and searchability, data are usually sorted into . A) columns and rows B) graphics and plots C) bar graphs and charts D) visualization and ranges E) dots and lines Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 173) The final stage in the process of business analytics is . A) visualization B) simulated C) prescriptive D) graph driven E) statistical Answer: C Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics.

174) Statistics can generally be divided into the two branches of and . A) variety; velocity B) nominal; ordinal C) SAS; SPSS D) descriptive; inferential E) volume; veracity Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 175) Companies collect terabytes of new data every day that are then added to its peta-bytes of historical data in order to . A) have more data than competitors B) see if it is possible C) improve their security systems D) fill data sets E) help managers make better decisions Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 176) Data extraction is the first process within . A) data visualization B) data inferential analysis C) frequency distribution D) data mining E) networking veracity Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization. 177) Which is considered the simplest and most commonly used of the categories of business analytics? A) Descriptive analytics B) Predictive analytics C) Time series analytics D) Theoretical analytics E) Simulation analytics Answer: A Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 51

178) What type of analytics strives to consider the risk of potential future decisions before they are made? A) Network-series analytics B) Communication analytics C) Predictive analytics D) Prescriptive analytics E) Trend analytics Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 179) What type of analytics include classifying techniques, such as decision tree models and neural networks? A) Trend analytics B) Network-series analytics C) Communication analytics D) Predictive analytics E) Prescriptive analytics Answer: D Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.7: Compare and contrast the three categories of business analytics. 180) Data mining utilizes the ETL process. ETL stands for . A) elapsed, triangular, and logarithmic B) extracting, tableau, and logarithms C) excel, traditional, and linear D) exploring, trends, and location plotting E) extract, transform, and load Answer: E Diff: 2 Response: See section 1.3 Introduction to Business Analytics Learning Objective: 1.5: Define important business analytics terms including big data, business analytics, data mining, and data visualization.

Business Statistics, 11e (Black) Chapter 2 Charts and Graphs 1) A summary of data in which raw data are grouped into different ordered intervals and the number of items in each group is noted is called a frequency distribution. Answer: TRUE Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 2) If the individual class frequency is divided by the total frequency, the result is the median frequency. Answer: FALSE Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 3) A cumulative frequency distribution provides a running total of the frequencies through the classes of a frequency distribution. Answer: TRUE Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 4) The difference between the highest number and the lowest number in a set of data is called the differential frequency. Answer: FALSE Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 5) For any given data set, a frequency distribution with a larger number of classes will always be better than the one with a smaller number of classes. Answer: FALSE Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 6) One rule that must always be followed in constructing frequency distributions is that adjacent classes must overlap. Answer: FALSE Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

7) A cumulative frequency polygon is also called an ogive. Answer: TRUE Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. 8) A histogram can be described as a type of vertical bar chart. Answer: TRUE Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. 9) One advantage of a stem and leaf plot over a frequency distribution is that the values of the original data are retained. Answer: TRUE Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. 10) For a company in a gardening supplies business, the best graphical presentation of the percentage of the total budget spent in each different expense category is the stem and leaf plot. Answer: FALSE Diff: 3 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. 11) In a histogram, the tallest bar(s) represents the class or classes with the highest cumulative frequency. Answer: FALSE Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

12) Dot Plots are mainly used to display a large data set. Answer: FALSE Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. 13) A graphical representation of a frequency distribution is called a pie chart. Answer: FALSE Diff: 1 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed. 14) In contrast to quantitative data graphs that are plotted along a numerical scale, qualitative graphs are plotted using non-numerical categories. Answer: TRUE Diff: 1 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed. 15) A Pareto chart and a pie chart are both types of qualitative graphs. Answer: TRUE Diff: 1 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed. 16) A scatter plot shows how the numbers in a data set are scattered around their average. Answer: FALSE Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data. 17) A scatter plot is a two-dimensional graph plot of data containing pairs of observations on two numerical variables. Answer: TRUE Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

18) A scatter plot is useful for examining the relationship between two numerical variables. Answer: TRUE Diff: 1 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data. 19) A cross tabulation is a graph that separately displays the frequency counts for two variables. Answer: FALSE Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data. 20) A scatter plot indicates that two variables are unrelated as their data are scattered. Answer: FALSE Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data. 21) When looking at a scatter plot, if a trend can be discerned between changes in one variable that appear to be related to changes in the other variable, there is likely a relationship between the two variables. Answer: TRUE Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data. 22) To consider historical data as part of their decisions, management often uses time-series data. Answer: TRUE Diff: 1 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data. 23) The point of "cleaning" time-series data is to be sure all the data are accurate. Answer: FALSE Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

24) Time-series data should be shown from oldest time period to the most recent. Answer: TRUE Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data. 25) To compare two series of data during the same time period, the graph should show the first and then show the second after that, all in one line. Answer: FALSE Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data. 26) To show differences between different series during the same time periods, different trend lines, each in a different color, and all using the same x axis for graphing. Answer: TRUE Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data. 27) Visualization of time-series data is considered descriptive business analytics. Answer: TRUE Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

28) An instructor made a frequency table of the scores his students got on a test Score 30-under 40 40-under 50 50-under 60 60-under 70 70-under 80 80-under 90 90-under 100

Frequency 1 4 5 10 20 10 5

The midpoint of the last class interval is _. A) 90 B) 5 C) 95 D) 100 E) 50 Answer: C Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 29) An instructor made a frequency table of the scores his students got on a test Score 30-under 40 40-under 50 50-under 60 60-under 70 70-under 80 80-under 90 90-under 100

Frequency 1 4 5 10 20 10 5

Approximately what percent of students got more than 70? A) 36 B) 20 C) 50 D) 10 E) 64 Answer: E Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

30) Consider the following frequency distribution: Class Interval 10-under 20 20-under 30 30-under 40

Frequency 15 25 10

What is the midpoint of the first class? A) 10 B) 20 C) 15 D) 30 E) 40 Answer: C Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 31) Consider the following frequency distribution: Class Interval 10-under 20 20-under 30 30-under 40

Frequency 15 25 10

What is the relative frequency of the first class? A) 0.15 B) 0.30 C) 0.10 D) 0.20 E) 0.40 Answer: B Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

32) Consider the following frequency distribution: Class Interval 10-under 20 20-under 30 30-under 40

Frequency 15 25 10

What is the cumulative frequency of the second class interval? A) 25 B) 40 C) 15 D) 50 Answer: B Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 33) The number of phone calls arriving at a switchboard each hour has been recorded, and the following frequency distribution has been developed. Class Interval 20-under 40 40-under 60 60-under 80 80-under 100

Frequency 30 45 80 45

What is the midpoint of the last class? A) 80 B) 100 C) 95 D) 90 E) 85 Answer: D Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

34) The number of phone calls arriving at a switchboard each hour has been recorded, and the following frequency distribution has been developed. Class Interval 20-under 40 40-under 60 60-under 80 80-under 100

Frequency 30 45 80 45

What is the relative frequency of the second class? A) 0.455 B) 0.900 C) 0.225 D) 0.750 E) 0.725 Answer: C Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 35) The number of phone calls arriving at a switchboard each hour has been recorded, and the following frequency distribution has been developed. Class Interval 20-under 40 40-under 60 60-under 80 80-under 100

Frequency 30 45 80 45

What is the cumulative frequency of the third class? A) 80 B) 0.40 C) 155 D) 75 E) 105 Answer: C Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

36) A person has decided to construct a frequency distribution for a set of data containing 60 numbers. The lowest number is 23 and the highest number is 68. If 5 classes are used, the class width should be approximately . A) 4 B) 12 C) 8 D) 5 E) 9 Answer: E Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 37) A person has decided to construct a frequency distribution for a set of data containing 60 numbers. The lowest number is 23 and the highest number is 68. If 7 classes are used, the class width should be approximately . A) 5 B) 7 C) 9 D) 11 E) 12 Answer: B Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 38) A frequency distribution was developed. The lower endpoint of the first class is 9.30, and the midpoint is 9.35. What is the upper endpoint of this class? A) 9.50 B) 9.60 C) 9.70 D) 9.40 E) 9.80 Answer: D Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

39) The cumulative frequency for a class is 27. The cumulative frequency for the next (nonempty) class will be . A) less than 27 B) equal to 27 C) next class frequency minus 27 D) 27 minus the next class frequency E) 27 plus the next class frequency Answer: E Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 40) The following class intervals for a frequency distribution were developed to provide information regarding the starting salaries for students graduating from a particular school: Salary ($1,000s) 28-under 31 31-under 35 34-under 37 39-under 40

Number of Graduates -

Before data were collected, someone questioned the validity of this arrangement. Which of the following represents a problem with this set of intervals? A) There are too many intervals. B) The class widths are too small. C) Some numbers between 28,000 and 40,000 would fall into two different intervals. D) The first and the second interval overlap. E) There are too few intervals. Answer: C Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

41) The following class intervals for a frequency distribution were developed to provide information regarding the starting salaries for students graduating from a particular school: Salary ($1,000s) 28-under 31 31-under 34 34- under 35 35- under 38

Number of Graduates -

Before data were collected, someone questioned the validity of this arrangement. Which of the following represents a problem with this set of intervals? A) The intervals are too wide. B) More classes are needed as the numbers are in the thousands. C) The first interval should start at 0. D) The second and the third interval overlap. E) The third interval is smaller than the others. Answer: E Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 42) The following class intervals for a frequency distribution were developed to provide information regarding the starting salaries for students graduating from a particular school: Salary ($1,000s) 28-under 31 31-under 35 34-under 37 39-under 40

Number of Graduates -

Before data were collected, someone questioned the validity of this arrangement. Which of the following represents a problem with this set of intervals? A) There are too many intervals. B) The class widths are too small. C) The class widths are too large. D) The second and the third interval overlap. E) There are too few intervals. Answer: D Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

43) Abel Alonzo, Director of Human Resources, is exploring employee absenteeism at Harrison Haulers, Inc. during the last operating year. A review of all personnel records indicated that absences ranged from zero to twenty-nine days per employee. The following class intervals were proposed for a frequency distribution of absences. Absences (Days) 0-under 5 5-under 10 10-under 15 20-under 25 25-under 30

Number of Employees -

Which of the following represents a problem with this set of intervals? A) There are too few intervals. B) Some numbers between 0 and 29, inclusively, would not fall into any interval. C) The first and second interval overlaps. D) There are too many intervals. E) The second and the third interval overlap. Answer: B Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 44) Abel Alonzo, Director of Human Resources, is exploring employee absenteeism at Harrison Haulers, Inc.during the last operating year. A review of all personnel records indicated that absences ranged from zero to twenty-nine days per employee. The following class intervals were proposed for a frequency distribution of absences. Absences (Days) 0-under 10 10-under 20 20-under 30

Number of Employees -

Which of the following might represent a problem with this set of intervals? A) There are too few intervals. B) Some numbers between 0 and 29 would not fall into any interval. C) The first and second interval overlaps. D) There are too many intervals. E) The second and the third interval overlap. Answer: A Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

45) Consider the relative frequency distribution given below: Class Interval 20-under 40 40-under 60 60-under 80 80-under 100

Relative Frequency 0.2 0.3 0.4 0.1

There were 60 numbers in the data set. How many numbers were in the interval 20-under 40? A) 12 B) 20 C) 40 D) 10 E) 15 Answer: A Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 46) Consider the relative frequency distribution given below: Class Interval 20-under 40 40-under 60 60-under 80 80-under 100

Relative Frequency 0.2 0.3 0.4 0.1

There were 60 numbers in the data set. How many numbers were in the interval 40-under 60? A) 30 B) 50 C) 18 D) 12 E) 15 Answer: C Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

47) Consider the relative frequency distribution given below: Class Interval 20-under 40 40-under 60 60-under 80 80-under 100

Relative Frequency 0.2 0.3 0.4 0.1

There were 60 numbers in the data set. How many of the number were less than 80? A) 90 B) 80 C) 0.9 D) 54 E) 100 Answer: D Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 48) Consider the following frequency distribution: Class Interval 100-under 200 200-under 300 300-under 400

Frequency 25 45 30

What is the midpoint of the first class? A) 100 B) 150 C) 25 D) 250 E) 200 Answer: B Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

49) Consider the following frequency distribution: Class Interval 100-under 200 200-under 300 300-under 400

Frequency 25 45 30

What is the relative frequency of the second class interval? A) 0.45 B) 0.70 C) 0.30 D) 0.33 E) 0.50 Answer: A Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 50) Consider the following frequency distribution: Class Interval 100-under 200 200-under 300 300-under 400

Frequency 25 45 30

What is the cumulative frequency of the second class interval? A) 25 B) 45 C) 70 D) 100 E) 250 Answer: C Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

51) Consider the following frequency distribution: Class Interval 100-under 200 200-under 300 300-under 400

Frequency 25 45 30

What is the midpoint of the last class interval? A) 15 B) 350 C) 300 D) 200 E) 400 Answer: B Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 52) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system and orders an inspection of "each and every payroll voucher issued since January 1, 2021." Each payroll voucher was inspected and the following frequency distribution was compiled. Errors per Voucher 0-under 2 2-under 4 4-under 6 6-under 8 8-under 10

Number of Vouchers 500 400 300 200 100

The relative frequency of the first class interval is . A) 0.50 B) 0.33 C) 0.40 D) 0.27 E) 0.67 Answer: B Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

53) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system and orders an inspection of "each and every payroll voucher issued since January 1, 2021." Each payroll voucher was inspected and the following frequency distribution was compiled. Errors per Voucher 0-under 2 2-under 4 4-under 6 6-under 8 8-under 10

Number of Vouchers 500 400 300 200 100

The cumulative frequency of the second class interval is . A) 1,500 B) 500 C) 900 D) 1,000 E) 1,200 Answer: C Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. 54) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system and orders an inspection of "each and every payroll voucher issued since January 1, 2021." Each payroll voucher was inspected and the following frequency distribution was compiled. Errors per Voucher 0-under 2 2-under 4 4-under 6 6-under 8 8-under 10

Number of Vouchers 500 400 300 200 100

The midpoint of the first class interval is _. A) 500 B) 2 C) 1.5 D) 1 E) 250 Answer: D Diff: 1 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data.

55) Consider the following stem and leaf plot: Stem 1 2 3 4 5

Leaf 0, 2, 5, 7 2, 3, 4, 4 0, 4, 6, 6, 9 5, 8, 8, 9 2, 7, 8

Suppose that a frequency distribution was developed from this plot, and there were 5 classes (10under 20, 20-under 30, etc.). What would the frequency be for class 30-under 40? A) 3 B) 4 C) 6 D) 7 E) 5 Answer: E Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. 56) Consider the following stem and leaf plot: Stem 1 2 3 4 5

Leaf 0, 2, 5, 7 2, 3, 4, 8 0, 4, 6, 6, 9 5, 8, 8, 9 2, 7, 8

Suppose that a frequency distribution was developed from this plot, and there were 5 classes (10under 20, 20-under 30, etc.). What would be the relative frequency of the class 20-under 30? A) 0.4 B) 0.25 C) 0.20 D) 4 E) 0.50 Answer: C Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

57) Consider the following stem and leaf plot: Stem 1 2 3 4 5

Leaf 0, 2, 5, 7 2, 3, 4, 8 0, 4, 6, 6, 9 5, 8, 8, 9 2, 7, 8

Suppose that a frequency distribution was developed from this plot, and there were 5 classes (10under 20, 20-under 30, etc.). What was the highest number in the data set? A) 50 B) 58 C) 59 D) 78 E) 98 Answer: B Diff: 1 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. 58) Consider the following stem and leaf plot: Stem 1 2 3 4 5

Leaf 0, 2, 5, 7 2, 3, 4, 8 0, 4, 6, 6, 9 5, 8, 8, 9 2, 7, 8

Suppose that a frequency distribution was developed from this plot, and there were 5 classes (10under 20, 20-under 30, etc.). What was the lowest number in the data set? A) 0 B) 10 C) 7 D) 2 E) 1 Answer: B Diff: 1 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

59) Consider the following stem and leaf plot: Stem 1 2 3 4 5

Leaf 0, 2, 5, 7 2, 3, 4, 8 0, 4, 6, 6, 9 5, 8, 8, 9 2, 7, 8

Suppose that a frequency distribution was developed from this, and there were 5 classes (10under 20, 20-under 30, etc.). What is the cumulative frequency for the 30-under 40 class interval? A) 5 B) 9 C) 13 D) 14 E) 18 Answer: C Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. 60) The following represent the ages of students in a class: 19, 23, 21, 19, 19, 20, 22, 31, 21, 20 If a stem and leaf plot were to be developed from this, how many stems would there be? A) 2 B) 3 C) 4 D) 5 E) 10 Answer: B Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

2) Each day, the office staff at Oasis Quick Shop prepares a frequency distribution and an ogive of sales transactions by dollar value of the transactions. Saturday's cumulative frequency ogive follows.

The total number of sales transactions on Saturday was . A) 200 B) 500 C) 300 D) 100 E) 400 Answer: B Diff: 1 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

3) Each day, the office staff at Oasis Quick Shop prepares a frequency distribution and an ogive of sales transactions by dollar value of the transactions. Saturday's cumulative frequency ogive follows.

The percentage of sales transactions on Saturday that were under $100 each was . A) 100 B) 10 C) 80 D) 20 E) 15 Answer: D Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

4) Each day, the office staff at Oasis Quick Shop prepares a frequency distribution and an ogive of sales transactions by dollar value of the transactions. Saturday's cumulative frequency ogive follows.

The percentage of sales transactions on Saturday that were at least $100 each was . A) 100 B) 10 C) 80 D) 20 E) 15 Answer: C Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

5) Each day, the office staff at Oasis Quick Shop prepares a frequency distribution and an ogive of sales transactions by dollar value of the transactions. Saturday's cumulative frequency ogive follows.

The percentage of sales transactions on Saturday that were between $100 and $150 was . A) 20 B) 40 C) 60 D) 80 E) 10 Answer: C Diff: 3 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

6) Each day, the manager at Jamie's Auto Care Shop prepares a frequency distribution and a histogram of sales transactions by dollar value of the transactions. Friday's histogram follows.

On Friday, the approximate number of sales transactions in the 75-under 100 category was . A) 10 B) 100 C) 150 D) 200 E) 60 Answer: E Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

7) Each day, the manager at Jamie's Auto Care Shop prepares a frequency distribution and a histogram of sales transactions by dollar value of the transactions. Friday's histogram follows.

On Friday, the approximate number of sales transactions between $150 and $175 was . A) 75 B) 200 C) 300 D) 400 E) 500 Answer: A Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

8) The staff of Mr. Wayne Wertz, VP of Operations at Portland Peoples Bank, prepared a cumulative frequency ogive of waiting time for walk-in customers.

The total number of walk-in customers included in the study was . A) 100 B) 250 C) 300 D) 450 E) 500 Answer: D Diff: 1 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

9) The staff of Mr. Wayne Wertz, VP of Operations at Portland Peoples Bank, prepared a cumulative frequency ogive of waiting time for walk-in customers.

The percentage of walk-in customers waiting one minute or less was . A) 22 B) 11 C) 67 D) 10 E) 5 Answer: A Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

10) The staff of Mr. Wayne Wertz, VP of Operations at Portland Peoples Bank, prepared a cumulative frequency ogive of waiting time for walk-in customers.

The percentage of walk-in customers waiting more than 6 minutes was . A) 22 B) 11 C) 67 D) 10 E) 75 Answer: B Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

11) The staff of Mr. Wayne Wertz, VP of Operations at Portland Peoples Bank, prepared a cumulative frequency ogive of waiting time for walk-in customers.

The percentage of walk-in customers waiting between 1 and 6 minutes was . A) 22 B) 11 C) 37 D) 10 E) 67 Answer: E Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

3 11

12) The staff of Mr. Wayne Wertz, VP of Operations at Portland Peoples Bank, prepared a frequency histogram of waiting time for drive up ATM customers.

Approximately drive up ATM customers waited less than 2 minutes. A) 20 B) 30 C) 100 D) 180 E) 200 Answer: D Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

3 12

13) The staff of Mr. Wayne Wertz, VP of Operations at Portland Peoples Bank, prepared a frequency histogram of waiting time for drive up ATM customers.

Approximately drive up ATM customers waited at least 7 minutes. A) 20 B) 30 C) 100 D) 180 E) 200 Answer: B Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

3 13

14) The staff of Ms. Tamara Hill, VP of Technical Analysis at Blue Sky Brokerage, prepared a frequency histogram of market capitalization of the 937 corporations listed on the American Stock Exchange in January 2021.

Approximately corporations had capitalization exceeding $200,000,000. A) 50 B) 100 C) 700 D) 800 E) 890 Answer: B Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

3 14

15) The staff of Ms. Tamara Hill, VP of Technical Analysis at Blue Sky Brokerage, prepared a frequency histogram of market capitalization of the 937 corporations listed on the American Stock Exchange in January 2021.

Approximately corporations had capitalizations of $200,000,000 or less. A) 50 B) 100 C) 700 D) 800 E) 900 Answer: D Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed.

3 15

75) An instructor has decided to graphically represent the grades on a test. The instructor uses a plus/minus grading system (i.e. she gives grades of A-, B+, etc.). Which of the following would provide the most information for the students? A) A histogram B) A bar chart C) A cumulative frequency distribution D) A frequency distribution E) A scatter plot Answer: B Diff: 2 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed. 76) The staff of the accounting and the quality control departments rated their respective supervisor's leadership style as either (1) authoritarian or (2) participatory. Sixty-eight percent of the accounting staff rated their supervisor "authoritarian," and thirty-two percent rated him/her "participatory." Forty percent of the quality control staff rated their supervisor "authoritarian," and sixty percent rated him/her "participatory." The best graphic depiction of these data would be two . A) histograms B) frequency polygons C) ogives D) pie charts E) scatter plots Answer: D Diff: 3 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

3 16

17) A recent survey of U.S. automobile owners showed the following preferences for exterior automobile colors:

What type of graph is used to depict exterior automobile color preferences? A) Frequency polygon B) Pareto chart C) Bar graph D) Ogive E) Histogram Answer: C Diff: 1 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

3 17

18) A recent survey of U.S. automobile owners showed the following preferences for exterior automobile colors:

What are the top two color preferences for automobiles? A) White and Black B) White and Red/ Maroon C) White and Blue D) White and Silver/Grey E) Black and Other Answer: A Diff: 1 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

3 18

79) The following is a bar chart of the self-reported race for 189 pregnant women.

Approximately percent of pregnant women are African-American A) 20 B) 14 C) 5 D) 35 E) 50 Answer: B Diff: 2 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

3 19

80) The 2020 and 2021 market share data of three competitors (Alston, Baren, and Clemson) in an oligopolistic industry are presented in the following pie charts.

Which of the following is true? A) Only Baren gained market share. B) Only Clemson lost market share. C) Alston lost market share. D) Baren lost market share. E) All companies lost market share Answer: B Diff: 2 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

81) The 2020 and 2021 market share data of three competitors (Alston, Baren, and Clemson) in an oligopolistic industry are presented in the following pie charts. Total sales for this industry were $1.5 billion in 2020 and $1.8 billion in 2021. Clemson's sales in 2020 were .

A) $330 million B) $630 million C) $675 million D) $828 million E) $928 million Answer: A Diff: 2 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

4 81

82) The 2020 and 2021 market share data of three competitors (Alston, Baren, and Clemson) in an oligopolistic industry are presented in the following pie charts. Total sales for this industry were $1.5 billion in 2020 and $1.8 billion in 2021. Baren's sales in 2020 were .

A) $342 million B) $630 million C) $675 million D) $828 million E) $928 million Answer: C Diff: 2 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

4 82

83) The 2020 and 2021 market share data of three competitors (Alston, Baren, and Clemson) in an oligopolistic industry are presented in the following pie charts.

Which of the following may be a false statement? A) Sales revenues gained at Clemson. B) Only Clemson lost market share. C) Alston gained market share. D) Baren gained market share. E) Both Alston and Baren gained market share Answer: A Diff: 3 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

4 83

84) The following graphic of PCB Failures is a

A) Scatter Plot B) Pareto Chart C) Pie Chart D) Cumulative Histogram Chart E) Line diagram Answer: B Diff: 1 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

4 84

85) According to the following graphic, the most common cause of PCB Failures is a

A) Cracked Trace B) Bent Pin C) Missing Part D) Solder Bridge E) Wrong Part Answer: A Diff: 1 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

4 85

86) According to the following graphic, "Bent Pins" account for

% of PCB Failures.

A) 10 B) 20 C) 30 D) 40 E) 50 Answer: B Diff: 3 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed.

4 86

87) Suppose a market survey of 200 consumers was conducted to determine the likelihood of each consumer purchasing a new computer next year. Data were collected based on the age of the consumer and are shown below: Age Bracket <25 25-34 35-44 45-54 >55 Total Surveyed

Intent to Purchase Computer within 1 year 54 57 49 29 11 200

Using the table above, which of the following statements is true about the surveyed consumers? A) More of the surveyed consumers likely to purchase a computer next year are younger. B) More of the surveyed consumers likely to purchase a computer next year are older. C) The surveyed consumers likely to purchase a computer are evenly distributed among the age brackets. D) The largest group of surveyed consumers likely to purchase a new computer next year are between 25 and 34 years old. E) None of the above statements are true. Answer: D Diff: 1 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

4 87

88) Suppose a market survey of 200 consumers was conducted to determine the likelihood of each consumer purchasing a new computer next year. The data were collected based on the income level of the consumer and are shown below: Income Level <$30K $30K - $59K $60K - $89K $90 - $119K $120K Total Surveyed

Intent to Purchase Computer within 1 year 40 43 38 40 39 200

Using the table above, which of the following statements is true? A) More of the surveyed consumers likely to purchase a computer next year are wealthier. B) The surveyed consumers likely to purchase a computer are distributed across all income levels. C) Among surveyed consumer likely to purchase a computer in the next year, the smallest group was among the most wealthy. D) Less of the surveyed consumers likely to purchase a new computer next year have income more than $120K. E) None of the above statements are true. Answer: B Diff: 1 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

4 88

89) The following graphic of residential housing data (selling price and size in square feet) is a .

A) scatter plot B) Pareto chart C) pie chart D) cumulative histogram E) cumulative frequency distribution Answer: A Diff: 1 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

4 89

90) The following graphic of residential housing data (selling price and size in square feet) indicates .

A) an inverse relation between the two variables B) no relation between the two variables C) a direct relation between the two variables D) a negative exponential relation between the two variables E) a sinusoidal relationship between the two variables Answer: C Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

91) The following graphic of cigarettes smoked (sold) per capita (CIG) and deaths per 100K population from lung cancer (LUNG) indicates .

A) a weak negative relationship between the two variables B) a somewhat positive relationship between the two variables C) when the number of cigarettes smoked (sold) per capita (CIG) increases the deaths per 100K population from lung cancer (LUNG) decreases D) a negative relationship between the two variables E) no relationship between the two variables Answer: B Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

2) The United Nations Development Programme website provides comparative data by country on key metrics, such as life expectancy over time. The chart below shows data on life expectancy over time in the United States.

Which of the following statements are not true based on the scatterplot of U.S. Life Expectancy over time? A) The life expectancy in the U.S. is increasing over time. B) U.S. citizens had a shorter life expectancy in 2010 than they did in in 2008. C) The scatterplot shows an increasing trend in life expectancy in the U.S. D) Based on the scatterplot, one can assume the life expectancy in 2014 will be higher than 78 years. E) All of these statements are true. Answer: B Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

3) The United Nations Development Programme website provides comparative data by country on key metrics. Two such metrics are life expectancy and expenditures on health as a percent of GDP. The table below shows data on life expectancy and health expenditures in the United States. Year 2000 2005 2006 2007 2008 2009 2010

U.S. Life Expectancy 76.8 77.6 77.7 77.9 78.1 78.2 78.4

Expenditure on Health (%GDP) 5.8 6.7 7.1 7.2 7.6 8.4 9.5

Which of the following scatterplots best depicts the relationship between life expectancy and expenditures on health as a percent of GDP? A)

Answer: C Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data. 94) A retail shoe company would like to consider key elements that might impact the sales related a specific store's location in the town. If placed on a scatter plot, which two variables would be helpful in helping management with this information? A) Sales and weather B) Sales and square footage C) Sales and nearest grocery store D) Sales and average age of customers E) Sales and nearest shopping mall Answer: E Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

95) If both variables being analyzed are nominal data, the best method to reveal any potential connections between them would be with a . A) bar chart B) scatter plot C) cross tabulation D) two pie charts E) line graphs Answer: C Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data. 96) Two other names for cross tabulations are and . A) frequencies; contingency tables B) contingency tables; pivot tables C) scatter tables; pivot tables D) cross plots; frequencies E) Pareto charts; cross plots Answer: B Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data.

97) Scott Brim, Chief Financial Officer of Space Mall, Inc., wants to better understand the busiest business hours during the weekend. There are door sensors that approximate the number of people who enter the mall. The table below presents the average number of people coming in during the weekend, for the last month each hour: Hour 9-under 10 10-under 11 11-under 12 12-under 1 1-under 2 2-under 3 3-under 4 4-under 5 5-under 6 6-under 7 7-under 8 8-under 9

Number of People 350 400 300 650 550 400 350 450 250 300 200 300

The relative frequency of the fourth class interval is . A) 0.07 B) 0.08 C) 0.14 D) 0.15 E) 0.38 Answer: C Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. Bloom's level: Application 98) In a frequency distribution, the first class interval begins at 18. The midpoint of the first class interval is 19.5, and the last class interval ends at 51. How many class intervals are there? A) 11 B) 17 C) 22 D) 33 E) 34 Answer: A Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. Bloom's level: Application

99) In a frequency distribution, the first class interval begins at 18. The midpoint of the first class interval is 19.5, and the midpoint of the last class interval is 49.5. How many class intervals are there? A) 11 B) 17 C) 22 D) 33 E) 34 Answer: A Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. Bloom's level: Application 100) The class mark is the , and it is . A) total number of class intervals in a frequency distribution; usually between 5 and 15 B) range of the observed values; the difference between the max and min values C) width of the class intervals; approximately equal to the range divided by the number of classes D) midpoint of each class interval; geometric mean of the class interval endpoints E) midpoint of each class interval; arithmetic mean of the class interval endpoints Answer: E Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. Bloom's level: Knowledge 101) Your company is doing market research to assess the feasibility of a new product. The market research team gathers pricing information of all the existing products that would compete with your company's product. The most expensive brand is priced at $22.95, and the least expensive one at $20.59. If a class width of 0.25 is used, then the class mark of the first class interval will be . A) 20.50 B) 20.59 C) 20.72 D) 21.75 E) 23.09 Answer: C Diff: 3 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. Bloom's level: Application

102) Your company is doing market research to assess the feasibility of a new product. The market research team gathers pricing information of all the existing products that would compete with your company's product. The most expensive brand is priced at $22.95, and the least expensive one at $20.59. If a class width of 0.25 is used, then the number of classes will be . A) 9 B) 9.4 C) undetermined, so you can choose either 9 or 10 D) undetermined, so you must choose another class width E) 10 Answer: E Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. Bloom's level: Knowledge 103) Your company is doing market research to assess the feasibility of a new product. The market research team gathers pricing information of the 60 existing products in the market that would compete with your company's product. The most expensive brand is priced at $22.95, and the least expensive one at $20.59. If the relative frequency of the first class is 0.05 and the cumulative frequency for the second class is 10, then the relative frequency for the second class is . A) 0.05 B) 0.11 C) 0.12 D) 0.17 E) 1.67 Answer: C Diff: 3 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. Bloom's level: Application 104) Given two class intervals and their respective frequencies and relative frequencies, the ratio of the frequencies the ratio of the relative frequencies. A) is less than B) is the same as C) is larger than D) could be less, equal, or larger than E) less than or equal to Answer: B Diff: 2 Response: See section 2.1 Frequency Distributions Learning Objective: 2.1: Construct a frequency distribution from a set of data. Bloom's level: Knowledge

105) The customer help center in your company receives calls from customers who need help with some of the customized software solutions your company provides. The staff prepare the following cumulative frequency ogive for waiting times during the last three months. What percentage of customers had waiting times exceeding 3 minutes?

A) 55.5% B) 80.0% C) 44.4% D) 30.0% E) 33.3% Answer: C Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. Bloom's level: Application

106) The staff of Ms. Tamara Hill, VP of Technical Analysis at Blue Sky Brokerage, prepared a frequency histogram of market capitalization of the 937 corporations listed on the American Stock Exchange in January 2016.

Approximately % of corporations had capitalization not exceeding $200,000,000. A) 15 B) 20 C) 75 D) 80 E) 85 Answer: E Diff: 2 Response: See section 2.2 Quantitative Data Graphs Learning Objective: 2.2: Construct different types of quantitative data graphs, including histograms, frequency polygons, ogives, dot plots, and stem-and-leaf plots, in order to interpret the data being graphed. Bloom's level: Application

107) The following pie chart shows the market share of the only three brands in a market at the end of last year:

If by the end of the current year, brand C increases its market share to 51%, brand B maintains its market share, and total market sales increase by 15%, then this year's sales for brand A will be . A) 67 B) 65 C) 55 D) 53 E) 50 Answer: D Diff: 3 Response: See section 2.3 Qualitative Data Graphs Learning Objective: 2.3: Construct different types of qualitative data graphs, including pie charts, bar graphs, and Pareto charts, in order to interpret the data being graphed. Bloom's level: Application

108) The scatterplot is missing from this problem. Consider a scatterplot showing the relationship between years of formal education and life expectancy. Which of the following statements is false? A) If more years of formal education are correlated with higher life expectancy, then the scatterplot would exhibit a positive slope. B) If more years of formal education are not correlated with higher life expectancy, then the scatterplot would exhibit a flat slope. C) If more years of formal education are not correlated with higher life expectancy, then the scatterplot would exhibit a flat or negative slope. D) If more years of formal education are negatively correlated with higher life expectancy, then the scatterplot would exhibit a negative slope. E) If other research shows a causal effect between years of formal education and higher life expectancy (additional years of formal education cause a higher life expectancy), then the scatterplot could not be flat. Answer: C Diff: 2 Response: See section 2.4 Charts and Graphs for Two Variables Learning Objective: 2.4: Construct a cross-tabulation table and recognize basic trends in twovariable scatter plots of numerical data. Bloom's level: Application 109) The following time-series data shows the average number of vacation days taken each year by employees. 2010 2011 2012 2013 2014 2015 2016 2017 2018

22 25 26 29 24 21 22 20 19

Which of the following would be indicated if these data were shown through a visualization of these data? A) The average increased with each year shown. B) The average increased and then generally decreased during these years. C) The average decreased with each year shown. D) The average was highest in 2012 then declined since then. E) The average was lowest in 2015 and increased since then. Answer: B Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data. 63

110) The following time-series data shows the average number of vacation days taken each year by employees. 2010 2011 2012 2013 2014 2015 2016 2017 2018

22 25 26 29 24 21 22 20 19

The most effective visualization of these data would be . A) a pie chart going chronologically clockwise B) a bar chart with most recent year to the left C) a line chart with most recent year to the right D) a bar chart with the highest average to the left E) a line chart with the highest average to the right Answer: C Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

111) A shirt production company has tracked their production since the company started in 2004. The graph of their annual production is shown.

During these years, production has generally . A) increased in these years B) declined in most of the years shown C) increased in every year since 2004 D) decreased in most of those years E) increased and declined without a discernable trend Answer: A Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

112) A shirt production company has tracked their production since the company started in 1999. The graph of their annual production is shown.

After 2010, in what year did production recover and surpass the production level of 2010? A) 2011 B) 2014 C) 2015 D) 2017 E) 2018 Answer: D Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data. 113) To show hourly sales throughout a day, a chart would be most effective, and a chart would be more effective at showing what products were sold during that day. A) pie; line B) line; line C) pie; bar D) line; pie E) bar; pie Answer: D Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

114) A shirt production company has tracked their production and expenses since the company started in 2004. The graph of both is shown.

When comparing production and expenses during these years, what conclusion is not true? A) Both series are generally decreasing. B) Both series are generally increasing. C) The time from 2010 to 2013 indicates a downward trend for both D) The time from 2013 to 2023 indicates an upward trend for both E) Expenses and production appear to follow similar trends Answer: A Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

8) A shirt production company has tracked their sales of red and blue shirts over the past few years. The graph of both is shown below.

Which of the following is a true statement about the trends in sales? A) Sales of red shirts are less than those of blue shirts in all the years before 2017. B) Sales of blue shirts are trending upward from 2019 to 2022. C) Sales of blue shirts are always higher than those of red shirts. D) Red shirt sales show an increasing trend. E) Sales of red shirts are always higher than those of blue shirts. Answer: B Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

9) A shirt production company has tracked their sales of red and blue shirts over the past few years. The graph of both is shown below.

In what years were red shirt sales higher than blue shirt sales? A) 2014 through 2017 B) 2015, 2016, and 2020 C) 2016, 2017, and 2018 D) 2018, 2020, 2021, 2022, and 2023 E) 2015, 2016, and 2017 Answer: E Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

10) A shirt production company has tracked their sales of red and blue shirts over the past few years. The graph of both is shown below.

What trends can be identified from this graph? A) Sales of red shirts have consistently increased throughout this time. B) Sales of blue shirts are currently declining. C) Between 2015 and 2023, sales of blue shirts consistently increased. D) Cannot determine trends as data not in chronological order. E) The smallest annual increase in red shirt sales was between the years of 2015 and 2019. Answer: D Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

11) A shirt production company has tracked their sales of red and blue shirts over the past few years. The graph of both is shown below.

Realizing that a more clear chart would match the colors correctly, using this chart, which of the following is not a true statement? A) Red shirt sales have been higher than blue shirt sales in the most recent years. B) Blue shirt sales were higher from 2015 through 2017. C) Blue shirt sales have been higher than red shirt sales in the most recent years. D) Red shirt sales declined from 2014 to 2016. E) Cannot make conclusions from this chart. Answer: C Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

7 11

12) Sales are tracked during the past year in the graph below.

Management is pleased to show the growth in sales at the end of the year. Why would this be an incorrect conclusion? A) The highest growth was in the month of June. B) Cannot compare sales for different time periods. C) The graph is not showing growth rates. D) A bar graph would be more effective in determining that conclusion. E) A second year of data would be needed to make that conclusion. Answer: C Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

7 12

13) Sales are tracked during the past year in the graph below.

What would be the most effective strategy to allow management to more clearly discern monthly trends in sales? A) Remove the total value from being included in the graph. B) Have the axis on the left show more detailed grid lines between 0 and 100. C) Show the trend line in a more vivid color. D) Add labels to each of the graphed data points. E) Add minor grid lines throughout the graph making values more clear. Answer: A Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

7 13

14) Monthly sales were tracked and shown on the graph below.

Which of the following would be an incorrect conclusion based on this graph? A) Sales were lowest in the month of March. B) Sales increased between August and September. C) The last month had higher sales than the first month. D) Sales declined from June through August. E) The last month had higher sales than September. Answer: E Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

7 14

15) Monthly sales were tracked and shown on the graph below.

Based on this graph, which month had the highest sales? A) April B) July C) February D) November E) December Answer: D Diff: 2 Response: See section 2.5 Visualizing Time-Series Data Learning Objective: 2.5: Construct a time-series graph and be able to visually identify any trends in the data.

7 15

Business Statistics, 11e (Black) Chapter 3 Descriptive Statistics 1) Statistical measures used to yield information about the center or the middle parts of a group of numbers are called the measures of central tendency. Answer: TRUE Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 2) The most appropriate measure of central tendency for nominal-level data is the median. Answer: FALSE Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 3) The most frequently occurring value in a set of data is called the mode. Answer: TRUE Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 4) An appropriate measure of central tendency for ordinal data is the mode. Answer: TRUE Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 5) It is inappropriate to use the mean to analyze data that are not at least interval level in measurement. Answer: TRUE Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 6) The lowest appropriate level of measurement for the median is ordinal. Answer: TRUE Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 1

7) The middle value in an ordered array of numbers is called the mode. Answer: FALSE Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 8) Average deviation is a common measure of the variability of data containing a set of numbers. Answer: FALSE Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 9) The sum of deviations about the arithmetic mean for a given set of data is always equal to zero. Answer: TRUE Diff: 1 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 10) The average of the squared deviations about the arithmetic mean is called the variance. Answer: TRUE Diff: 1 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 11) The sample standard deviation is calculated by taking the square root of the population standard deviation. Answer: FALSE Diff: 1 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

12) The coefficient of variation is unitless. Answer: TRUE Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 13) Skewness of a data set is a measure of the shape of the distribution. Answer: TRUE Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 14) Skewness is when a distribution is asymmetrical or lacks symmetry. Answer: TRUE Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 15) If the mean, median, and mode are equal, then the distribution is positively skewed. Answer: FALSE Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 16) If the mean of a distribution is greater than the median, then the distribution is positively skewed. Answer: TRUE Diff: 2 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 17) If the median of a distribution is greater than mean, then the distribution is skewed to the left. Answer: TRUE Diff: 2 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots.

18) A box and whisker plot is determined from the mean, the smallest and the largest values, and the lower and upper quartile. Answer: FALSE Diff: 2 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 19) An outlier of a data set is determined from the lower and upper quartile. Answer: TRUE Diff: 2 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 20) A histogram can be used in business analytics to determine if a variable is approximately normally distributed. Answer: TRUE Diff: 1 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 21) A business analyst could use descriptive statistics of skewness to determine if the empirical rule could appropriately be applied to a variable. Answer: TRUE Diff: 1 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 22) By comparing the mean and median of a variable in a large data set, a business analyst can assess the variability within the values of that variable. Answer: FALSE Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions.

23) From a large data set, a variable would be considered positively skewed if the descriptive statistics showed the median to be less than the mean. Answer: TRUE Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 24) If a large data set can be assumed to be normally distributed, then a business analyst could apply the normality rule to determine a range with approximately 60% of the data. Answer: FALSE Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 25) In a large data set, an analyst finds that the interquartile range is 102 to 331, indicating that about 75% of the data points fall within that range. Answer: FALSE Diff: 1 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 26) A statistics student made the following grades on 7 tests: 76, 82, 92, 95, 79, 86, and 92. What is the mean grade? A) 78 B) 80 C) 86 D) 84 E) 88 Answer: C Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data.

27) A statistics student made the following grades on 7 tests: 76, 82, 92, 95, 79, 86, and 92. What is the median grade? A) 86 B) 76 C) 82 D) 94 E) 95 Answer: A Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 28) A statistics student made the following grades on 7 tests: 76, 82, 92, 95, 79, 86, and 92. What is the mode? A) 79 B) 82 C) 86 D) 92 E) 76 Answer: D Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 29) A commuter travels many miles to work each morning. She has timed this trip 5 times during the last month. The time (in minutes) required to make this trip was 34, 39, 41, 35, and 41. The mean time (in minutes) required for this trip was . A) 35 B) 41 C) 37.5 D) 38 E) 35.5 Answer: D Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data.

30) A commuter travels many miles to work each morning. She has timed this trip 5 times during the last month. The time (in minutes) required to make this trip was 34, 39, 41, 35, and 41. The median time (in minutes) required for this trip was . A) 39 B) 41 C) 37.5 D) 38 E) 35.5 Answer: A Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 31) A commuter travels many miles to work each morning. She has timed this trip 5 times during the last month. The time (in minutes) required to make this trip was 34, 39, 41, 35, and 41. The mode time required for this trip was . A) 39 B) 41 C) 37.5 D) 38 E) 35 Answer: B Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. 32) A sample was taken of the salaries of four employees from a large company. The following are their salaries (in thousands of dollars) for this year: 33, 36, 41, and 47. The median of their salaries is approximately . A) 38.5 B) 34.5 C) 34 D) 44.5 E) 38 Answer: A Diff: 1 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data.

33) A sample was taken of the salaries of four employees from a large company. The following are their salaries (in thousands of dollars) for this year: 33, 36, 41, and 47. The variance of their salaries is approximately . A) 28.19 B) 75.59 C) 37.58 D) 6.13 E) 5.31 Answer: C Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 34) The number of standard deviations that a value (x) is above or below the mean is the . A) absolute deviation B) coefficient of variation C) interquartile range D) z score E) correlation coefficient Answer: D Diff: 1 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 35) The empirical rule says that approximately what percentage of the values would be within 2 standard deviations of the mean in a bell shaped set of data? A) 95% B) 68% C) 50% D) 97.7% E) 100% Answer: A Diff: 1 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

36) The empirical rule says that approximately what percentage of the values would be within 1 standard deviation of the mean in a bell shaped set of data? A) 95% B) 68% C) 50% D) 97.7% E) 100% Answer: B Diff: 1 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 37) According to Chebyshev's Theorem, approximately how many values in a large data set will be within 2 standard deviations of the mean? A) At least 75% B) At least 68% C) At least 95% D) At least 89% E) At least 99% Answer: A Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 38) According to Chebyshev's theorem how many values in a data set will be within 3 standard deviations of the mean? A) At least 75% B) At least 68% C) At least 95% D) At least 89% E) At least 99% Answer: D Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

39) A commuter travels many miles to work each morning. She has timed this trip 5 times during the last month. The time (in minutes) required to make this trip was 38, 33, 36, 47, and 41. What is the variance for these sample data? A) 28.5 B) 11 C) 22.8 D) 5.34 E) 4.77 Answer: A Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 40) A commuter travels many miles to work each morning. She has timed this trip 5 times during the last month. The time (in minutes) required to make this trip was 38, 33, 36, 47, and 41. What is the standard deviation for these sample data? A) 28.5 B) 11 C) 22.8 D) 5.34 E) 4.77 Answer: D Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 41) A commuter travels many miles to work each morning. She has timed this trip 5 times during the last month. The time (in minutes) required to make this trip was 44, 39, 41, 35, and 41. What is the standard deviation for these sample data? A) 0 B) 11 C) 3.32 D) 2.97 E) 10.69 Answer: C Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

42) The mean life of a particular brand of light bulb is 1200 hours and the standard deviation is 50 hours. We can conclude that at least 75% of this brand of bulbs will last between . A) 1100 and 1300 hours B) 1150 and 1250 hours C) 1050 and 1350 hours D) 1000 and 1400 hours E) 950 and 1450 hours Answer: A Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 43) The mean life of a particular brand of light bulb is 1200 hours and the standard deviation is 50 hours. It can be concluded that at least 89% of this brand of bulbs will last between . A) 1100 and 1300 hours B) 1150 and 1250 hours C) 1050 and 1350 hours D) 1000 and 1400 hours E) 950 and 1450 hours Answer: C Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 44) The mean life of a particular brand of light bulb is 1200 hours and the standard deviation is 75 hours. Tests show that the life of the bulb is approximately normally distributed. It can be concluded that approximately 68% of the bulbs will last between . A) 900 and 1100 hours B) 950 and 1050 hours C) 975 and 1475 hours D) 1050 and 1350 hours E) 1125 and 1275 hours Answer: E Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

45) Jessica Salas, President of Salas Products, is reviewing the warranty policy for her company's new model of automobile batteries. Life tests performed on a sample of 100 batteries indicated: (1) an average life of 75 months, (2) a standard deviation of 5 months, and (3) a bell-shaped battery life distribution. Approximately 68% of the batteries will last between . A) 70 and 80 months B) 60 and 90 months C) 65 and 85 months D) 55 and 95 months E) 60 and 100 months Answer: A Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 46) Jessica Salas, President of Salas Products, is reviewing the warranty policy for her company's new model of automobile batteries. Life tests performed on a sample of 100 batteries indicated: (1) an average life of 75 months, (2) a standard deviation of 5 months, and (3) a bell-shaped battery life distribution. Approximately 95% of the batteries will last between . A) 70 and 80 months B) 60 and 90 months C) 65 and 85 months D) 55 and 95 months E) 60 and 100 months Answer: C Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

47) Jessica Salas, President of Salas Products, is reviewing the warranty policy for her company's new model of automobile batteries. Life tests performed on a sample of 100 batteries indicated: (1) an average life of 75 months, (2) a standard deviation of 5 months, and (3) a bell shaped battery life distribution. Approximately 99.7% of the batteries will last between . A) 70 and 80 months B) 60 and 90 months C) 65 and 85 months D) 55 and 95 months E) 50 and 100 months Answer: B Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 48) Jessica Salas, President of Salas Products, is reviewing the warranty policy for her company's new model of automobile batteries. Life tests performed on a sample of 100 batteries indicated: (1) an average life of 75 months, (2) a standard deviation of 5 months, and (3) a bell-shaped battery life distribution. What percentage of the batteries will fail within the first 65 months of use? A) 0.5% B) 1% C) 2.5% D) 5% E) 7.5% Answer: C Diff: 3 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

49) The average starting salary for graduates at a university is $33,000 with a standard deviation of $2,000. If a histogram of the data shows that it takes on a mound shape, the empirical rule says that approximately 95% of the graduates would have a starting salary between . A) 29,000 and 37,000 B) 27,000 and 39,000 C) 25,000 and 41,000 D) 31,000 and 35,000 E) 21,000 and 39,000 Answer: A Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 50) The average starting salary for graduates at a university is $33,000 with a standard deviation of $2,000. If a histogram of the data shows that it takes on a mound shape, the empirical rule says that approximately 68% of the graduates would have a starting salary between . A) 29,000 and 37,000 B) 27,000 and 39,000 C) 25,000 and 41,000 D) 31,000 and 35,000 E) 21,000 and 39,000 Answer: D Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

51) Liz Chapa manages a portfolio of 250 common stocks. Her staff compiled the following performance statistics for two new stocks.

Stock Salas Products, Inc. Hot Boards, Inc.

Mean 15% 20%

Rate of Return Standard Deviation 5% 5%

The coefficient of variation for Salas Products, Inc. is . A) 300% B) 100% C) 33% D) 5% E) 23% Answer: C Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 52) Liz Chapa manages a portfolio of 250 common stocks. Her staff compiled the following performance statistics for two new stocks.

Stock Salas Products, Inc. Hot Boards, Inc.

Mean 15% 20%

Rate of Return Standard Deviation 5% 5%

The coefficient of variation for Hot Boards, Inc. is . A) 400% B) 100% C) 33% D) 40% E) 25% Answer: E Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data.

53) If stock A has a coefficient of variation of 30% and stock B has a coefficient of variation of 35%. Based on this measure of risk, which stock would be considered riskier? A) Stock A B) The values are so close, they would be considered to have the same level of risk C) Stock B D) Risk cannot be measured by the coefficient of variation E) There is not enough information to answer Answer: C Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. 54) The following box and whisker plot was constructed for the age of accounts receivable.

The box and whisker plot reveals that the accounts receivable ages are . A) skewed to the left B) skewed to the right C) not skewed D) normally distributed E) symmetrical Answer: B Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots.

55) The following box and whisker plot was constructed for the age of accounts receivable.

The box and whisker plot reveals that the accounts receivable ages are . A) skewed to the left B) skewed to the right C) not skewed D) normally distributed E) symmetrical Answer: A Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 56) The following frequency distribution was constructed for the wait times in the emergency room.

The frequency distribution reveals that the wait times in the emergency room are A) skewed to the left B) skewed to the right C) not skewed D) normally distributed E) symmetrical Answer: A Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots.

57) The following frequency distribution was constructed for the wait times to check out at the grocery store.

The frequency distribution reveals that the wait times to check out at the grocery store are . A) skewed to the left B) skewed to the right C) not skewed D) normally distributed E) symmetrical Answer: B Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 58) Shaun Connor, Human Resources Manager for American Oil Terminals (AOT), is reviewing the operator training hours at AOT nationally. His staff compiled the following table of national statistics on operators training hours.

Mean Median Mode Standard Deviation

West Coast Region 32 32 32 8

East Coast Region 38 32 27 7

What can Shaun conclude from these statistics? A) The East Coast distribution is skewed to the left. B) The East Coast distribution is skewed to the right. C) The West Coast distribution is skewed to the left. D) The West Coast distribution is skewed to the right. E) Both distributions are symmetrical. Answer: B Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 18

59) David Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. His staff compiled the following table of regional statistics on teller training hours.

Mean Median Mode Standard Deviation

Southeast Region 20 20 20 5

Southwest Region 28 20 21 7

What can David conclude from these statistics? A) The Southeast distribution is symmetrical. B) The Southwest distribution is skewed to the left. C) The Southeast distribution has the greater dispersion. D) The Southeast distribution is skewed to the left. E) The two distributions are symmetrical. Answer: A Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots.

60) Karen Merlott, VP for Strategic Planning at a recruitment firm, recently conducted a survey to determine customer satisfaction with job placement. She distributed the survey to 45 of the most recently placed executives. Two items on the survey asked them to rate the importance of "initial interview process" and "satisfaction of final job placement" on a scale of 1 to 10 (with 1 meaning "not important" and 10 meaning "highly important"). Her staff assembled the following statistics on these two items.

Mean Median Mode Standard Deviation

Initial Interview Process 8.5 9 9.0 1.0

Satisfaction of Final Job Placement 7.5 8.5 9 1.5

What can Karen conclude from these statistics? A) The Initial Interview Process distribution is positively skewed. B) The Initial Interview Process distribution is not skewed. C) The Satisfaction of Final Job Placement distribution is negatively skewed. D) The Satisfaction of Final Job Placement distribution is positively skewed. E) Both are symmetrically distributed. Answer: C Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 61) In its Industry Norms and Key Business Ratios, Dun & Bradstreet reported that Q1, Q2, and Q3 for 2,037 gasoline service stations' sales to inventory ratios were 20.8, 33.4, and 53.8, respectively. From this we can conclude that . A) 68% of these service stations had sales to inventory ratios of 20.8 or less B) 50% of these service stations had sales to inventory ratios of 33.4 or less C) 50% of these service stations had sales to inventory ratios of 53.8 or more D) 95% of these service stations had sales to inventory ratios of 33.4 or more E) 99% of these service stations had sales to inventory ratios of 53.8 or more Answer: B Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots.

62) The data represent the age of faculty members in the Department of Statistics at a state university. 50,64,55,48,61,49,55,58,47,62,46,63,56,52,48 We can conclude that . A) the distribution is skewed to the left B) the distribution is roughly symmetric C) the distribution is skewed to the right D) the shape of the distribution cannot be determined E) there are two outliers Answer: B Diff: 3 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. 63) A sample of 117 records of the selling price of homes from Feb 15 to Apr 30, 2021 was taken from the files maintained by the Albuquerque Board of Realtors. The following are summary statistics for the selling prices. Variable Prices

N 117

Mean 106270

Minimum Q1 54000 77650

Median 96000

Q3 Maximum 121750 215000

From this we can conclude that . A) there are no outliers B) more homes were sold for greater than $121750 than for less than $77650 C) 68% of the selling price of these homes is from $77650 to $121750 D) 25% of the selling price of these homes is at least $121750 E) the distribution of selling price of these homes is negatively skewed Answer: D Diff: 2 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots.

64) A company is reviewing the database of customer purchases over the past 3 years. Using descriptive statistics on this large data set, a business analyst found the following values. Variable Count Mean Purchases 56,472 105.21

Minimum Q1 10.24 31.09

Median 74.88

Q3 Maximum 117.23 201.40

From this we can conclude that . A) 95% of these purchases are between $105.24 and $74.88 B) the distribution of these purchases is negatively skewed C) more of these purchases had values less than $74.88 than above that amount D) the distribution of these purchases is positively skewed E) the distribution of these purchases is not skewed Answer: D Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 65) A company is reviewing the database of customer purchases over the past 3 years. Using descriptive statistics on this large data set, a business analyst found the following values. Variable Count Mean Purchases 56,472 105.21

Minimum Q1 10.24 31.09

Median 74.88

Q3 Maximum 117.23 201.40

66) A company is reviewing the database of customer purchases over the past 3 years. Using descriptive statistics on this large data set, a business analyst found the following values. Variable Count Mean Purchases 56,472 105.21

Minimum Q1 10.24 31.09

Median 74.88

Q3 Maximum 117.23 201.40

From this we can conclude that . A) 68% of these purchases are between $105.21 and $74.88 B) the distribution of these purchases is negatively skewed C) 50% of these purchases are between $105.21 and $117.23 D) 50% of these purchases are less than $105.21 E) more of these purchases were below the mean than above the mean Answer: E Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 67) After obtaining a large data set, a business analyst will first want to determine . A) descriptive statistics of all variables B) the standard deviation of key variables C) only the mean of all variables D) what other variables should be included in the data set E) when the updated version of the data set will be available Answer: A Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 68) A business analyst is considering a data set reflecting the number of products purchased at one time, there is a minimum of 45 and a maximum of 61. Given that the data set the number of products purchased for 5,791 purchases, the number of values at the mode would be expected to be . A) less than 45 B) a large number since there is little variation in the number of products purchased C) a small number since there is little variation in the number of products purchased D) about 61 E) greater than 5,791 Answer: B Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 23

69) A business analyst is considering a data set reflecting the number of products purchased at one time, there is a minimum of 45 and a maximum of 61. Given that the data set the number of products purchased for 5,791 purchases, the range would be . A) less than 45 B) 53 C) 5791 D) greater than 61 E) 16 Answer: E Diff: 1 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 70) A business analyst is considering a data set reflecting the number of products purchased at one time, there is a mean of 159 and a standard deviation of 43.2. Given that the data set the number of products purchased for 5,791 purchases and is mound shaped, the analyst would expect 68% of the data points to be . A) between 115.8 and 202.2 B) less than 5,791 C) between 72.6 and 245.4 D) greater than 61 E) between 43.2 and 159 Answer: A Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 71) A statistics student has a mean score of 92 in the first 3 tests. Suppose there are a total of 4 tests and all of them have equal weight. You can also assume that there is no extra credit in the last test. What is the best possible final average this student can get? A) 96 B) 95 C) 94 D) 93 E) There is not enough information to find out. Answer: C Diff: 2 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. Bloom's level: Application 24

72) A statistics student made the following grades on the first 6 tests: 76, 82, 92, 95, 79, 86. The total number of tests for the semester is 7. What could be her median score for the whole semester? A) It's not possible to answer without knowing the score on the last test. B) 76 C) 82 D) 92 E) 95 Answer: C Diff: 3 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. Bloom's level: Application 73) A statistics student made the following grades on the first 6 tests: 76, 82, 92, 95, 92, 86. The total number of tests for the semester is 7. If the median score for the whole semester was 92, what could not have been the score of the last test? A) 77 B) 92 C) 93 D) 94 E) 96 Answer: A Diff: 3 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. Bloom's level: Application 74) Your company is testing a new auto engine in a race car. The race car is on a 2-mile track. If the first lap was exactly at 2 minutes, so that the average speed for the first lap was 1.00 mile per minute, what would be the time required for the second lap so that the overall average speed will be 4 miles per minute? A) 0.286 minutes B) 1.021 minute C) 0.812 minutes D) 0.643 minutes E) 0.4 minutes Answer: A Diff: 3 Response: See section 3.1 Measures of Central Tendency Learning Objective: 3.1: Apply various measures of central tendency–including the mean, median, and mode–to a set of data. Bloom's level: Application

75) The mean life of a particular brand of light bulb is 1200 hours. If you know that at about 95% of this brand of bulbs will last between 1100 and 1300 hours, then what is the standard deviation of the light bulbs' life? A) 25 B) 50 C) 75 D) 100 E) 200 Answer: B Diff: 2 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. Bloom's level: Application 76) The mean life of a specialized essential electronic component in one of your company's main products is 80 months, and its standard deviation is 6 months. It is known that the life for this component is not normally distributed. We can conclude that at least 96% of these components will last between . A) 74 and 86 months B) 68 and 92 months C) 66 and 94 months D) 64 and 96 months E) 50 and 110 months Answer: E Diff: 3 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. Bloom's level: Application

77) An electrical component for one of the main products your company produces is specified to have an electrical impedance of 2.15 ohms. However, the manufacturing process is not perfect and there is some variation on the actual impedance of these components. A recent statistical study indicated that in fact, the impedances are normally distributed with a mean impedance of 2.15 ohms and a standard deviation of 0.05 ohms. When the impedance exceeds 2.25 ohms, the product malfunctions so that component must be rejected and replaced by one whose impedance is within the acceptable limit. If you need 1350 usable components, how many components should you order? A) 1608 B) 1422 C) 1421 D) 1417 E) 1384 Answer: E Diff: 3 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. Bloom's level: Application 78) You have computed the z score for a value. Then you realize that the actual standard deviation of the population is 25% larger than the one you used for computing the z value. Your corrected z value will be . A) 25% smaller than the original value B) 25% larger than the original value C) 75% smaller than the original value D) 20% larger than the original value E) 20% smaller than the original value Answer: E Diff: 3 Response: See section 3.2 Measures of Variability Learning Objective: 3.2: Apply various measures of variability–including the range, interquartile range, mean absolute deviation, variance, and standard deviation (using the empirical rule and Chebyshev's theorem)–to a set of data. Bloom's level: Application

79) A box-and-whisker plot for last year's data is compared with a box-and-whisker plot for this year's data. The biggest change is that the median is moved from the left side of the box to the right side of the box. The other elements of the plot remained fairly constant. Based on this change, it can be concluded that . A) the data have become more positively skewed B) the data are less skewed than in the previous year C) the skew has not changed between the two years D) the data have become more negatively skewed E) the data are more skewed than in the previous year Answer: D Diff: 2 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. Bloom's level: Application 80) If a data set is negatively skewed, which calculations cannot be used? A) Business analytics B) Chebyshev's theorem C) Descriptive statistics D) Measures of variance E) Empirical rule Answer: E Diff: 1 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots. Bloom's level: Application 81) The data represent the GPA of a senior class in Grandview High School. 3.5, 2.0, 4.0, 3.5, 4.0, 2.5, 3.5, 3.0, 2.0, 4.0, 4.0, 3.0, 2.5, 3.5, 4.0, 3.0 We can conclude that . A) the distribution is skewed to the left B) the distribution is roughly symmetric C) the distribution is skewed to the right D) the shape of the distribution cannot be determined E) there is an outlier Answer: A Diff: 3 Response: See section 3.3 Measures of Shape Learning Objective: 3.3: Describe a data distribution statistically and graphically using skewness, and box-and-whisker plots.

82) A business analyst compares 2017 daily sales to 2018 daily sales using descriptive statistics for each. In 2017, the standard deviation of daily sales was 73.87, while in 2018 the standard deviation of daily sales was 136.32. The analyst could conclude that . A) in 2018 there was more variation in daily sales B) the average daily sales increased between those two years C) in 2018 there was less variation in daily sales D) the variance remained the same between the two years E) the average daily sales decreased between those two years Answer: A Diff: 1 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. Bloom's level: Application 83) Considering sales levels at each hour of operation in a shoe store, a business analyst finds that the mode is between 3pm and 4pm each day. The analyst could conclude that . A) half of shoe sales occur before 3pm B) hourly sales of shoes are normally distributed C) half of shoe sales occur after 4pm D) 3pm to 4pm has more shoe sales than other hours E) 3pm to 4pm is when any retail business would have the most sales Answer: D Diff: 1 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. Bloom's level: Application 84) Considering sales levels at each hour of operation in a shoe store over the past year, a business analyst looks at a histogram that has the selling hours of the day along the horizontal axis and the frequency of sales along the vertical axis. The analyst notices that the distribution is left skewed. From this, the analyst could conclude that . A) most sales are made later in the day B) sales are made evenly throughout the day C) the store should close earlier in the day due to lack of sales at that time D) most sales are made earlier in the day E) most sales are made in the middle of the day Answer: E Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 29

85) The hourly production of a plastic cup company is tracked over several months and is found to be normally distributed. Given a mean of 15,630 cups and a standard deviation of 251 cups, about what percent of all hours of production should produce between 15,379 and 15,881 cups? A) 50% B) 68% C) 75% D) 95% E) nearly 100% Answer: B Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 86) The hourly production of a plastic cup company is tracked over several months and is found to be normally distributed. Given a mean of 15,630 cups and a standard deviation of 251 cups, about what percent of all hours of production should produce between 15,128 and 16,132 cups? A) 50% B) 68% C) 75% D) 95% E) nearly 100% Answer: D Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions. 87) The hourly production of a plastic cup company is tracked over the most recent several months and is found to be normally distributed. Comparing the mean of this data set to a similar data set collected last year during the same months, the business analyst notices that the mean has increased, while the overall shape of the distribution has not. Which of the following would not be a possible explanation for this difference? A) the overall distribution has shifted to the right B) one or two large outliers occurred in the more recent data set C) the mode increased as well D) one or two small outliers occurred in the earlier data set E) the overall distribution has shifted to the left Answer: E Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions.

88) The hourly production of a plastic cup company is tracked over several months and is found to be normally distributed. A business analyst creates a box and whisker plot from those data and finds that the box starts at 15259 to 16,001. Based on the graph, what percentage of the hourly sales would the analyst expect to find within that range? A) 50% B) 68% C) 75% D) 95% E) nearly 100% Answer: A Diff: 2 Response: See section 3.4 Business Analytics Using Descriptive Statistics Learning Objective: 3.4: Use descriptive statistics as a business analytics tool to better understand meanings and relationships in data so as to aid businesspeople in making better decisions.

Business Statistics, 11e (Black) Chapter 4 Probability 1) Inferring the value of a population parameter from the statistic on a random sample drawn from the population is an inferential process under uncertainty. Answer: TRUE Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. 2) Probability is used to develop knowledge of the fundamental mathematical tools for quantitatively assessing risk. Answer: TRUE Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. 3) The method of assigning probabilities to uncertain outcomes based on laws and rules is called the relative frequency method. Answer: FALSE Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. 4) Assigning probabilities by dividing the number of ways that an event can occur by the total number of possible outcomes in an experiment is called the classical method. Answer: TRUE Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. 5) Assigning probabilities to uncertain events based on one's beliefs or intuitions is called classical method. Answer: FALSE Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities.

6) An experiment is a process that produces outcomes. Answer: TRUE Diff: 1 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 7) An event that cannot be broken down into other events is called a certainty outcome. Answer: FALSE Diff: 1 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 8) The list of all elementary events for an experiment is called the sample space. Answer: TRUE Diff: 1 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 9) If the occurrence of one event does not affect the occurrence of another event, then the two events are mutually exclusive. Answer: FALSE Diff: 1 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 10) If the occurrence of one event precludes the occurrence of another event, then the two events are mutually exclusive. Answer: TRUE Diff: 2 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities.

11) If two events are mutually exclusive, then the two events are also independent. Answer: FALSE Diff: 2 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 12) The probability of A 𝖴 B where A is receiving a state grant and B is receiving a federal grant is the probability of receiving no more than one of the two grants. Answer: FALSE Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 13) If two events are mutually exclusive, then their joint probability is always zero. Answer: TRUE Diff: 2 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 14) The probability that a person's favorite color is blue would be an example of a marginal probability. Answer: TRUE Diff: 2 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 15) A joint probability is the probability that at least one of two events occurs. Answer: FALSE Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 16) In the conditional probability of P(E1|E2) is when E2 has occurred and then the probability of E1 occurring is determined. Answer: TRUE Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one.

17) Given two events, A and B, if the probability of either A or B occurring is 0.6, then the probability of neither A nor B occurring is -0.6. Answer: FALSE Diff: 1 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 18) Given two events, A and B, if the probability of A is 0.7, the probability of B is 0.3, and the joint probability of A and B is 0.21, then the two events are independent. Answer: TRUE Diff: 1 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 19) The law of multiplication gives the probability that at least one of the two events will occur. Answer: FALSE Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 20) If the probability that someone likes the color blue is 44% and the probability that among those individuals, the probability that they wake up early is 52%, then the probability that individuals who like the color blue and wake up early is about 23%. Answer: TRUE Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 21) If the probability that someone likes the color blue is 44% and the probability that individuals wake up early is 64%, then the probability that individuals who like the color blue and wake up early is about 23%. In this case, the two events are independent. Answer: FALSE Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 22) If P(X|Y) = P(X) then the events are X and Y are independent. Answer: TRUE Diff: 1 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 4

23) Given that two events, A and B, are independent, if the marginal probability of A is 0.6, the conditional probability of A given B will be 0.4. Answer: FALSE Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 24) Given two events A and B each with a non-zero probability, if the conditional probability of A given B is zero, it implies that the events A and B are independent. Answer: FALSE Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 25) Given two events A and B each with a non-zero probability, if the conditional probability of A given B is zero, it implies that the events A and B are mutually exclusive. Answer: TRUE Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 26) Bayes' rule is a rule to assign probabilities under the relative frequency method. Answer: FALSE Diff: 1 Response: See section 4.7 Revision of Probabilities: Bayes' Rule Learning Objective: 4.7: Calculate conditional probabilities using Bayes' rule. 27) Bayes' rule is an extension of the law of conditional probabilities to allow revision of original probabilities with new information. Answer: TRUE Diff: 1 Response: See section 4.7 Revision of Probabilities: Bayes' Rule Learning Objective: 4.7: Calculate conditional probabilities using Bayes' rule. 28) Which of the following statements is not true regarding probabilities . A) probability is the basis for inferential statistics B) probabilities are subjective measures with limited value in business. C) probabilities are used to determine the likelihood of certain outcomes D) probabilities can inform many business decisions. Answer: B Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities.

29) Belinda Bose is reviewing a newly proposed advertising campaign. Based on her 15-years' experience, she believes the campaign has a 75% chance of significantly increasing brand name recognition of the product. This is an example of assigning probabilities using the method. A) subjective probability B) relative frequency C) classical probability D) a priori probability E) a posterior probability Answer: A Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. 30) Which of the following is not a legitimate probability value? A) 0.87 B) 12/13 C) 0.05 D) 5/4 E) 0.93 Answer: D Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. 31) Which of the following is a legitimate probability value? A) 1.67 B) 16/15 C) -0.23 D) 3/2 E) 0.28 Answer: E Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities.

32) Assigning probability 1/52 on drawing the ace of spade in a deck of cards is an example of assigning probabilities using the method A) subjective probability B) relative frequency C) classical probability D) a priori probability E) a posterior probability Answer: C Diff: 2 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. 33) The list of all elementary events for an experiment is called . A) the sample space B) the exhaustive list C) the population space D) the event union E) a frame Answer: A Diff: 1 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 34) In a set of 25 aluminum castings, four castings are defective (D), and the remaining twentyone are good (G). A quality control inspector randomly selects three of the twenty-five castings without replacement, to test. The sample space for selecting the group to test contains elementary events. A) 12,650 B) 2,300 C) 455 D) 16 E) 15,625 Answer: B Diff: 2 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities.

35) In a set of 12 aluminum castings, two castings are defective (D), and the remaining ten are good (G). A quality control inspector randomly selects three of the twelve castings with replacement to test. The sample space for selecting the group to test contains elementary events. A) 8 B) 220 C) 120 D) 10 E) 66 Answer: A Diff: 2 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 36) If X and Y are mutually exclusive events, then if X occurs . A) Y must also occur B) Y cannot occur C) X and Y are independent D) X and Y are complements E) X and Y are collectively exhaustive Answer: B Diff: 1 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 37) Consider the following sample space, S, and several events defined on it. S = {Albert, Betty, Abel, Jack, Patty, Meagan}, and the events are: F = {Betty, Patty, Meagan}, H = {Abel, Meagan}, and P = {Betty, Abel}. F ∩ H is . A) {Meagan} B) {Betty, Patty, Abel, Meagan} C) empty, since F and H are complements D) empty, since F and H are independent E) empty, since F and H are mutually exclusive Answer: A Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one.

38) Consider the following sample space, S, and several events defined on it. S = {Albert, Betty, Abel, Jack, Patty, Meagan}, and the events are: F = {Betty, Patty, Meagan}, H = {Abel, Meagan}, and P = {Betty, Abel}. F 𝖴 H is . A) {Meagan} B) {Betty, Abel, Patty, Meagan} C) empty, since F and H are complements D) empty, since F and H are independent E) empty, since F and H are mutually exclusive Answer: B Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 39) Consider the following sample space, S, and several events defined on it. S = {Albert, Betty, Abel, Jack, Patty, Meagan}, and the events are: F = {Betty, Patty, Meagan}, H = {Abel, Meagan}, and P = {Betty, Abel}. The complement of F is . A) {Albert, Betty, Jack, Patty} B) {Betty, Patty, Meagan} C) {Albert, Abel, Jack} D) {Betty, Abel} E) {Meagan} Answer: C Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 40) If E and F are mutually exclusive, then . A) the probability of the union is zero B) P(E) = 1 - P(F) C) the probability of the intersection is zero D) the probability of the union is one E) P(E) = P(F) Answer: C Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one.

41) Meagan Dubean manages a portfolio of 200 common stocks. Her staff classified the portfolio stocks by "industry sector" and "investment objective." Industry Sector Investment Objective Growth Income Total

Electronics 100 20 120

Airlines 10 20 30

Healthcare 40 10 50

Total 150 50 200

Which of the following statements is not true? A) Growth and Income are complementary events. B) Electronics and Growth are dependent. C) Electronics and Healthcare are mutually exclusive. D) Airlines and Healthcare are collectively exhaustive. E) Growth and Income are collectively exhaustive. Answer: E Diff: 3 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 42) Meagan Dubean manages a portfolio of 200 common stocks. Her staff classified the portfolio stocks by "industry sector" and "investment objective." Industry Sector Investment Objective Growth Income Total

Electronics 84 36 120

Airlines 21 9 30

Healthcare 35 15 50

Total 140 60 200

Which of the following statements is true? A) Growth and Healthcare are complementary events. B) Electronics and Growth are independent. C) Electronics and Growth are mutually exclusive. D) Airlines and Healthcare are collectively exhaustive. E) Electronics and Healthcare are collectively exhaustive. Answer: B Diff: 3 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities.

43) The number of different committees of 2 students that can be chosen from a group of 5 students is . A) 20 B) 2 C) 5 D) 10 E) 1 Answer: D Diff: 2 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. 44) Let F be the event that a student is enrolled in a finance course, and let S be the event that a student is enrolled in a statistics course. It is known that 40% of all students are enrolled in afinance course and 35% of all students are enrolled in statistics. Included in these numbers are 15% who are enrolled in both statistics and finance. Find P(S). A) 0.15 B) 0.35 C) 0.40 D) 0.55 E) 0.60 Answer: B Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 45) Let F be the event that a student is enrolled in a finance course, and let S be the event that a student is enrolled in a statistics course. It is known that 40% of all students are enrolled in a finance course and 35% of all students are enrolled in statistics. Included in these numbers are 15% who are enrolled in both statistics and finance. Find the probability that among all students, a student is in finance and is also in statistics. A) 0.15 B) 0.70 C) 0.55 D) 0.12 E) 0.60 Answer: A Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one.

46) Which of the following statements in not true? A) The marginal probability uses the total possible outcomes in the denominator. B) The union probability is the probability of X or Y occurring. C) The joint probability uses the probability of X in the denominator. D) The conditional probability uses subtotal of the possible outcomes in denominator. Answer: C Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 47) One event is that individuals like lasagna and the other event is that individuals like soda, the union of these two events would be the probability of . A) both events occurring B) at least one event occurring C) neither event occurring D) 0% E) 100% Answer: B Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 48) The probability that given one event has occurred that another event would occur would be an example of probability. A) marginal B) union C) joint D) conditional E) non-union Answer: D Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one.

49) The probability of at least one of two events occurring would be an example of a probability. A) marginal B) union C) joint D) conditional E) non-union Answer: B Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 50) If the CEO of Apple wanted to know the probability that if someone owned an Apple computer, they would also own a different brand computer, this would be an example of a probability. A) conditional B) marginal C) joint D) non-joint E) union Answer: A Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 51) If the CEO of Apple wanted to know the probability that someone would own an Apple computer and spend more than 20 hours each week on the internet would be an example of a probability. A) unconditional B) union C) joint D) marginal E) conditional Answer: C Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one.

52) The CEO of Apple wanted to know the probability that someone would own an Apple computer or spend more than 20 hours each week on the internet, this would be an example of a probability. A) union B) unconditional C) marginal D) conditional E) joint Answer: A Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. 53) Let F be the event that a student is enrolled in a finance course, and let S be the event that a student is enrolled in a statistics course. It is known that 40% of all students are enrolled in a finance course and 35% of all students are enrolled in statistics. Included in these numbers are 15% who are enrolled in both statistics and finance. A student is randomly selected, what is the probability that the student is enrolled in either finance or statistics or both? A) 0.15 B) 0.75 C) 0.60 D) 0.55 E) 0.90 Answer: C Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 54) Abel Alonzo, Director of Human Resources, is exploring employee absenteeism at the Plano Power Plant. Ten percent of all plant employees work in the finishing department; 20% of all plant employees are absent excessively; and 7% of all plant employees work in the finishing department and are absent excessively. A plant employee is selected randomly; F is the event "works in the finishing department;" and A is the event "is absent excessively." P(A 𝖴 F) = . A) 0.07 B) 0.10 C) 0.20 D) 0.23 E) 0.37 Answer: D Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 14

55) Max Sandlin researched the characteristics of stock market investors. He found that sixty percent of all investors have a net worth exceeding $1,000,000; 20% of all investors use an online brokerage; and 10% of all investors a have net worth exceeding $1,000,000 and use an online brokerage. An investor is selected randomly, and E is the event "net worth exceeds $1,000,000" and O is the event "uses an online brokerage." P(O 𝖴 E) = . A) 0.17 B) 0.50 C) 0.80 D) 0.70 E) 0.10 Answer: D Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 56) Given P(A) = 0.40, P(B) = 0.50, P(A ∩ B) = 0.15. Find P(A 𝖴 B). A) 0.90 B) 1.05 C) 0.75 D) 0.65 E) 0.60 Answer: C Diff: 1 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary.

57) An automobile dealer wishes to investigate the relation between the gender of the buyer and type of vehicle purchased. Based on the following joint probability table that was developed from the dealer's records for the previous year, P (Male) = . Buyer Gender Type of Vehicle SUV Not SUV Total

Female

Male

.32 .40

.48

Total

1.00

A) 0.48 B) 0.50 C) 0.20 D) 0.02 E) 0.60 Answer: E Diff: 1 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 58) An automobile dealer wishes to investigate the relation between the gender of the buyer and type of vehicle purchased. Based on the following joint probability table that was developed from the dealer's records for the previous year, P (Female) = . Buyer Gender Type of Vehicle SUV Not SUV Total

Female

Male

Total

.30

.40 .60

1.00

A) 0.30 B) 0.40 C) 0.12 D) 0.10 E) 0.60 Answer: B Diff: 1 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary.

59) An automobile dealer wishes to investigate the relation between the gender of the buyer and type of vehicle purchased. Based on the following joint probability table that was developed from the dealer's records for the previous year, P (SUV) = . Buyer Gender Type of Vehicle SUV Not SUV Total

Female

Male

Total

.30

.40 .60

1.00

A) 0.30 B) 0.40 C) 0.12 D) 0.10 E) 0.60 Answer: A Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 60) Meagan Dubean manages a portfolio of 200 common stocks. Her staff classified the portfolio stocks by "industry sector" and "investment objective." Industry Sector Investment Objective Growth Income Total

Electronics 100 20 120

Airlines 10 20 30

Healthcare 40 10 50

Total 150 50 200

If a stock is selected randomly from Meagan's portfolio, P (Growth) = . A) 0.50 B) 0.83 C) 0.67 D) 0.75 E) 0.90 Answer: D Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary.

61) Meagan Dubean manages a portfolio of 200 common stocks. Her staff classified the portfolio stocks by "industry sector" and "investment objective." Industry Sector Investment Objective Growth Income Total

Electronics 100 20 120

Airlines 10 20 30

Healthcare 40 10 50

Total 150 50 200

If a stock is selected randomly from Meagan's portfolio, P (Healthcare 𝖴 Electronics) = . A) 0.25 B) 0.85 C) 0.60 D) 0.75 E) 0.90 Answer: B Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 62) The table below provides summary information about the students in a class. The sex of each individual and their age is given.

Under 20 yrs old Between 20 and 25 yrs old. Older than 25 yrs. Total

Male 10

Female 8

Total 18

12 26 48

18 26 52

30 52 100

If a student is randomly selected from this group, what is the probability that the student is male? A) 0.12 B) 0.48 C) 0.50 D) 0.52 E) 0.68 Answer: B Diff: 1 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary.

63) The table below provides summary information about the students in a class. The sex of each individual and their age is given.

Under 20 yrs old Between 20 and 25 yrs old. Older than 25 yrs. Total

Male 10

Female 8

Total 18

12 26 48

18 26 52

30 52 100

If a student is randomly selected from this group, what is the probability that the student is a female who is also under 20 years old? A) 0.08 B) 0.18 C) 0.52 D) 0.26 E) 0.78 Answer: A Diff: 1 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 64) A market research firm is investigating the appeal of three package designs. The table below gives information obtained through a sample of 200 consumers. The three package designs are labeled A, B, and C. The consumers are classified according to age and package design preference.

Under 25 years 25 or older Total

A 22 54 76

B 34 28 62

C 40 22 62

Total 96 104 200

If one of these consumers is randomly selected, what is the probability that the person prefers design A? A) 0.76 B) 0.38 C) 0.33 D) 0.22 E) 0.39 Answer: B Diff: 1 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary.

65) A market research firm is investigating the appeal of three package designs. The table below gives information obtained through a sample of 200 consumers. The three package designs are labeled A, B, and C. The consumers are classified according to age and package design preference.

Under 25 years 25 or older Total

A 22 54 76

B 34 28 62

C 40 22 62

Total 96 104 200

If one of these consumers is randomly selected, what is the probability that the person prefers design A and is under 25? A) 0.22 B) 0.11 C) 0.18 D) 0.54 E) 0.78 Answer: B Diff: 1 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. 66) It is known that 20% of all students in some large universities are overweight, 20% exercise regularly and 2% are overweight and exercise regularly. What is the probability that a randomly selected student is either overweight or exercises regularly or both? A) 0.40 B) 0.38 C) 0.20 D) 0.42 E) 0.10 Answer: B Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary.

67) An automobile dealer wishes to investigate the relation between the gender of the buyer and type of vehicle purchased. Based on the joint probability table below that was developed from the dealer's records for the previous year, P (Female ∩ SUV) = . Buyer Gender Type of Vehicle SUV Not SUV Total

Female

Male

Total

.30

.40 .60

1.00

A) 0.30 B) 0.40 C) 0.12 D) 0.10 E) 0.60 Answer: D Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 68) Suppose 5% of the population have a certain disease. A laboratory blood test gives a positive reading for 95% of people who have the disease. What is the probability of testing positive and having the disease? A) 0.0475 B) 0.95 C) 0.05 D) 0.9 E) 0.02 Answer: A Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication.

69) Suppose that 3% of all TVs made by a company in 2018 are defective. If 2 of these TVs are randomly selected what is the probability that both are defective? A) 0.0009 B) 0.0025 C) 0.0900 D) 0.0475 E) 0.19 Answer: A Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 70) A recent survey found that 24% of people in Michigan like oatmeal. If the probability that someone lives in Michigan is 4.8%, what is the probability that someone lives in Michigan and likes oatmeal? A) 28.4% B) 24.0% C) 4.8% D) 19.2% E) 1.2% Answer: E Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 71) A recent survey found that 24% of people in Michigan like oatmeal. If the probability that someone lives in Michigan is 4.8%. What is the probability that you choose two people in the US and they are both from Michigan? A) 28.4% B) 24.0% C) 0.2% D) 100.0% E) 48.0% Answer: C Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication.

72) A recent survey found that 24% of people in Michigan like oatmeal. If the probability that someone lives in Michigan is 4.8%. What is the probability that two people from Michigan would both like oatmeal? A) 5.8% B) 24.0% C) 4.8% D) 48.0% E) 1.2% Answer: A Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 73) Given the following joint probability table, find the probability that a dog is small if we know that it takes less than 30-minute walk? Walk Time Type of Doc < 30 min ≥ 30 min Small .29 .08 Large .22 .41 Total .51 .49

Total .36 .63 1.00

A) 36% B) 51% C) 29% D) 57% E) 81% Answer: D Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication.

74) Given the following joint probability table, find the probability that a dog is small and takes less than 30-minute walks?

Type of Doc Small Large Total

< 30 min .29 .22 .51

Walk Time ≥ 30 min .08 .41 .49

Total .36 .63 1.00

A) 63% B) 22% C) 29% D) 51% E) 32% Answer: C Diff: 1 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. 75) Let F be the event that a student is enrolled in a finance course, and let S be the event that a student is enrolled in a statistics course. It is known that 40% of all students are enrolled in a finance course and 35% of all students are enrolled in statistics. Included in these numbers are 15% who are enrolled in both statistics and finance. A student is randomly selected, and it is found that the student is enrolled in finance. What is the probability that this student is also enrolled in statistics? A) 0.15 B) 0.75 C) 0.375 D) 0.50 E) 0.80 Answer: C Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule.

76) Abel Alonzo, Director of Human Resources, is exploring employee absenteeism at the Plano Power Plant. Ten percent of all plant employees work in the finishing department; 20% of all plant employees are absent excessively; and 7% of all plant employees work in the finishing department and are absent excessively. A plant employee is selected randomly; F is the event "works in the finishing department;" and A is the event "is absent excessively." P(A|F) = . A) 0.37 B) 0.70 C) 0.13 D) 0.35 E) 0.80 Answer: B Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 77) Abel Alonzo, Director of Human Resources, is exploring employee absenteeism at the Plano Power Plant. Ten percent of all plant employees work in the finishing department; 20% of all plant employees are absent excessively; and 7% of all plant employees work in the finishing department and are absent excessively. A plant employee is selected randomly; F is the event "works in the finishing department;" and A is the event "is absent excessively." P(F|A) = . A) 0.35 B) 0.70 C) 0.13 D) 0.37 E) 0.10 Answer: A Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 78) Max Sandlin is exploring the characteristics of stock market investors. He found that sixty percent of all investors have a net worth exceeding $1,000,000; 20% of all investors use an online brokerage; and 10% of all investors a have net worth exceeding $1,000,000 and use an online brokerage. An investor is selected randomly, and E is the event "net worth exceeds $1, 000, 000," and O is the event "uses an online brokerage." P(O|E) = . A) 0.17 B) 0.50 C) 0.80 D) 0.70 E) 0.88 Answer: A Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 25

79) Given P (A) = 0.45, P (B) = 0.30, P (A ∩ B) = 0.05. Find P (A|B). A) 0.45 B) 0.135 C) 0.30 D) 0.111 E) 0.167 Answer: E Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 80) Given P (A) = 0.45, P (B) = 0.30, P (A ∩ B) = 0.05. Find P (B|A). A) 0.45 B) 0.135 C) 0.30 D) 0.111 E) 0.167 Answer: D Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 81) Given P (A) = 0.45, P (B) = 0.30, P (A ∩ B) = 0.05. Which of the following is true? A) A and B are independent B) A and B are mutually exclusive C) A and B are collectively exhaustive D) A and B are not independent E) A and B are complimentary Answer: D Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule.

82) Meagan Dubean manages a portfolio of 200 common stocks. Her staff classified the portfolio stocks by "industry sector" and "investment objective." Industry Sector Investment Objective Growth Income Total

Electronics 100 20 120

Airlines 10 20 30

Healthcare 40 10 50

Total 150 50 200

If a stock is selected randomly from Meagan's portfolio, P (Airlines|Income) = A) 0.10 B) 0.40 C) 0.25 D) 0.67 E) 0.90 Answer: B Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule.

83) The table below provides summary information about the students in a class. The sex of each individual and their age is given.

Under 20 yrs old Between 20 and 25 yrs old. Older than 25 yrs. Total

Male 10

Female 8

Total 18

12 26 48

18 26 52

30 52 100

A student is randomly selected from this group, and it is found that the student is older than 25 years. What is the probability that the student is a male? A) 0.21 B) 0.10 C) 0.50 D) 0.54 E) 0.26 Answer: C Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule.

84) A market research firm is investigating the appeal of three package designs. The table below gives information obtained through a sample of 200 consumers. The three package designs are labeled A, B, and C. The consumers are classified according to age and package design preference.

Under 25 years 25 or older Total

A 22 54 76

B 34 28 62

C 40 22 62

Total 96 104 200

If one of these consumers is randomly selected and is under 25, what is the probability that the person prefers design A? A) 0.22 B) 0.23 C) 0.29 D) 0.18 E) 0.78 Answer: B Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 85) A market research firm is investigating the appeal of three package designs. The table below gives information obtained through a sample of 200 consumers. The three package designs are labeled A, B, and C. The consumers are classified according to age and package design preference.

Under 25 years 25 or older Total

A 22 54 76

B 34 28 62

C 40 22 62

Total 96 104 200

If one of these consumers is randomly selected and prefers design B, what is the probability that the person is 25 or older? A) 0.28 B) 0.14 C) 0.45 D) 0.27 E) 0.78 Answer: C Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule.

86) It is known that 20% of all students in some large university are overweight, 20% exercise regularly and 2% are overweight and exercise regularly. What is the probability that a randomly selected student is overweight given that this student exercises regularly? A) 0.40 B) 0.38 C) 0.20 D) 0.42 E) 0.10 Answer: E Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 87) A market research firm is investigating the appeal of three package designs. The table below gives information obtained through a sample of 200 consumers. The three package designs are labeled A, B, and C. The consumers are classified according to age and package design preference.

Under 25 years 25 or older Total

A 22 54 76

B 34 28 62

C 40 22 62

Total 96 104 200

Are "B" and "25 or older" independent and why or why not? A) No, because P (25 or over | B) ≠ P (B) B) Yes, because P (B) = P(C) C) No, because P (25 or older | B) ≠ P (25 or older) D) Yes, because P (25 or older ∩ B) ≠ 0 E) No, because age and package design are different things Answer: C Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 88) An analysis of personal loans at a local bank revealed the following facts: 10% of all personal loans are in default (D), 90% of all personal loans are not in default (D΄), 20% of those in default are homeowners (H | D), and 70% of those not in default are homeowners (H | D΄). If a personal loan is selected at random P (H ∩ D΄) = . A) 0.20 B) 0.63 C) 0.90 D) 0.18 E) 0.78 Answer: B Diff: 3 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 29

89) An analysis of personal loans at a local bank revealed the following facts: 10% of all personal loans are in default (D), 90% of all personal loans are not in default (D΄), 20% of those in default are homeowners (H | D), and 70% of those not in default are homeowners (H | D΄). If a personal loan is selected at random, P (D | H) = . A) 0.03 B) 0.63 C) 0.02 D) 0.18 E) 0.78 Answer: A Diff: 3 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. 90) A market research firms conducts studies regarding the success of new products. The company is not always perfect in predicting the success. Suppose that there is a 50% chance that any new product would be successful (and a 50% chance that it would fail). In the past, for all new products that ultimately were successful, 80% were predicted to be successful (and the other 20% were inaccurately predicted to be failures). Also, for all new products that were ultimately failures, 70% were predicted to be failures (and the other 30% were inaccurately predicted to be successes). What is the a priori probability that a new product would be a success? A) 0.50 B) 0.80 C) 0.70 D) 0.60 E) 0.95 Answer: A Diff: 3 Response: See section 4.7 Revision of Probabilities: Bayes' Rule Learning Objective: 4.7: Calculate conditional probabilities using Bayes' rule.

91) A market research firms conducts studies regarding the success of new products. The company is not always perfect in predicting the success. Suppose that there is a 50% chance that any new product would be successful (and a 50% chance that it would fail). In the past, for all new products that ultimately were successful, 80% were predicted to be successful (and the other 20% were inaccurately predicted to be failures). Also, for all new products that were ultimately failures, 70% were predicted to be failures (and the other 30% were inaccurately predicted to be successes). For any randomly selected new product, what is the probability that the market research firm would predict that it would be a success? A) 0.80 B) 0.50 C) 0.45 D) 0.55 E) 0.95 Answer: D Diff: 3 Response: See section 4.7 Revision of Probabilities: Bayes' Rule Learning Objective: 4.7: Calculate conditional probabilities using Bayes' rule. 92) A market research firm conducts studies regarding the success of new products. The company is not always perfect in predicting the success. Suppose that there is a 50% chance that any new product would be successful (and a 50% chance that it would fail). In the past, for all new products that ultimately were successful, 80% were predicted to be successful (and the other 20% were inaccurately predicted to be failures). Also, for all new products that were ultimately failures, 70% were predicted to be failures (and the other 30% were inaccurately predicted to be successes). If the market research predicted that the product would be a success, what is the probability that it would actually be a success? A) 0.27 B) 0.73 C) 0.80 D) 0.24 E) 1.00 Answer: B Diff: 3 Response: See section 4.7 Revision of Probabilities: Bayes' Rule Learning Objective: 4.7: Calculate conditional probabilities using Bayes' rule.

93) Suppose 5% of the population have a certain disease. A laboratory blood test gives a positive reading for 95% of people who have the disease and 10% positive reading of people who do not have the disease. What is the probability of testing positive? A) 0.0475 B) 0.1425 C) 0.95 D) 0.9 E) 0.3333 Answer: B Diff: 3 Response: See section 4.7 Revision of Probabilities: Bayes' Rule Learning Objective: 4.7: Calculate conditional probabilities using Bayes' rule. 94) Suppose 5% of the population have a certain disease. A laboratory blood test gives a positive reading for 95% of people who have the disease and 10% positive reading of people who do not have the disease. What is the probability that a randomly selected person has the disease given that this person is testing positive? A) 0.0475 B) 0.1425 C) 0.95 D) 0.9 E) 0.3333 Answer: E Diff: 3 Response: See section 4.7 Revision of Probabilities: Bayes' Rule Learning Objective: 4.7: Calculate conditional probabilities using Bayes' rule. 95) Suppose you toss a fair coin three times in a row and obtain tails, tails, and tails. What it the probability that the fourth toss will give heads? A) 0.75 B) 0.67 C) 0.50 D) 0.33 E) 0.25 Answer: C Diff: 1 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. Bloom's level: Application

96) Suppose that your company is sending invitations to its 50 most important clients for an endof-the-year event where a new product will be exhibited. These are printed invitation cards mailed through USPS, and each card is personalized with the name of the corresponding client. You realize that the cards have been accidentally shuffled before placing them in the envelopes. What is the probability that only one card is in the wrong envelope? A) 0.02 B) 0.15 C) 0.10 D) 0.05 E) 0.00 Answer: E Diff: 3 Response: See section 4.1 Introduction to Probability Learning Objective: 4.1: Describe what probability is and how to differentiate among the three methods of assigning probabilities. Bloom's level: Application 97) The department in which you work in your company has 24 employees. A team of 3 employees must be selected to represent the department at a companywide meeting in the headquarters of the company. How many different teams of 3 can be selected? A) 3 B) 8 C) 24 D) 72 E) 2024 Answer: E Diff: 2 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. Bloom's level: Application

98) The department in which you work in your company has 24 employees: 10 women and 14 men. A team of 4 employees must be selected to represent the department at a companywide meeting in the headquarters of the company. The team must have two women and two men. How many different teams of 4 can be selected? A) 12 B) 35 C) 136 D) 2024 E) 4095 Answer: E Diff: 3 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. Bloom's level: Application 99) The department in which you work in your company has 22 employees: 10 women and 12 men. A team of 5 employees must be chose to attend a meeting at the company's headquarters. The team must have 2 women and 3 men. How many different teams of 5 can be selected? A) 7770 B) 4095 C) 5095 D) 9900 E) 9000 Answer: D Diff: 3 Response: See section 4.2 Structure of Probability Learning Objective: 4.2: Deconstruct the elements of probability by defining experiments, sample spaces, and events; classifying events as mutually exclusive, collectively exhaustive, complementary, or independent; and counting possibilities. Bloom's level: Application

100) Your company provides services to three different industries: automotive, construction, and financial services. These services can be either on a yearly or on a monthly contract. The crosstabulation below summarizes this information:

Automotive Construction Financial Total

Monthly 125 212 357 694

Yearly 85 351 210 646

Total 210 563 567 1340

Suppose you know that a given client is a construction company and you are interested in the likelihood that this client is on a yearly contract. This is an example of . A) joint probability B) marginal probability C) conditional probability D) a priori probability E) a posteriori probability Answer: C Diff: 1 Response: See section 4.3 Marginal, Joint, Union, and Conditional Probabilities Learning Objective: 4.3: Compare marginal, union, joint, and conditional probabilities by defining each one. Bloom's level: Application 101) Your company provides services to three different industries: automotive, construction, and financial services. These services can be either on a yearly or on a monthly contract. The crosstabulation below summarizes this information:

Automotive Construction Financial Total

Monthly 125 212 357 694

Yearly 85 351 210 646

Total 210 563 567 1340

If a client is randomly selected, what is the likelihood that it will be from the financial industry or on a monthly contract? A) 26.6% B) 42.3% C) 51.8% D) 65.5% E) 67.5% Answer: E Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. Bloom's level: Application 35

102) Suppose that the probability that LIBOR (London inter-bank offered rate) will increase next trimester is 0.025. Assume also that the probability that your company will open a new branch overseas is 0.20. If the probability that at least one of the two events occur, i.e., LIBOR increases and/or your company opens a new overseas branch, is 0.21, what is the probability that both events occur? A) 0.225 B) 0.015 C) 0.010 D) 0.007 E) 0.005 Answer: B Diff: 2 Response: See section 4.4 Additional Laws Learning Objective: 4.4: Calculate probabilities using the general law of addition, along with a joint probability table, the complement of a union, or the special law of addition if necessary. Bloom's level: Application 103) The division of the company where you work has 85 employees. Thirty of them are bilingual, and 37% of the bilingual employees have a graduate degree. If an employee of this division is randomly selected, what is the probability that the employee is bilingual and has a graduate degree? A) 0.131 B) 0.128 C) 0.126 D) 0.124 E) 0.122 Answer: A Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. Bloom's level: Application

104) A recently published study shows that 50% of Americans adults take multivitamins regularly. Another recent study showed that 20.6% of American adults work out regularly. Suppose that these two variables are independent. The probability that a randomly selected American adult takes multivitamins regularly and works out regularly is . A) 0.706 B) 0.309 C) 0.155 D) 0.106 E) 0.103 Answer: E Diff: 2 Response: See section 4.5 Multiplication Laws Learning Objective: 4.5: Calculate joint probabilities of both independent and dependent events using the general and special laws of multiplication. Bloom's level: Application 105) The marketing department of your company is investigating the appeal of three possible service plans your company is planning to offer. The table below gives information obtained through a sample of 2395 clients. The three service plans are labeled A, B, and C. The clients are classified according to their industry and their service plan preference.

Automotive Construction Financial Serv. Total

A 212 540 205 957

B 340 280 351 971

C 40 322 105 467

Total 592 1142 661 2395

If one of these consumers is randomly selected and prefers plan C, what is the probability that this client is from the construction industry? A) 0.69 B) 0.13 C) 0.66 D) 0.60 E) 0.58 Answer: A Diff: 2 Response: See section 4.6 Conditional Probability Learning Objective: 4.6: Calculate conditional probabilities using Bayes' rule. Bloom's level: Application

106) There are three companies that produce a critical electronic navigation component (ENC) used in the aerospace industry. These companies are Alice Manufacturing, Byte International, and Cognizant Technologies. Alice makes 85% of the ENCs, Byte makes 10%, and Cognizant makes the remaining 5%. The ENCs made by Alice have a 2.5% rate of defects, the ones made by Byte have a 4.0% rate of defects, and the ones made by Cognizant have a 6% rate of defects. If an ENC is randomly selected from the general population of ENCs, the probability that it was made by Alice is . If this ENC is later tested and found to be defective, the probability that it was made by Alice is . A) 0.15; 0.85 B) 0.85; 0.85 C) 0.85; 0.77 D) 0.85; 0.76 E) 0.85; 0.75 Answer: E Diff: 3 Response: See section 4.7 Revision of Probabilities: Bayes' Rule Learning Objective: 4.7: Calculate conditional probabilities using Bayes' rule. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 5 Discrete Distributions 1) Variables which take on values only at certain points over a given interval are called continuous random variables. Answer: FALSE Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 2) A variable that can take on values at any point over a given interval is called a discrete random variable. Answer: FALSE Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 3) The number of visitors to a website each day is an example of a discrete random variable. Answer: TRUE Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 4) The amount of time a patient waits in a doctor's office is an example of a continuous random variable. Answer: TRUE Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 5) The mean or the expected value of a discrete distribution is the long-run average of the occurrences. Answer: TRUE Diff: 1 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution.

6) To compute the variance of a discrete distribution, it is not necessary to know the mean of the distribution. Answer: FALSE Diff: 1 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution. 7) The variance of a discrete distribution increases if we add a positive constant to each one of its values. Answer: FALSE Diff: 2 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution. 8) In a binomial experiment, any single trial contains only two possible outcomes and successive trials are independent. Answer: TRUE Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 9) In a binomial distribution, p, the probability of getting a successful outcome on any single trial, increases proportionately with every success. Answer: FALSE Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 10) The assumption of independent trials in a binomial distribution is not a great concern if the sample size is smaller than 1/20th of the population size. Answer: TRUE Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 11) For a binomial distribution in which the probability of success is p = 0.5, the variance is twice the mean. Answer: FALSE Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 2

12) The Poisson distribution is a continuous distribution which is very useful in solving waiting time problems. Answer: FALSE Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 13) Both the Poisson and the binomial distributions are discrete distributions and both have a given number of trials. Answer: FALSE Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 14) The Poisson distribution is best suited to describe occurrences of rare events in a situation where each occurrence is independent of the other occurrences. Answer: TRUE Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 15) For the Poisson distribution the mean represents twice the value of the standard deviation. Answer: FALSE Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 16) A binomial distribution is better than a Poisson distribution to describe the occurrence of major oil spills in the Gulf of Mexico. Answer: FALSE Diff: 2 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 17) For the Poisson distribution the mean and the variance are the same. Answer: TRUE Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 3

18) Poisson distribution describes the occurrence of discrete events that may occur over a continuous interval of time or space. Answer: TRUE Diff: 2 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 19) A Poisson distribution is characterized by one parameter. Answer: TRUE Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 20) A hypergeometric distribution applies to experiments in which the trials represent sampling with replacement. Answer: FALSE Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 21) As in a binomial distribution, each trial of a hypergeometric distribution results in one of two mutually exclusive outcomes, i.e., either a success or a failure. Answer: TRUE Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 22) The number of successes, A, in the population in a hypergeometric distribution is unknown. Answer: FALSE Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 23) In a hypergeometric distribution the population, N, is finite and known. Answer: TRUE Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula.

24) The volume of liquid in an unopened 1-gallon can of paint is an example of . A) the binomial distribution B) both discrete and continuous variable C) a continuous random variable D) a discrete random variable E) a constant Answer: C Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 25) The number of finance majors within the School of Business is an example of . A) a discrete random variable B) a continuous random variable C) the Poisson distribution D) the normal distribution E) a constant Answer: A Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 26) The speed at which a jet plane can fly is an example of . A) neither discrete nor continuous random variable B) both discrete and continuous random variable C) a continuous random variable D) a discrete random variable E) a constant Answer: C Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution.

27) The number of sandwiches sold at a deli during a 2-hour lunch period would be an example of . A) neither discrete nor continuous random variable B) both discrete and continuous random variable C) a continuous random variable D) a discrete random variable E) a constant Answer: D Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 28) The time between customer arrivals at a deli counter during a 2-hour lunch period would be an example of . A) neither discrete nor continuous random variable B) both discrete and continuous random variable C) a continuous random variable D) a discrete random variable E) a constant Answer: C Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 29) If the owner of a deli wanted to see all the outcomes of how many sandwiches are sold during 2-hour lunch periods with the probabilities of each outcome, this would be . A) a discrete random variable B) a continuous random variable C) the continuous distribution D) the normal distribution E) a discrete distribution Answer: E Diff: 2 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution.

30) If the owner of a deli wanted to see all the outcomes of the time between arrivals at the deli counter during 2-hour lunch periods with the probabilities of each outcome, this would be . A) a discrete random variable B) a continuous random variable C) the continuous distribution D) the normal distribution E) a discrete distribution Answer: C Diff: 2 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 31) A manufacturer of cans of soda pop weighs every 10th can to determine if the production line is working properly. The weight of the cans would be an example of . A) a discrete random variable B) a continuous random variable C) the continuous distribution D) the normal distribution E) a discrete distribution Answer: B Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 32) A manufacturer of cans of soda pop weights every 10th can to determine if the production line is working properly. The number of cans weighed in an hour would be an example of . A) a discrete random variable B) a continuous random variable C) the continuous distribution D) the normal distribution E) a discrete distribution Answer: A Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution.

33) A manufacturer of cans of soda pop weighs every 10th can to determine if the production line is working properly. The possible outcomes of how many cans weighed in an hour and their associated probabilities would be an example of . A) a discrete random variable B) a continuous random variable C) the continuous distribution D) the normal distribution E) a discrete distribution Answer: E Diff: 2 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. 34) In American Roulette, there are two zeroes and 36 non-zero numbers (18 red and 18 black). If a player bets 1 unit on red, his chance of winning 1 unit is therefore 18/38 and his chance of losing 1 unit (or winning -1) is 20/38. Let x be the player profit per game. The mean (average) value of x is approximately . A) 0.0526 B) -0.0526 C) 1 D) -1 E) 0 Answer: B Diff: 1 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution.

35) A recent analysis of the number of rainy days per month found the following outcomes and probabilities. Number of Raining Days (x) 3 4 5

P(x) .40 .20 .40

The mean of this distribution is . A) 2 B) 3 C) 4 D) 5 E) <1 Answer: C Diff: 1 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution. 36) A recent analysis of the number of rainy days per month found the following outcomes and probabilities. Number of Raining Days (x) 3 4 5

P(x) .40 .20 .40

The standard deviation of this distribution is . A) .800 B) .894 C) .400 D) 4.00 E) .457 Answer: B Diff: 2 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution.

37) You are offered an investment opportunity. Its outcomes and probabilities are presented in the following table. x -$1,000 $0 +$1,000

P(x) .40 .20 .40

Which of the following statements is true? A) This distribution is skewed to the right. B) This is a binomial distribution. C) This distribution is symmetric. D) This distribution is skewed to the left. E) This is a Poisson distribution Answer: C Diff: 1 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution. 38) A market research team compiled the following discrete probability distribution on the number of sodas the average adult drinks each day. In this distribution, x represents the number of sodas which an adult drinks. x 0 1 2 3

P(x) 0.30 0.10 0.50 0.10

The mean (average) value of x is . A) 1.40 B) 1.75 C) 2.10 D) 2.55 E) 3.02 Answer: A Diff: 1 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution.

39) A market research team compiled the following discrete probability distribution on the number of sodas the average adult drinks each day. In this distribution, x represents the number of sodas which an adult drinks. x 0 1 2 3

P(x) 0.30 0.10 0.50 0.10

The standard deviation of x is . A) 1.04 B) 0.89 C) 1.40 D) 1.02 E) .588 Answer: D Diff: 3 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution. 40) A market research team compiled the following discrete probability distribution. In this distribution, x represents the number of automobiles owned by a family. x 0 1 2 3

P(x) 0.10 0.10 0.50 0.30

Which of the following statements is true? A) This distribution is skewed to the right. B) This is a binomial distribution. C) This is a normal distribution. D) This distribution is skewed to the left. E) This distribution is bimodal. Answer: D Diff: 1 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution.

41) A market research team compiled the following discrete probability distribution for families residing in Randolph County. In this distribution, x represents the number of evenings the family dines outside their home during a week. x 0 1 2 3

P(x) 0.30 0.50 0.10 0.10

The mean (average) value of x is . A) 1.0 B) 1.5 C) 2.0 D) 2.5 E) 3.0 Answer: A Diff: 1 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution. 42) A market research team compiled the following discrete probability distribution for families residing in Randolph County. In this distribution, x represents the number of evenings the family dines outside their home during a week. x 0 1 2 3

P(x) 0.30 0.50 0.10 0.10

The standard deviation of x is . A) 1.00 B) 2.00 C) 0.80 D) 0.89 E) 1.09 Answer: D Diff: 3 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution.

43) If x has a binomial distribution with p = .5, then the distribution of x is . A) skewed to the right B) skewed to the left C) symmetric D) a Poisson distribution E) a hypergeometric distribution Answer: C Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 44) The following graph is a binomial distribution with n = 6.

This graph reveals that _ . A) p > 0.5 B) p = 1.0 C) p = 0 D) p < 0.5 E) p = 1.5 Answer: A Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

4) The following graph is a binomial distribution with n = 6.

This graph reveals that _ . A) p > 0.5 B) p = 1.0 C) p = 0 D) p < 0.5 E) p = 1.5 Answer: D Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

5) The following graph is a binomial distribution with n = 6.

This graph reveals that _ . A) p = 0.5 B) p = 1.0 C) p = 0 D) p < 0.5 E) p = 1.5 Answer: A Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 47) If x is a binomial random variable with n = 10 and p = 0.8, the mean value of x is . A) 6 B) 4.8 C) 3.2 D) 8 E) 48 Answer: D Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

48) If x is a binomial random variable with n = 10 and p = 0.8, the standard deviation of x is . A) 8.0 B) 1.26 C) 1.60 D) 64.0 E) 10 Answer: B Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 49) If x is a binomial random variable with n = 10 and p = 0.8, what is the probability that x is equal to 4? A) .0055 B) .0063 C) .124 D) .232 E) .994 Answer: A Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 50) Twenty-five individuals are randomly selected out of 100 shoppers leaving a local bedding store. Each shopper was asked if they made a purchase during their visit. Each of the shoppers has the same probability of answering "yes" to having made a purchase. The probability that exactly four of the twenty-five shoppers made a purchase could best be found by . A) using the normal distribution B) using the binomial distribution C) using the Poisson distribution D) using the exponential distribution E) using the uniform distribution Answer: B Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

51) During a recent sporting event, a quarter is tossed to determine which team picks the starting side. Suppose the referee says the coin will be tossed 3 times and the best two out of three wins If team A calls "heads", what is the probability that exactly two heads are observed in three tosses? A) .313 B) .375 C) .625 D) .875 E) .500 Answer: B Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 52) A student randomly guesses the answers to a five-question true/false test. If there is a 50% chance of guessing correctly on each question, what is the probability that the student misses exactly 1 question? A) 0.200 B) 0.031 C) 0.156 D) 0.073 E) 0.001 Answer: C Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 53) A student randomly guesses the answers to a five-question true/false test. If there is a 50% chance of guessing correctly on each question, what is the probability that the student misses no questions? A) 0.000 B) 0.200 C) 0.500 D) 0.031 E) 1.000 Answer: D Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

54) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system, and orders an inspection of a random sample of vouchers issued since January 1, 2021. A sample of ten vouchers is randomly selected, without replacement, from the population of 2,000 vouchers. Each voucher in the sample is examined for errors and the number of vouchers in the sample with errors is denoted by x. If 20% of the population of vouchers contain errors, P(x = 0) is . A) 0.8171 B) 0.1074 C) 0.8926 D) 0.3020 E) 0.2000 Answer: B Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 55) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system, and orders an inspection of a random sample of vouchers issued since January 1, 2021. A sample of ten vouchers is randomly selected, without replacement, from the population of 2,000 vouchers. Each voucher in the sample is examined for errors and the number of vouchers in the sample with errors is denoted by x. If 20% of the population of vouchers contain errors, P(x > 0) is . A) 0.8171 B) 0.1074 C) 0.8926 D) 0.3020 E) 1.0000 Answer: C Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

56) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system, and orders an inspection of a random sample of vouchers issued since January 1, 2021. A sample of ten vouchers is randomly selected, without replacement, from the population of 2,000 vouchers. Each voucher in the sample is examined for errors and the number of vouchers in the sample with errors is denoted by x. If 20% of the population of vouchers contains errors, the mean value of x is . A) 400 B) 2 C) 200 D) 5 E) 1 Answer: B Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 57) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system, and orders an inspection of a random sample of vouchers issued since January 1, 2021. A sample of ten vouchers is randomly selected, without replacement, from the population of 2,000 vouchers. Each voucher in the sample is examined for errors and the number of vouchers in the sample with errors is denoted by x. If 20% of the population of vouchers contains errors, the standard deviation of x is . A) 1.26 B) 1.60 C) 14.14 D) 3.16 E) 0.00 Answer: A Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

58) Dorothy Little purchased a mailing list of 2,000 names and addresses for her mail order business, but after scanning the list she doubts the authenticity of the list. She randomly selects five names from the list for validation. If 40% of the names on the list are non-authentic, and x is the number of non-authentic names in her sample, P(x = 0) is . A) 0.8154 B) 0.0467 C) 0.0778 D) 0.4000 E) 0.5000 Answer: C Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 59) Dorothy Little purchased a mailing list of 2,000 names and addresses for her mail order business, but after scanning the list she doubts the authenticity of the list. She randomly selects five names from the list for validation. If 40% of the names on the list are non-authentic, and x is the number of non-authentic names in her sample, P(x < 2) is . A) 0.3370 B) 0.9853 C) 0.9785 D) 0.2333 E) 0.5000 Answer: A Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 60) Dorothy Little purchased a mailing list of 2,000 names and addresses for her mail order business, but after scanning the list she doubts the authenticity of the list. She randomly selects five names from the list for validation. If 40% of the names on the list are non-authentic, and x is the number of non-authentic names in her sample, P(x > 0) is . A) 0.2172 B) 0.9533 C) 0.1846 D) 0.9222 E) 1.0000 Answer: D Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

61) Dorothy Little purchased a mailing list of 2,000 names and addresses for her mail order business, but after scanning the list she doubts the authenticity of the list. She randomly selects five names from the list for validation. If 40% of the names on the list are non-authentic, and x is the number on non-authentic names in her sample, the expected (average) value of x is . A) 2.50 B) 2.00 C) 1.50 D) 1.25 E) 1.35 Answer: B Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 62) A large industrial firm allows a discount on any invoice that is paid within 30 days. Of all invoices, 10% receive the discount. In a company audit, 10 invoices are sampled at random. The probability that fewer than 3 of the 10 sampled invoices receive the discount is approximately . A) 0.1937 B) 0.057 C) 0.001 D) 0.3486 E) 0.9298 Answer: E Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. 63) A large industrial firm allows a discount on any invoice that is paid within 30 days. Of all invoices, 10% receive the discount. In a company audit, 15 invoices are sampled at random. The mean (average) value of the number of the 15 sampled invoices that receive discount is . A) 1 B) 3 C) 1.5 D) 2 E) 10 Answer: C Diff: 1 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table.

64) In a certain communications system, there is an average of 1 transmission error per 10 seconds. Assume that the distribution of transmission errors is Poisson. The probability of 1 error in a period of one-half minute is approximately . A) 0.1494 B) 0.3333 C) 0.3678 D) 0.1336 E) 0.03 Answer: A Diff: 3 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 65) It is known that screws produced by a certain company will be defective with a probability of .01, independently of each other. The company sells the screws in packages of 25 and offers a money-back guarantee that at most, 1 of the 25 screws is defective. Using Poisson approximation for binomial distribution, the probability that the company must replace a package is approximately . A) 0.01 B) 0.1947 C) 0.7788 D) 0.0265 E) 0.2211 Answer: D Diff: 3 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 66) The number of cars arriving at a toll booth in five-minute intervals is Poisson distributed with a mean of 3 cars arriving in five-minute time intervals. The probability of 5 cars arriving over a five-minute interval is . A) 0.0940 B) 0.0417 C) 0.1500 D) 0.1008 E) 0.2890 Answer: D Diff: 2 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table.

67) The number of cars arriving at a toll booth in five-minute intervals is Poisson distributed with a mean of 3 cars arriving in five-minute time intervals. The probability of 3 cars arriving over a five-minute interval is . A) 0.2700 B) 0.0498 C) 0.2240 D) 0.0001 E) 0.0020 Answer: C Diff: 2 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 68) Assume that a random variable has a Poisson distribution with a mean of 5 occurrences per ten minutes. The number of occurrences per hour follows a Poisson distribution with λ equal to . A) 5 B) 60 C) 30 D) 10 E) 20 Answer: C Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 69) On Monday mornings, customers arrive at the coffee shop drive-thru at the rate of 6 cars per fifteen-minute interval. Using the Poisson distribution, the probability that five cars will arrive during the next fifteen-minute interval is . A) 0.1008 B) 0.0361 C) 0.1339 D) 0.1606 E) 0.5000 Answer: D Diff: 2 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table.

70) On Monday mornings, customers arrive at the coffee shop drive-thru at the rate of 6 cars per fifteen-minute interval. Using the Poisson distribution, the probability that five cars will arrive during the next five-minute interval is . A) 0.1008 B) 0.0361 C) 0.1339 D) 0.1606 E) 0.3610 Answer: B Diff: 3 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 71) The Poisson distribution is being used to approximate a binomial distribution. If n = 30 and p = 0.03, what value of lambda would be used? A) 0.09 B) 9.0 C) 0.90 D) 90 E) 30 Answer: C Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 72) The Poisson distribution is being used to approximate a binomial distribution. If n = 60 and p = 0.02, what value of lambda would be used? A) 0.02 B) 12 C) 0.12 D) 1.2 E) 120 Answer: D Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table.

73) The number of bags arriving on the baggage claim conveyor belt in a 3-minute time period would best be modeled with the . A) binomial distribution B) hypergeometric distribution C) Poisson distribution D) hyperbinomial distribution E) exponential distribution Answer: C Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 74) The number of defects per 1,000 feet of extruded plastic pipe is best modeled with the . A) Poisson distribution B) Pascal distribution C) binomial distribution D) hypergeometric distribution E) exponential distribution Answer: A Diff: 1 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. 75) Which of the following conditions is not a condition for the hypergeometric distribution? A) the probability of success is the same on each trial B) sampling is done without replacement C) there are only two possible outcomes D) trials are dependent E) n < 5%N Answer: A Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula.

76) The hypergeometric distribution must be used instead of the binomial distribution when . A) sampling is done with replacement B) sampling is done without replacement C) n ≥ 5% N D) both B and C E) there are more than two possible outcomes Answer: D Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 77) The probability of selecting 2 male baseball players and 3 female basketball players for an intermural competition at a small sports camp would best be modeled with the . A) binomial distribution B) hypergeometric distribution C) Poisson distribution D) hyperbinomial distribution E) exponential distribution Answer: B Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 78) The probability of selecting 3 female employees and 7 male employees to win a promotional trip a company with 10 female and 50 male employees would best be modeled with the . A) binomial distribution B) hypergeometric distribution C) Poisson distribution D) hyperbinomial distribution E) exponential distribution Answer: B Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula.

79) Suppose an interdisciplinary committee of 3 faculty members is to be selected from a group consisting of 4 men and 5 women. The probability that all three of the selected faculty are men is approximately . A) 0.05 B) 0.33 C) 0.11 D) 0.80 E) 0.90 Answer: A Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 80) Suppose an interdisciplinary committee of 3 faculty members is to be selected from a group consisting of 4 men and 5 women. The probability that one male faculty and two female faculty are selected is approximately . A) 0.15 B) 0.06 C) 0.33 D) 0.48 E) 0.58 Answer: D Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 81) Aluminum castings are processed in lots of five each. A sample of two castings is randomly selected from each lot for inspection. A particular lot contains one defective casting; and x is the number of defective castings in the sample. P(x = 0) is . A) 0.2 B) 0.4 C) 0.6 D) 0.8 E) 1.0 Answer: C Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula.

82) Aluminum castings are processed in lots of five each. A sample of two castings is randomly selected from each lot for inspection. A particular lot contains one defective casting; and x is the number of defective castings in the sample. P(x = 1) is . A) 0.2 B) 0.4 C) 0.6 D) 0.8 E) 1.0 Answer: B Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 83) Circuit boards for wireless telephones are etched, in an acid bath, in batches of 100 boards. A sample of seven boards is randomly selected from each lot for inspection. A batch contains two defective boards; and x is the number of defective boards in the sample. P(x = 1) is . A) 0.1315 B) 0.8642 C) 0.0042 D) 0.6134 E) 0.6789 Answer: A Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 84) Circuit boards for wireless telephones are etched, in an acid bath, in batches of 100 boards. A sample of seven boards is randomly selected from each lot for inspection. A particular batch contains two defective boards; and x is the number of defective boards in the sample. P(x = 2) is . A) 0.1315 B) 0.8642 C) 0.0042 D) 0.6134 E) 0.0034 Answer: C Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula.

85) Circuit boards for wireless telephones are etched, in an acid bath, in batches of 100 boards. A sample of seven boards is randomly selected from each lot for inspection. A particular batch contains two defective boards; and x is the number of defective boards in the sample. P(x = 0) is . A) 0.1315 B) 0.8642 C) 0.0042 D) 0.6134 E) 0.8134 Answer: B Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 86) Ten policyholders file claims with CareFree Insurance. Three of these claims are fraudulent. Claims manager Earl Evans randomly selects three of the ten claims for thorough investigation. If x represents the number of fraudulent claims in Earl's sample, P(x = 0) is . A) 0.0083 B) 0.3430 C) 0.0000 D) 0.2917 E) 0.8917 Answer: D Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 87) Ten policyholders file claims with CareFree Insurance. Three of these claims are fraudulent. Claims manager Earl Evans randomly selects three of the ten claims for thorough investigation. If x represents the number of fraudulent claims in Earl's sample, P(x = 1) is . A) 0.5250 B) 0.4410 C) 0.3000 D) 0.6957 E) 0.9957 Answer: A Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula.

88) If sampling is performed without replacement, the hypergeometric distribution should be used. However, the binomial may be used to approximate this if . A) n > 5%N B) n < 5%N C) the population size is very small D) there are more than two possible outcomes of each trial E) the outcomes are continuous Answer: B Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 89) One hundred policyholders file claims with CareFree Insurance. Ten of these claims are fraudulent. Claims manager Earl Evans randomly selects four of the one hundred claims for thorough investigation. If x represents the number of fraudulent claims in Earl's sample, x has a distribution. A) continuous B) normal C) binomial D) hypergeometric E) exponential Answer: D Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula. 90) One hundred policyholders file claims with CareFree Insurance. Ten of these claims are fraudulent. Claims manager Earl Evans randomly selects four of the one hundred claims for thorough investigation. If x represents the number of fraudulent claims in Earl's sample, x has a . A) normal distribution B) hypergeometric distribution, but may be approximated by a binomial C) binomial distribution, but may be approximated by a normal D) binomial distribution, but may be approximated by a Poisson E) exponential distribution Answer: B Diff: 1 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.5: Solve problems involving the hypergeometric distribution using the hypergeometric formula.

91) The age of the employees in a given company, measured in whole number of years, is an example of . A) a discrete random variable B) a continuous random variable C) the Poisson distribution D) the normal distribution E) a constant Answer: A Diff: 1 Response: See section 5.1 Discrete versus Continuous Distributions Learning Objective: 5.1: Define a random variable in order to differentiate between a discrete distribution and a continuous distribution. Bloom's level: Knowledge 92) Epsilon Manufacturing Company makes a specialized electronic product used in the automotive industry. This product has 4 components, each of which could be defective. So the total number of defects of this product can be 0, 1, 2, 3, or 4. The following table provides the probabilities of the number of defects: Number of defects 0 1 2 3 4

Probability .508 .302 .105 .080 .005

The mean number of defects is . A) .75 B) .77 C) .80 D) .85 E) .87 Answer: B Diff: 2 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution. Bloom's level: Knowledge

93) Epsilon Manufacturing Company makes a specialized electronic product used in the automotive industry. This product has 4 components, each of which could be defective. So the total number of defects of this product can be 0, 1, 2, 3, or 4. The following table provides the probabilities of the number of defects: Number of defects 0 1 2 3 4

Probability .508 .302 .105 .080 .005

The standard deviation of the number of defects is . A) 0.89 B) 0.90 C) 0.91 D) 0.92 E) 0.96 Answer: E Diff: 3 Response: See section 5.2 Describing a Discrete Distribution Learning Objective: 5.2: Determine the mean, variance, and standard deviation of a discrete distribution. Bloom's level: Knowledge 94) From recent polls about customer satisfaction, you know that 85% of the clients of your company are highly satisfied and want to renew their contracts. If you select a random sample of 30 clients, what is the probability that exactly 25 are highly satisfied? A) .184 B) .186 C) .188 D) .190 E) .192 Answer: B Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. Bloom's level: Application

95) From recent polls about customer satisfaction, you know that 85% of the clients of your company are highly satisfied and want to renew their contracts. If you select a random sample of 30 clients, what is the probability that at least one client is dissatisfied? A) .984 B) .986 C) .988 D) .990 E) .992 Answer: E Diff: 2 Response: See section 5.3 Binomial Distribution Learning Objective: 5.3: Solve problems involving the binomial distribution using the binomial formula and the binomial table. Bloom's level: Application 96) Suppose that the number of calls coming per minute into an airline reservation center follows a Poisson distribution. Assume that the mean number of calls is 3 calls per minute. The probability that no calls are received in a given one-minute period is . A) 0.0498 B) 0.0508 C) 0.0528 D) 0.0548 E) 0.0558 Answer: A Diff: 2 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. Bloom's level: Application 97) Suppose that the number of calls coming per minute into an airline reservation center follows a Poisson distribution. Assume that the mean is 3 calls per minute. The probability that at least two calls are received in a given two-minute period is . A) 0.9822 B) 0.9824 C) 0.9826 D) 0.9828 E) 0.9830 Answer: C Diff: 2 Response: See section 5.4 Poisson Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. Bloom's level: Application

98) Suppose that during a given week, 20 new customers have signed up for a specialized service your company provides. Eight of these new customers are automotive companies, and the remaining twelve are financial services firms. If a random sample of 5 of these new customers will be selected for a study of customer satisfaction in one month, what is the probability that 2 of the selected customers are financial services firms? A) 0.232 B) 0.234 C) 0.236 D) 0.238 E) 0.240 Answer: D Diff: 2 Response: See section 5.5 Hypergeometric Distribution Learning Objective: 5.4: Solve problems involving the Poisson distribution using the Poisson formula and the Poisson table. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 6 Continuous Distributions 1) A uniform continuous distribution is also referred to as a rectangular distribution. Answer: TRUE Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 2) The height of the rectangle depicting a uniform distribution is the probability of each outcome and it is the same for all of the possible outcomes. Answer: FALSE Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 3) The area of the rectangle depicting a uniform distribution is always equal to the mean of the distribution. Answer: FALSE Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 4) Many human characteristics such as height and weight and many variables such as household insurance and cost per square foot of rental space are normally distributed. Answer: TRUE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 5) Normal distribution is a symmetrical distribution with its tails extending to infinity on either side of the mean. Answer: TRUE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

6) Since a normal distribution curve extends from minus infinity to plus infinity, the area under the curve is infinity. Answer: FALSE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 7) A z-score is the number of standard deviations that a value of a random variable is above or below the mean. Answer: TRUE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 8) A normal distribution with a mean of zero and a standard deviation of 1 is called a null distribution. Answer: FALSE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 9) A standard normal distribution has a mean of one and a standard deviation of three. Answer: FALSE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 10) The standard normal distribution is also called a finite distribution because its mean is zero and standard deviation one, always. Answer: FALSE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

11) In a standard normal distribution, if the area under curve to the right of a z-value is 0.10, then the area to the left of the same z-value is -0.10. Answer: FALSE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 12) The area under the standard normal distribution between -1 and 1 is twice the area between 0 and 1. Answer: TRUE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 13) The area under the standard normal distribution between 0 and 2 is twice the area between 0 and 1. Answer: FALSE Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 14) The normal approximation for binomial distribution can be used when n = 10 and p = 1/5. Answer: FALSE Diff: 1 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 15) Binomial distributions in which the sample sizes are large may be approximated by a Poisson distribution. Answer: FALSE Diff: 1 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity.

16) A correction for continuity must be made when approximating the binomial distribution problems using a normal distribution. Answer: TRUE Diff: 1 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 17) If the probability of a binomial distribution was considering values that were greater than 14, then the correction for continuity would start at 13.5. Answer: FALSE Diff: 2 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 18) If the probability of a binomial distribution was considering values that were less than 14, then the correction for continuity would start at 13.5. Answer: TRUE Diff: 2 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 19) Correcting for continuity is necessary when a researcher is using a discrete distribution to estimate probabilities from a continuous distribution. Answer: FALSE Diff: 2 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 20) If arrivals at a bank followed a Poisson distribution, then the time between arrivals would follow a binomial distribution. Answer: FALSE Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution.

21) For an exponential distribution, the mean is always equal to its variance. Answer: FALSE Diff: 1 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 22) For an exponential distribution, the mean and the median are equal. Answer: FALSE Diff: 1 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 23) The exponential distribution is a continuous distribution that is closely related to the Poisson distribution. Answer: TRUE Diff: 1 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 24) The mean of an exponential distribution is equal to 1/λ. Answer: TRUE Diff: 1 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 25) The exponential distribution is especially useful in queuing problems to analyze the interarrival times. Answer: TRUE Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution.

26) Suppose the number of parking spots at urban grocery stores is uniformly distributed over the interval 90 to 140, inclusively (90 ≤ x ≤ 140), then the height of this distribution, f(x), is . A) 1/90 B) 1/50 C) 1/140 D) 1/200 E) 1/500 Answer: B Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 27) If the number of parking spots at urban grocery stores is uniformly distributed over the interval 90 to 140, inclusively (90 ≤ x ≤ 140), then the mean of this distribution is . A) 115 B) 230 C) 45 D) 70 E) unknown Answer: A Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 28) If the number of parking spots at urban grocery stores is uniformly distributed over the interval 90 to 140, inclusively (90 ≤ x ≤ 140), then the standard deviation of this distribution is . A) 4.16 B) 50 C) 14.4 D) 7.07 E) 28.2 Answer: C Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution.

29) If the number of parking spots at urban grocery stores is uniformly distributed over the interval 90 to 140, inclusively (90 ≤ x ≤ 140), then P(x = exactly 100) is . A) 0.750 B) 0.000 C) 0.333 D) 0.500 E) 0.900 Answer: B Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 30) If x is uniformly distributed over the interval 8 to 12, inclusively (8 ≤ x ≤ 12), then the probability, P(9 x ≤ 11) is . A) 0.250 B) 0.500 C) 0.333 D) 0.750 E) 1.000 Answer: B Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 31) If x is uniformly distributed over the interval 8 to 12, inclusively (8 ≤ x ≤ 12), then the probability, P(10.0 ≤ x ≤ 11.5), is . A) 0.250 B) 0.333 C) 0.375 D) 0.500 E) 0.750 Answer: C Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 32) If x is uniformly distributed over the interval 8 to 12, inclusively (8 ≤ x ≤ 12), then the probability, P(13 x ≤ 15), is . A) 0.250 B) 0.500 C) 0.375 D) 0.000 E) 1.000 Answer: D Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 7

33) If x is uniformly distributed over the interval 8 to 12, inclusively (8 ≤ x ≤ 12), then P(x < 7) is . A) 0.500 B) 0.000 C) 0.375 D) 0.250 E) 1.000 Answer: B Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 34) If x is uniformly distributed over the interval 8 to 12, inclusively (8 ≤ x ≤ 12), then P(x ≤ 11) is . A) 0.750 B) 0.000 C) 0.333 D) 0.500 E) 1.000 Answer: A Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 35) If x is uniformly distributed over the interval 8 to 12, inclusively (8 ≤ x ≤ 12), then P(x ≥ 10) is . A) 0.750 B) 0.000 C) 0.333 D) 0.500 E) 0.900 Answer: D Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution.

36) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the height of this distribution, f(x), is . A) 1/10 B) 1/20 C) 1/30 D) 12/50 E) 1/60 Answer: A Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 37) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the mean of this distribution is . A) 50 B) 25 C) 10 D) 15 E) 5 Answer: B Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 38) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the standard deviation of this distribution is . A) unknown B) 8.33 C) 0.833 D) 2.89 E) 1.89 Answer: D Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution.

39) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the probability that an oil change job is completed in 25 to 28 minutes, inclusively, i.e., P(25 ≤ x ≤ 28) is . A) 0.250 B) 0.500 C) 0.300 D) 0.750 E) 81.000 Answer: C Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 40) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the probability that an oil change job is completed in 21.75 to 24.25 minutes, inclusively, i.e., P(21.75 ≤ x ≤ 24.25) is . A) 0.250 B) 0.333 C) 0.375 D) 0.000 E) 1.000 Answer: A Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 41) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the probability that an oil change job is completed in 33 to 35 minutes, inclusively, i.e., P(33 ≤ x ≤ 35) is . A) 0.5080 B) 0.000 C) 0.375 D) 0.200 E) 1.000 Answer: B Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution.

42) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the probability that an oil change job is completed in less than 17 minutes, i.e., P(x < 17) is . A) 0.500 B) 0.300 C) 0.000 D) 0.250 E) 1.000 Answer: C Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 43) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the probability that an oil change job is completed in less than or equal to 22 minutes, i.e., P(x ≤ 22) is . A) 0.200 B) 0.300 C) 0.000 D) 0.250 E) 1.000 Answer: A Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. 44) If x, the time (in minutes) to complete an oil change job at certain auto service station, is uniformly distributed over the interval 20 to 30, inclusively (20 ≤ x ≤ 30), then the probability that an oil change job will be completed 24 minutes or more, i.e., P(x ≥ 24) is . A) 0.100 B) 0.000 C) 0.333 D) 0.600 E) 1.000 Answer: D Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution.

45) The normal distribution is an example of . A) a discrete distribution B) a continuous distribution C) a bimodal distribution D) an exponential distribution E) a binomial distribution Answer: B Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 46) The total area underneath any normal curve is equal to . A) the mean B) one C) the variance D) the coefficient of variation E) the standard deviation Answer: B Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 47) The area to the left of the mean in any normal distribution is equal to . A) the mean B) 1 C) the variance D) 0.5 E) -0.5 Answer: D Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

48) A standard normal distribution has the following characteristics . A) the mean and the variance are both equal to 1 B) the mean and the variance are both equal to 0 C) the mean is equal to the variance D) the mean is equal to 0 and the variance is equal to 1 E) the mean is equal to the standard deviation Answer: D Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 49) Let z be a normal random variable with mean 0 and standard deviation 1. What is P(z < 1.3)? A) 0.4032 B) 0.9032 C) 0.0968 D) 0.3485 E) 0. 5485 Answer: B Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 50) Let z be a normal random variable with mean 0 and standard deviation 1. What is P(1.3 < z < 2.3)? A) 0.4032 B) 0.9032 C) 0.4893 D) 0.0861 E) 0.0086 Answer: D Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

51) Let z be a normal random variable with mean 0 and standard deviation 1. What is P(z > 2.4)? A) 0.4918 B) 0.9918 C) 0.0082 D) 0.4793 E) 0.0820 Answer: C Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 52) Let z be a normal random variable with mean 0 and standard deviation 1. What is P(z < 2.1)? A) 0.4821 B) -0.4821 C) 0.9821 D) 0.0179 E) -0.0179 Answer: D Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 53) Let z be a normal random variable with mean 0 and standard deviation 1. What is P(z > 1.1)? A) 0.3643 B) 0.8643 C) 0.1357 D) -0.1357 E) -0.8643 Answer: B Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

54) Let z be a normal random variable with mean 0 and standard deviation 1. Which of these most closely reflects P(-2.25 < z < -1.1)? A) 0.3643 B) 0.8643 C) 0.1234 D) 0.4878 E) 0.5000 Answer: C Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 55) Let z be a normal random variable with mean 0 and standard deviation 1. The 50th percentile of z is most closely reflected by . A) 0.6700 B) -1.254 C) 0.0000 D) 1.2800 E) 0.5000 Answer: C Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 56) Let z be a normal random variable with mean 0 and standard deviation 1. The 75th percentile of z is most closely reflected by . A) 0.6700 B) -1.254 C) 0.0000 D) 1.2800 E) 0.5000 Answer: A Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

57) Let z be a normal random variable with mean 0 and standard deviation 1. The 90th percentile of z is most closely reflected by . A) 1.645 B) -1.254 C) 1.960 D) 1.282 E) 1.650 Answer: D Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 58) A z score is the number of that a value is from the mean. A) variances B) standard deviations C) units D) miles E) minutes Answer: B Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 59) Within a range of z scores from -1 to +1, you can expect to find per cent of the values in a normal distribution. A) 95 B) 99 C) 68 D) 34 E) 100 Answer: C Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

60) Within a range of z scores from -2 to +2, you can expect to find per cent of the values in a normal distribution. A) 95 B) 99 C) 68 D) 34 E) 100 Answer: A Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 61) Suppose the total time to fill a routine prescription at a local pharmacy averages 35 minutes starting from the time the physician places the order to the time it is dispensed. Assume the standard deviation is 11 minutes. The z-score for x = 46 is . A) 1.0 B) -1.0 C) 11 D) -11 E) .10 Answer: A Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 62) Suppose the total time to fill a routine prescription at a local pharmacy averages 35 minutes starting from the time the physician places the order to the time it is dispensed. Assume the standard deviation is 11 minutes. A z score was calculated for a number, and the z score is 3.4. What is x? A) 37.4 B) 72.4 C) 0.00 D) 68.0 E) 2.0.8 Answer: B Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

63) Suppose the total time to fill a routine prescription at a local pharmacy averages 35 minutes starting from the time the physician places the order to the time it is dispensed. Assume the standard deviation is 11 minutes and the z score is -1.3. What is x? A) 20.7 B) 0.0 C) -14.3 D) 14.3 E) -20.7 Answer: A Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 64) Suppose the total time to fill a routine prescription at a local pharmacy averages 35 minutes starting from the time the physician places the order to the time it is dispensed. Assume the standard deviation is 11 minutes and the z score is 0. What is x? A) -35.0 B) 0.0 C) 70.0 D) 35.0 E) -1.0 Answer: D Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 65) The expected (mean) life of a particular type of light bulb is 1,000 hours with a standard deviation of 50 hours. The life of this bulb is normally distributed. Which most closely reflects the probability that a randomly selected bulb would last longer than 1150 hours? A) 0.4987 B) 0.9987 C) 0.0013 D) 0.5013 E) 0.5513 Answer: C Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

66) The expected (mean) life of a particular type of light bulb is 1,000 hours with a standard deviation of 50 hours. The life of this bulb is normally distributed. Which most closely reflects the probability that a randomly selected bulb would last fewer than 1100 hours? A) 0.4772 B) 0.9772 C) 0.0228 D) 0.5228 E) 0.5513 Answer: B Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 67) The expected (mean) life of a particular type of light bulb is 1,000 hours with a standard deviation of 50 hours. The life of this bulb is normally distributed. Which most closely reflects the probability that a randomly selected bulb would last fewer than 940 hours? A) 0.3849 B) 0.8849 C) 0.1151 D) 0.6151 E) 0.6563 Answer: C Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 68) Suppose you are working with a data set that is normally distributed with a mean of 400 and a standard deviation of 20. Determine the most likely value of x such that 60% of the values are greater than x. A) 404.5 B) 395.5 C) 405.0 D) 394.9 E) 415.0 Answer: D Diff: 3 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

69) Sure Stone Tire Company has established that the useful life of a particular brand of its automobile tires is normally distributed with a mean of 40,000 miles and a standard deviation of 5000 miles. What is the probability that a randomly selected tire of this brand has a life of at most 30,000 miles? A) 0.5000 B) 0.4772 C) 0.0228 D) 0.9772 E) 1.0000 Answer: C Diff: 3 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 70) Sure Stone Tire Company has established that the useful life of a particular brand of its automobile tires is normally distributed with a mean of 40,000 miles and a standard deviation of 5000 miles. What is the probability that a randomly selected tire of this brand has a life of at least 50,000 miles? A) 0.0228 B) 0.9772 C) 0.5000 D) 0.4772 E) 1.0000 Answer: A Diff: 3 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 71) Sure Stone Tire Company has established that the useful life of a particular brand of its automobile tires is normally distributed with a mean of 40,000 miles and a standard deviation of 5000 miles. Which probability most closely reflects that a randomly selected tire of this brand has a life between 30,000 and 50,000 miles? A) 0.5000 B) 0.4772 C) 0.9544 D) 0.9772 E) 1.0000 Answer: C Diff: 3 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 20

72) The net profit from a certain investment is normally distributed with a mean of $2,500 and a standard deviation of $1,000. The probability that the investor will not have a net loss is . A) 0.4938 B) 0.0062 C) 0.9938 D) 0.5062 E) 0.0000 Answer: C Diff: 3 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 73) The net profit from a certain investment is normally distributed with a mean of $2,500 and a standard deviation of $1,000. The probability that the investor's net gain will be at least $2,000 is most closely reflected by . A) 0.0000 B) 0.3413 C) 0.6915 D) 0.0500 E) 0.5000 Answer: C Diff: 3 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 74) Completion time (from start to finish) of a building remodeling project is normally distributed with a mean of 200 work-days and a standard deviation of 10 work-days. The probability that the project will be completed within 185 work-days is . A) 0.0668 B) 0.4332 C) 0.5000 D) 0.9332 E) 0.9950 Answer: A Diff: 3 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve.

75) Completion time (from start to finish) of a building remodeling project is normally distributed with a mean of 200 work-days and a standard deviation of 10 work-days. To be 99% sure that we will not be late in completing the project, we should request a completion time of work-days. A) 211 B) 207 C) 223 D) 200 E) 250 Answer: C Diff: 3 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. 76) Let x be a binomial random variable with n = 35 and p = .20. If we use the normal distribution to approximate probabilities for this, we would use a mean of . A) 35 B) 20 C) 70 D) 7 E) 3.5 Answer: D Diff: 1 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 77) Let x be a binomial random variable with n=50 and p=.3. If we use the normal distribution to approximate probabilities for this, a correction for continuity should be made. To find the probability of more than 15 successes, we should find . A) P(x > 15.5) B) P(x > 15) C) P(x > 14.5) D) P(x < 14.5) E) P(x < 15) Answer: A Diff: 1 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity.

78) Let x be a binomial random variable with n = 50 and p = .3. The probability of less than or equal to 13 successes, when using the normal approximation for binomial is most closely reflected by . A) -0.6172 B) 0.3086 C) 3.240 D) 0.2324 E) 0.3217 F) -0.23224 Answer: E Diff: 3 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 79) Assuming an equal chance of a new baby being a boy or a girl (that is, p = 0.5), we would like to find the probability of 40 or more of the next 100 births at a local hospital will be boys. Using the normal approximation for binomial with a correction for continuity, we should use the z-score . A) 0.4 B) -2.1 C) 0.6 D) 2 E) -1.7 Answer: B Diff: 3 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 80) The probability that a call to an emergency help line is answered in less than 10 seconds is 0.8. Assume that the calls are independent of each other. Using the normal approximation for binomial with a correction for continuity, the probability that at least 75 of 100 calls are answered within 10 seconds is approximately . A) 0.8 B) 0.1313 C) 0.5235 D) 0.9154 E) 0.8687 Answer: D Diff: 3 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 23

81) For the normal approximation to be a good approximation for binomial distribution, n*p must be greater than . A) 50 B) 5 C) 0.5 D) 0.05 E) 0 Answer: B Diff: 1 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 82) The probability that someone will prefer Coke over Pepsi is 0.56. A researcher asked 90 people which soda pop they preferred, and used the normal approximation to the binomial with a correction for continuity, what would be the mean used in the calculation? A) 0.56 B) 90 C) 45.0 D) 50.4 E) 106.7 Answer: D Diff: 1 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 83) The probability that someone will prefer Coke over Pepsi is 0.56. A researcher asked 90 people which soda pop they preferred, and used the normal approximation to the binomial with a correction for continuity, what would be the standard deviation used in the calculation? A) 4.71 B) 22.2 C) 50.4 D) 39.6 E) 22.5 Answer: A Diff: 2 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity.

84) The probability that someone will prefer Coke over Pepsi is 0.56. A researcher asked 90 people which soda pop they preferred, and used the normal approximation to the binomial with a correction for continuity, what would be the probability at least 50 preferring Coke? A) 0.615 B) 0.507 C) 0.560 D) 0.900 E) 0.576 Answer: E Diff: 3 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 85) Based on the caterer's experience, 38% of attendees to events will prefer chicken for the main dish. As the caterer plans for an event attended by 780 individuals, the normal approximation will be used for the binomial with a correction for continuity. In this case, what is the average number that she would expect to prefer chicken, when determining the probability that less than 300 will prefer chicken? A) 296.4 B) 114.0 C) 483.6 D) 13.6 E) 183.8 Answer: A Diff: 1 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity.

86) Based on the caterer's experience, 38% of attendees to events will prefer chicken for the main dish. As the caterer plans for an event attended by 780 individuals, the normal approximation will be used for the binomial with a correction for continuity. In this case, what is the standard deviation of the number that she would expect to prefer chicken, when determining the probability that at less than 300 will prefer chicken? A) 114.0 B) 300.0 C) 183.8 D) 296.4 E) 13.6 Answer: E Diff: 2 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 87) Based on the caterer's experience, 38% of attendees to events will prefer chicken for the main dish. As the caterer plans for an event attended by 780 individuals, the normal approximation will be used for the binomial with a correction for continuity. In this case, what is the probability that less than 300 will prefer chicken? A) 0.604 B) 0.380 C) 0.590 D) 0.620 E) 0.618 Answer: C Diff: 3 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. 88) The exponential distribution is an example of . A) a discrete distribution B) a continuous distribution C) a bimodal distribution D) a normal distribution E) a symmetrical distribution Answer: B Diff: 1 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution.

89) For an exponential distribution with a lambda (λ) equal to 20, the mean equal to . A) 20 B) .05 C) 4.474 D) 1 E) 2.11 Answer: B Diff: 1 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 90) The average time between phone calls arriving at a call center is 30 seconds. Assuming that the time between calls is exponentially distributed, find the probability that more than a minuteelapses between calls. A) 0.135 B) 0.368 C) 0.865 D) 0.607 E) 0.709 Answer: A Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 91) The average time between phone calls arriving at a call center is 30 seconds. Assuming that the time between calls is exponentially distributed, find the probability that less than two minutes elapse between calls. A) 0.018 B) 0.064 C) 0.936 D) 0.982 E) 1.000 Answer: D Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution.

92) At a certain workstation in an assembly line, the time required to assemble a component is exponentially distributed with a mean time of 10 minutes. Find the probability that a component is assembled in 7 minutes or less? A) 0.349 B) 0.591 C) 0.286 D) 0.714 E) 0.503 Answer: E Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 93) At a certain workstation in an assembly line, the time required to assemble a component is exponentially distributed with a mean time of 10 minutes. Find the probability that a component is assembled in 3 to 7 minutes? A) 0.5034 B) 0.2592 C) 0.2442 D) 0.2942 E) 0.5084 Answer: C Diff: 3 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 94) Participants in a 2 day biking event, cross the finish line at a rate of 10 bike riders per fifteen minute interval. On average, how much time, in minutes, elapses between bike riders? A) 1.50 B) .0667 C) .1667 D) 1.00 E) 2.50 Answer: A Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution.

95) Participants in a 2 day biking event, cross the finish line at a rate of 10 bike riders per fifteen minute interval. The probability that at least 2 minutes will elapse between bike riders is closest to . A) .0000 B) .0498 C) .2636 D) .1353 E) .4647 Answer: C Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 96) Participants in a 2 day biking event, cross the finish line at a rate of 10 bike riders per fifteen minute interval. The probability that less than 10 minutes will elapse between car arrivals is . A) .0001 B) .9987 C) .0013 D) .6667 E) .1667 Answer: B Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 97) Inquiries arrive at a record message device according to a Poisson process at a rate of 15 inquiries per minute. The probability that it takes more than 12 seconds for the first inquiry to arrive is approximately _ . A) 0.05 B) 0.75 C) 0.25 D) 0.27 E) 0.73 Answer: A Diff: 3 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution.

98) In a busy gas station, an average of 2.4 cars arrive every 5 minutes. What is the average time between arrivals? A) 0.42 minutes B) 2.08 minutes C) 5.24 minutes D) 2.40 minutes E) 5.00 minutes Answer: B Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 99) In a busy gas station, an average of 2.4 cars arrive every minute. What is the average time between arrivals? A) 0.42 minutes B) 2.08 minutes C) 5.24 minutes D) 2.40 minutes E) 5.00 minutes Answer: A Diff: 1 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. 100) If x is uniformly distributed over the interval a2 to b2 (x ~ [a2, b2]), the mean value of x is . A) (a + b)/2 B) (b - a)/2 C) (a2 + b2)/2 D) (b2 - a2)/2 E) b2 - a2 Answer: C Diff: 1 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. Bloom's level: Knowledge

101) If x is uniformly distributed over the interval a2 to b2 (x ~ [a2, b2]), and a2< a < b < b2, the probability that x is between a and b, P(a ≤ x ≤ b), is . A) 1/(a + b) B) (a + b)/(a2 + b2) C) (a2 + b2)/(a + b) D) 1/(b2 - a2)/ E) b2 - a2 Answer: A Diff: 2 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. Bloom's level: Knowledge 102) If x is uniformly distributed over the interval a2 to b2 (x ~ [a2, b2]), and a < a2 < b < b2, the probability that x is between a and b, P(a ≤ x ≤ b), is . A) (b - a)/(b2 - a2) B) (a + b)/(a2 + b2) C) (a + b)/( b2 - a2) D) (b - a2)/(b2 - a2) E) (b2 - a2)/(a2 + b2) Answer: D Diff: 3 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. Bloom's level: Knowledge 103) If x is uniformly distributed over some interval. The mean value (μ) of x is 2 and its variance (σ2) is 1/3. The probability that x is between 2 and 2.5, P(2 ≤ x ≤ 2.5), is . A) 0.45 B) 0.40 C) 0.35 D) 0.33 E) 0.25 Answer: E Diff: 3 Response: See section 6.1 The Uniform Distribution Learning Objective: 6.1: Solve for probabilities in a continuous uniform distribution. Bloom's level: Application

104) If variable x is normally distributed with mean 0 and standard deviation 1, x ~ N(0,1), then the probability that x is exactly 0 is . A) 0.05 B) 0.04 C) 0.02 D) 0.01 E) 0.00 Answer: E Diff: 1 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. Bloom's level: Application 105) Most graduate business schools require applicants to take the GMAT. Scores on this test are approximately normally distributed with a mean of 545 points and a standard deviation of 100 points. What score do you need to be in the top 5% (approximately the scores needed for top schools)? A) 718 B) 716 C) 714 D) 712 E) 709 Answer: E Diff: 2 Response: See section 6.2 The Normal Distribution Learning Objective: 6.2: Solve for probabilities in a normal distribution using z scores and solve for the mean, the standard deviation, or a value of x in a normal distribution when given information about the area under the normal curve. Bloom's level: Application

106) A recent market study has determined that the probability that a young adult would be willing to try a new online financial service that your company is offering is 50%. In a random sample of 10 young adults, the probability that exactly 5 will be willing to try this new service is , and the approximate probability (using the normal distribution is) . A) 0.251; 0.246 B) 0.246; 0.242 C) 0.246; 0.248 D) 0.249; 0.251 E) 0.248; 0.246 Answer: C Diff: 3 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. Bloom's level: Application 107) A recent market study has determined that the probability that a young adult would be willing to try a new online financial service that your company is offering is 50%. In a random sample of 10 young adults, the approximate probability that at least 2 but no more than 3 will be willing to try this new service is . A) 0.1560 B) 0.1612 C) 0.1775 D) 0.1812 E) 0.1975 Answer: A Diff: 3 Response: See section 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems Learning Objective: 6.3: Solve problems from the discrete binomial distribution using the continuous normal distribution and correcting for continuity. Bloom's level: Application 108) Employees arrive at a cafeteria according to a Poisson process at an average rate of 30 employees per hour. The average waiting time between employees' arrivals is minutes. A) 0.03 B) 0.05 C) 0.5 D) 1 E) 2 Answer: E Diff: 1 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. Bloom's level: Application 33

109) Employees arrive at a cafeteria according to a Poisson process at an average rate of 30 employees per hour. The probability that after one employee arrives, the next one will arrive at least 3 minutes later is . A) 0.223 B) 0.202 C) 0.183 D) 0.162 E) 0.143 Answer: A Diff: 2 Response: See section 6.4 Exponential Distribution Learning Objective: 6.4: Solve for probabilities in an exponential distribution and contrast the exponential distribution to the discrete Poisson distribution. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 7 Sampling and Sampling Distributions 1) Saving time and money are reasons to take a sample rather than do a census. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 2) In some situations, sampling may be the only option because the population is inaccessible. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 3) A population list, map, directory, or other source used to represent the population and from which a sample is taken, is called a frame. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 4) In a random sampling technique, every unit of the population has a randomly varying chance or probability of being included in the sample. Answer: FALSE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 5) Cluster (or area) sampling is a type of random sampling technique. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 6) Systematic sampling is a type of random sampling technique. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 1

7) A major limitation of nonrandom samples is that they are not appropriate for most statistical methods. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 8) The directory or map from which a sample is taken is called the census. Answer: FALSE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 9) The two major categories of sampling methods are proportionate and disproportionate sampling. Answer: FALSE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 10) If every unit of the population has the same probability of being selected for the sample, then the researcher is probably conducting random sampling. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 11) With cluster sampling, there is homogeneity within a subgroup or stratum. Answer: FALSE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 12) If a researcher selects every kth item from a population of N items, then she is likely conducting a systematic random sampling. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each.

13) If a researcher wants to study all students in their university and includes only those students in the researcher's class, then the researcher is conducting convenience sampling. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 14) A nonrandom sampling technique that is similar to stratified random sampling is called quota sampling. Answer: TRUE Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 15) Sampling errors cannot by determined objectively for nonrandom sampling techniques. Answer: TRUE Diff: 2 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 16) A sampling distribution is the distribution of a sample statistic such as the sample mean or sample proportion. Answer: TRUE Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 17) The standard deviation of a sampling distribution of the sample means is commonly called the standard error of the mean. Answer: TRUE Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 18) The central limit theorem states that if the sample size, n, is large enough (n ≥ 20), the distribution of the sample means is normally distributed regardless of the shape of the population. Answer: FALSE Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 3

19) Increasing the sample size causes the numerical value of standard error of the sample means to increase. Answer: FALSE Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 20) The mean of the sample means is the same as the mean of the population. Answer: TRUE Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 21) If the population is normally distributed, the sample means of size n=5 are normally distributed. Answer: TRUE Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 22) The sampling distribution of the sample means is close to the normal distribution only if the distribution of the population is close to normal. Answer: FALSE Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 23) The sampling distribution of the sample means is less variable than the population distribution. Answer: TRUE Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 24) The sampling distribution of close to normal provided that n ≥ 30. Answer: FALSE Diff: 1 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 4

25) The sampling distribution of has a mean equal to the square root of the population proportion p. Answer: FALSE Diff: 1 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 26) Suppose 90% of students in some specific college have a computer at home and a sample of 40 students is taken. The probability that more than 30 of those in the sample have a computer at home can be approximated using the normal approximation. Answer: FALSE Diff: 3 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 27) Paige DeMarco is the Vice President for University Advancement at State University. She is responsible for the capital campaign to raise money for the new student services building. Paige selects the first 100 alumni listed on a web-based social networking site for State University. She intends to contact these individuals regarding possible donations. Her sample is a . A) simple random sample B) stratified sample C) systematic sample D) convenience sample E) cluster sample Answer: D Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each.

28) Paige DeMarco is the Vice President for University Advancement at State University. She is responsible for the capital campaign to raise money for the new student services building. Paige plans to target alumni and acquires her sampling frame from the State University Office of Alumni Relations. She intends to contact these individuals regarding possible donations. She randomly selects the sixth name as a starting point and then selects every 100th subsequent name (106, 206, 306, etc.). Her sample is a . A) simple random sample B) stratified sample C) systematic sample D) convenience sample E) cluster sample Answer: C Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 29) Paige DeMarco is the Vice President for University Advancement at State University. She is responsible for the capital campaign to raise money for the new student services building. Paige plans to target alumni and acquires her sampling frame from the State University Office of Alumni Relations. She intends to contact these individuals regarding possible donations. Paige chooses her sample by selecting six-digit numbers (1 to 150,000) from a random number table. Her sample is a . A) simple random sample B) stratified sample C) systematic sample D) convenience sample E) cluster sample Answer: A Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each.

30) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system. She knows that 2,500 payroll vouchers have been issued since January 1, 2021, and her staff doesn't have time to inspect each voucher. So, she orders her staff to inspect the last 200 vouchers. Her sample is a . A) stratified sample B) simple random sample C) convenience sample D) systematic sample E) cluster sample Answer: C Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 31) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system. She knows that 2,500 payroll vouchers have been issued since January 1, 2021, and her staff doesn't have time to inspect each voucher. So, she randomly selects 53 as a starting point and orders her staff to inspect the 53rd voucher and each voucher at an increment of 100 (53, 153, 253, etc.). Her sample is a . A) stratified sample B) simple random sample C) convenience sample D) cluster sample E) systematic sample Answer: E Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 32) Financial analyst Larry Potts needs a sample of 100 securities listed on the New York Stock Exchange. The current issue of the Wall Street Journal has an alphabetical list of the 2,531 securities traded on the previous business day. Larry uses a table of random numbers to select 100 numbers between 1 and 2,531. His sample is a . A) quota sample B) simple random sample C) systematic sample D) stratified sample E) cluster sample Answer: B Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each.

33) Financial analyst Larry Potts needs a sample of 100 securities listed on the New York Stock Exchange. The current issue of the Wall Street Journal has an alphabetical list of the 2,531 securities traded on the previous business day. Larry randomly selects the 7th security as a starting point, and selects every 25th security thereafter (7, 32, 57, etc.). His sample is a . A) quota sample B) simple random sample C) stratified sample D) systematic sample E) cluster sample Answer: D Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 34) Financial analyst Larry Potts needs a sample of 100 securities listed on either the New York Stock Exchange (NYSE) or the American Stock Exchange (AMEX). According to the Wall Street Journal's "Stock Market Data Bank," 2,531 NYSE securities and 746 AMEX securities were traded on the previous business day. Larry directs his staff to randomly select 77 NYSE and 23 AMEX securities. His sample is a . A) disproportionate systematic sample B) disproportionate stratified sample C) proportionate stratified sample D) proportionate systematic sample E) proportionate cluster sampling Answer: C Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 35) On Saturdays, cars arrive at David Zebda's Scrub and Shine Car Wash at the rate of 80 cars per hour during the ten-hour shift. David wants a sample of 40 Saturday customers to answer the long version of his quality service questionnaire. He instructs the Saturday crew to select the first 40 customers. His sample is a . A) convenience sample B) simple random sample C) systematic sample D) stratified sample E) cluster sample Answer: A Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 8

36) On Saturdays, cars arrive at David Zebda's Scrub and Shine Car Wash at the rate of 80 cars per hour during the ten-hour shift. David wants a sample of 40 Saturday customers to answer the long version of his quality service questionnaire. He randomly selects 9 as a starting point and instructs the crew to select the 9th customer and every 20th customer thereafter (9, 29, 49, etc.). His sample is a . A) convenience sample B) simple random sample C) unsystematic sample D) stratified sample E) systematic sample Answer: E Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 37) Albert Abbasi, VP of Operations at Ingleside International Bank, is evaluating the service level provided to walk-in customers. Accordingly, he plans a sample of waiting times for walkin customers. Albert instructs his staff to record the waiting times for the first 45 walk-in customers arriving after the noon hour. Albert's sample is a . A) simple random sample B) systematic sample C) convenience sample D) stratified sample E) cluster sample Answer: C Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 38) Albert Abbasi, VP of Operations at Ingleside International Bank, is evaluating the service level provided to walk-in customers. Accordingly, he plans a sample of waiting times for walkin customers. Albert randomly selects 4 as a starting point and instructs his staff to record the waiting times for the 4th walk-in customer and every 10th customer thereafter (4, 14, 24, etc.). Albert's sample is a . A) simple random sample B) cluster sample C) convenience sample D) stratified sample E) systematic sample Answer: E Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 9

39) A carload of various palletized aluminum castings has arrived at Mansfield Motor Manufacturers. The car contains 1,000 pallets of 100 castings each. Mario Munoz, Manager of Quality Assurance, directs the receiving crew to deliver the 127th and 869th pallets to his crew for 100% inspection. Mario randomly selected 127 and 869 from a table of random numbers. Mario's sample of 200 castings is a . A) simple random sample B) systematic sample C) stratified sample D) cluster sample E) convenience sample Answer: D Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 40) A carload of steel rods has arrived at Cybermatic Construction Company. The car contains 1,000 bundles of 50 rods of various sizes. Claude Ong, Manager of Quality Assurance, directs the receiving crew to deliver the 63rd and 458th bundles to his crew for 100% inspection. Claude randomly selected 63 and 458 from a table of random numbers. Claude's sample of 100 rods is a . A) cluster sample B) simple random sample C) quota sample D) systematic sample E) stratified sample Answer: A Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 41) Abel Alonzo, Director of Human Resources, is exploring employee absenteeism at the Harrison Haulers Plant. Abel knows that absenteeism varies significantly between departments. For example, workers in the wood shop are absent more than those in the tuning department and the size of the departments ranges from 40 to 120 workers. He orders a random sample of 10 workers from each of the six departments. Abel's sample is a . A) proportionate systematic sample B) proportionate stratified sample C) disproportionate systematic sample D) disproportionate stratified sample E) proportionate cluster sample Answer: D Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 10

42) Abel Alonzo, Director of Human Resources, is exploring employee absenteeism at the Harrison Haulers Plant. Abel knows that absenteeism varies significantly between departments. For example, workers in the wood shop are absent more than those in the tuning department and the size of the departments ranges from 40 to 120 workers. He orders a random sample of 10% of the workers from each of the six departments. Abel's sample is a . A) proportionate systematic sample B) proportionate stratified sample C) disproportionate systematic sample D) disproportionate stratified sample E) proportionate cluster sample Answer: B Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 43) Catherine Chao, Director of Marketing Research, needs a sample of households to participate in the testing of a new toothpaste package. She chooses thirty-six of her closest friends. Catherine's sample is a . A) cluster sample B) convenience sample C) quota sample D) systematic sample E) random sample Answer: B Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. 44) Catherine Chao, Director of Marketing Research, needs a sample of households to participate in the testing of a new toothpaste package. She directs the seven members of her staff to find five households each. Catherine's sample is a . A) cluster sample B) proportionate stratified sample C) quota sample D) disproportionate stratified sample E) simple random sample Answer: C Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each.

45) According to the central limit theorem, if a sample of size 100 is drawn from a population with a mean of 80, the mean of all sample means would equal . A) 0.80 B) 8 C) 80 D) 100 E) 120 Answer: C Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 46) According to the central limit theorem, if a sample of size 56 is drawn from a population with a mean of 16, the mean of all sample means would equal . A) 56 B) 16 C) 7.5 D) 44.0 E) 196 Answer: B Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 47) According to the central limit theorem, if a sample of size 56 is drawn from a population with a standard deviation of 14, the standard deviation of the distribution of the sample means would equal . A) 14 B) 1.87 C) 3.5 D) 0.25 E) 3.74 Answer: B Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary.

48) According to the central limit theorem, if a sample of size 100 is drawn from a population with a standard deviation of 80, the standard deviation of sample means would equal . A) 0.80 B) 8 C) 80 D) 800 E) 0.080 Answer: B Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 49) According to the central limit theorem, if a sample of size 64 is drawn from a population with a standard deviation of 80, the standard deviation of sample means would equal A) 10.000 B) 1.250 C) 0.125 D) 0.800 E) 0.080 Answer: A Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 50) Decreasing the sample size causes the sampling distribution of x̄ to . A) shift to the right B) shift to the left C) have more dispersion D) have less dispersion E) stay unchanged Answer: C Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary.

51) According to the central limit theorem, for samples of size 64 drawn from a population with μ = 800 and σ = 56, the mean of the sampling distribution of sample means would equal . A) 7 B) 8 C) 100 D) 800 E) 80 Answer: D Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 52) According to the central limit theorem, for samples of size 64 drawn from a population with μ = 800 and σ = 56, the standard deviation of the sampling distribution of sample means would equal . A) 7 B) 8 C) 100 D) 800 E) 80 Answer: A Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 53) According to the central limit theorem, for samples of size 169 drawn from a population with μ = 1,014 and σ = 65, the mean of the sampling distribution of sample means would equal . A) 1,014 B) 65 C) 5 D) 6 E) 3 Answer: A Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary.

54) According to the central limit theorem, for samples of size 169 drawn from a population with μ = 1,014 and σ = 65, the standard deviation of the sampling distribution of sample means would equal . A) 1,014 B) 65 C) 15 D) 6 E) 5 Answer: E Diff: 1 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 55) Suppose the population of all public Universities shows the annual parking fee per student is normally distributed with a mean of $110 with a standard deviation of $18. If a random sample of size 49 is drawn from the population, the probability of drawing a sample with a mean of more than $115 is closest to . A) 0.9738 B) 0.4738 C) 0.0259 D) 0.6103 E) 0.1103 Answer: C Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 56) Suppose the population of all public Universities shows the annual parking fee per student is normally distributed with a mean of $110 with a standard deviation of $18. If a random sample of size 49 is drawn from the population, the probability of drawing a sample with a mean of less than $92 is closest to . A) 0.3400 B) 0.1600 C) 0.0000 D) 1.0000 E) 0.7000 Answer: C Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary.

57) Suppose the population of all public Universities shows the annual parking fee per student is normally distributed with a mean of $110 with a standard deviation of $18. If a random sample of size 49 is drawn from the population, the probability of drawing a sample with a sample mean between $100 and $115 is closest to . A) 0.9740 B) 0.4738 C) 0.0262 D) 0.6103 E) 0.1103 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 58) Suppose the population of all public Universities shows the annual parking fee per student is normally distributed with a mean of $110 with a standard deviation of $18. If a random sample of size 49 is drawn from the population, the probability of drawing a sample with a sample mean between $112 and $115 is closest to . A) 0.9738 B) 0.7777 C) 0.7823 D) 0.1924 E) 1.7561 Answer: D Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 59) Suppose a population has a mean of 400 and a standard deviation of 24. If a random sample of size 144 is drawn from the population, the probability of drawing a sample with a mean of more than 404.5 is closest to . A) 0.0139 B) 0.4861 C) 0.4878 D) 0.0122 E) 0.5000 Answer: D Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary.

60) Suppose a population has a mean of 400 and a standard deviation of 24. If a random sample of size 144 is drawn from the population, the probability of drawing a sample with a mean between 395.5 and 404.5 is closest to . A) 0.9756 B) 0.0244 C) 0.0278 D) 0.9722 E) 1.0000 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 61) Suppose a population has a mean of 400 and a standard deviation of 24. If a random sample of size 144 is drawn from the population, the probability of drawing a sample with a mean less than 402 is closest to . A) 0.3413 B) 0.6826 C) 0.8413 D) 0.1587 E) 0.9875 Answer: C Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 62) Suppose a population has a mean of 450 and a variance of 900. If a random sample of size 100 is drawn from the population, the probability that the sample mean is between 448 and 453 is closest to . A) 0.4972 B) 0.6826 C) 0.4101 D) 0.5889 E) 0.9878 Answer: D Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary.

63) Suppose a population has a mean of 870 and a variance of 1,600. If a random sample of size 64 is drawn from the population, the probability that the sample mean is between 860 and 875 is closest to . A) 0.9544 B) 0.6826 C) 0.8785 D) 0.5899 E) 0.8186 Answer: E Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 64) Suppose a population has a mean of 870 and a variance of 8,100. If a random sample of size 36 is drawn from the population, the probability that the sample mean is between 840 and 900 is closest to . A) 0.9545 B) 0.6826 C) 0.8185 D) 0.5899 E) 0.0897 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 65) Albert Abbasi, VP of Operations at Ingleside International Bank, is evaluating the service level provided to walk-in customers. Accordingly, he plans a sample of waiting times for walkin customers. If the population of waiting times has a mean of 15 minutes and a standard deviation of 4 minutes, the probability that Albert's sample of 64 will have a mean less than 14 minutes is closest to . A) 0.4772 B) 0.0228 C) 0.9772 D) 0.9544 E) 1.0000 Answer: B Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary.

66) Albert Abbasi, VP of Operations at Ingleside International Bank, is evaluating the service level provided to walk-in customers. Accordingly, he plans a sample of waiting times for walkin customers. If the population of waiting times has a mean of 15 minutes and a standard deviation of 4 minutes, the probability that Albert's sample of 64 will have a mean less than 16 minutes is closest to . A) 0.4772 B) 0.0228 C) 0.9072 D) 0.9544 E) 0.9773 Answer: E Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 67) Albert Abbasi, VP of Operations at Ingleside International Bank, is evaluating the service level provided to walk-in customers. Accordingly, he plans a sample of waiting times for walkin customers. If the population of waiting times has a mean of 15 minutes and a standard deviation of 4 minutes, the probability that Albert's sample of 64 will have a mean less than 15 minutes is . A) 0.5000 B) 0.0228 C) 0.9072 D) 0.9544 E) 1.0000 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 68) Albert Abbasi, VP of Operations at Ingleside International Bank, is evaluating the service level provided to walk-in customers. Accordingly, he plans a sample of waiting times for walkin customers. If the population of waiting times has a mean of 15 minutes and a standard deviation of 4 minutes, the probability that Albert's sample of 64 will have a mean between 13.5 and 16.5 minutes is closest to . A) 0.9973 B) 0.4987 C) 0.9772 D) 0.4772 E) 0.5000 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 19

69) A carload of steel rods has arrived at Cybermatic Construction Company. The car contains 50,000 rods. Claude Ong, Manager of Quality Assurance, directs his crew to measure the lengths of 100 randomly selected rods. If the population of rods has a mean length of 120 inches and a standard deviation of 0.05 inch, the probability that Claude's sample has a mean greater than 120.0125 inches is . A) 0.0124 B) 0.0062 C) 0.4938 D) 0.9752 E) 1.0000 Answer: B Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 70) A carload of steel rods has arrived at Cybermatic Construction Company. The car contains 50,000 rods. Claude Ong, manager of Quality Assurance, directs his crew to measure the lengths of 100 randomly selected rods. If the population of rods have a mean length of 120 inches and a standard deviation of 0.05 inch, the probability that Claude's sample has a mean less than 119.985 inches is closest to . A) 0.9974 B) 0.0026 C) 0.4987 D) 0.0013 E) 0.0030 Answer: D Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary.

71) A carload of steel rods has arrived at Cybermatic Construction Company. The car contains 50,000 rods. Claude Ong, manager of Quality Assurance, directs his crew to measure the lengths of 100 randomly selected rods. If the population of rods has a mean length of 120 inches and a standard deviation of 0.05 inch, the probability that Claude's sample has a mean between 119.985 and 120.0125 inches is closest to . A) 0.9924 B) 0.9974 C) 0.9876 D) 0.9544 E) 0.9044 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 72) A random sample of size 100 is drawn from a population with a standard deviation of 10. If only 5% of the time a sample mean greater than 20 is obtained, the mean of the population is closest to . A) 18.35 B) 16.25 C) 17.2 D) 20 E) 19 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. 73) Suppose 40% of the population of pre-teens have a TV in their bedroom. If a random sample of 500 pre-teens is drawn from the population, then the probability that 44% or fewer of the preteens have a TV in their bedroom is closest to . A) 0.9661 B) 0.4644 C) 0.0336 D) 0.0400 E) 0.9600 Answer: A Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions.

74) Suppose 40% of the population of pre-teens have a TV in their bedroom. If a random sample of 500 pre-teens is drawn from the population, then the probability that 44% or more of the preteens have a TV in their bedroom is closest to . A) 0.9664 B) 0.4644 C) 0.0339 D) 0.0400 E) 0.9600 Answer: C Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 75) Suppose 40% of the population of pre-teens have a TV in their bedroom. If a random sample of 500 pre-teens is drawn from the population, then the probability that between 36% and 44% of the pre-teens have a TV in their bedroom is closest to . A) 0.9664 B) 0.4644 C) 0.0336 D) 0.9321 E) 0.0712 Answer: D Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 76) Suppose 30% of the U.S. population has green eyes. If a random sample of size 1200 U.S. citizens is drawn, then the probability that less than 348 of those U.S. citizens have green eyes is closest to . A) 0.2248 B) 0.2764 C) 0.2900 D) 0.7764 E) 0.3336 Answer: A Diff: 3 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions.

77) If the population proportion is 0.90 and a sample of size 64 is taken, which is closest to the probability that the sample proportion is less than 0.88? A) 0.2019 B) 0.2969 C) 0.5300 D) 0.7019 E) 0.7899 Answer: B Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 78) If the population proportion is 0.90 and a sample of size 64 is taken, which is closest to the probability that the sample proportion is more than 0.89? A) 0.1064 B) 0.2700 C) 0.3936 D) 0.6051 E) 0.9000 Answer: D Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 79) Suppose 40% of all college students have a computer at home and a sample of 64 is taken. Which is closest to the probability that more than 30 of those in the sample have a computer at home? A) 0.3686 B) 0.1308 C) 0.8686 D) 0.6314 E) 0.1343 Answer: B Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions.

80) Suppose 40% of all college students have a computer at home and a sample of 100 is taken. Which is closest to the probability that more than 50 of those in the sample have a computer at home? A) 0.4793 B) 0.9793 C) 0.0206 D) 0.5207 E) 0.6754 Answer: C Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 81) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system. If 10% of the 5,000 payroll vouchers issued since January 1, 2021, have irregularities, the probability that Pinky's random sample of 200 vouchers will have a sample proportion greater than .06 is closest to . A) 0.4706 B) 0.9703 C) 0.0588 D) 0.9412 E) 0.9876 Answer: B Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 82) Pinky Bauer, Chief Financial Officer of Harrison Haulers, Inc., suspects irregularities in the payroll system. If 10% of the 5,000 payroll vouchers issued since January 1, 2021, have irregularities, the probability that Pinky's random sample of 200 vouchers will have a sample proportion of between .06 and .14 is closest to . A) 0.4706 B) 0.9706 C) 0.0588 D) 0.9407 E) 0.8765 Answer: D Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions.

83) Catherine Chao, Director of Marketing Research, needs a sample of Kansas City households to participate in the testing of a new toothpaste package. If 40% of the households in Kansas City prefer the new package, the probability that Catherine's random sample of 300 households will have a sample proportion greater than 0.45 is closest to . A) 0.9232 B) 0.0768 C) 0.4616 D) 0.0385 E) 0.8974 Answer: D Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 84) Catherine Chao, Director of Marketing Research, needs a sample of Kansas City households to participate in the testing of a new toothpaste package. If 40% of the households in Kansas City prefer the new package, the probability that Catherine's random sample of 300 households will have a sample proportion between 0.35 and 0.45 is closest to . A) 0.9229 B) 0.0768 C) 0.4616 D) 0.0384 E) 0.8976 Answer: A Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 85) In an instant lottery, your chance of winning is 0.1. If you play the lottery 100 times and outcomes are independent, the probability that you win at least 15 percent of the time is closest to . A) 0.4933 B) 0.5 C) .15 D) 0.0478 E) 0.9213 Answer: D Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions.

86) Suppose 65% of all college students have a laptop computer at home and a sample of 150 students is taken. The mean of the sampling distribution of is . A) 0.65 B) 6.5 C) 97.5 D) 0.975 E) 15.0 Answer: A Diff: 1 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 87) Suppose 65% of all college students have a laptop computer at home and a sample of 150 is taken. The standard deviation of the sampling distribution of is . A) 0.0015 B) 0.0389 C) 0.6500 D) 0.4769 E) 0.0477 Answer: B Diff: 1 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. 88) Alice Zhong is the VP of Operations at Pearl Financial Services. She wants to measure customer satisfaction after a new website and other changes were introduced a few months ago. For this purpose, she instructs her staff to prepare a questionnaire and send it to any IP address that accesses the company's website from 8 a.m. to 9 a.m. during each day of the next four weeks. This is an example of . A) simple random sample B) systematic sample C) convenience sample D) stratified sample E) cluster sample Answer: E Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. Bloom's level: Application

89) Alice Zhong is the VP of Operations at Pearl Financial Services. She wants to measure customer satisfaction after a new website and other changes were introduced a few months ago. For this purpose, she instructs her staff to prepare a questionnaire and send it to the first 200 clients in their alphabetical list of active clients. This is an example of . A) simple random sample B) systematic sample C) convenience sample D) stratified sample E) cluster sample Answer: C Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. Bloom's level: Application 90) Alice Zhong is the VP of Operations at Pearl Financial Services. She wants to measure customer satisfaction after a new website and other changes were introduced a few months ago. For this purpose, she uses random five-digits numbers from the website random.org (which offers true random numbers on the Internet). This is an example of . A) simple random sample B) systematic sample C) convenience sample D) stratified sample E) cluster sample Answer: A Diff: 1 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. Bloom's level: Application

91) Alice Zhong is the VP of Operations at Pearl Financial Services. She wants to measure customer satisfaction after a new website and other changes were introduced a few months ago. 28% of clients are from the healthcare industry, 35% are manufacturing companies, 27% are financial firms, and 7% are construction companies. For this purpose, she gets random five-digits numbers from the website random.org (which offers true random numbers on the Internet). Then she uses these numbers to select 140 random clients from the healthcare sector, 175 from the manufacturing sector, 135 from the manufacturing sector, and 35 from the construction sector. This is an example of . A) disproportionate systematic sample B) disproportionate stratified sample C) proportionate stratified sample D) proportionate systematic sample E) proportionate cluster sampling Answer: C Diff: 2 Response: See section 7.1 Sampling Learning Objective: 7.1: Contrast sampling to census and differentiate among different methods of sampling by assessing the advantages associated with each. Bloom's level: Application 92) If random variable X is distributed according to a uniform distribution between 0 and 1 (X ~ U[0, 1]), and you take a random sample of size 35, what is the probability that the sample mean will fall between 0.48 and 0.55? A) 0.98 B) 0.51 C) 0.76 D) 0.27 E) 0.07 Answer: B Diff: 3 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. Bloom's level: Application

93) If random variable X is distributed according to a uniform distribution between 0 and 1 (X ~ U[0, 1]), and 100 random samples of sizes 35-40 were taken, the sum of the sample means would be . A) 50.0 B) 37.5 C) 0.50 D) 0.38 E) 0.005 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. Bloom's level: Application 94) If random variable X is distributed according to a uniform distribution between 0 and 1 (X ~ U[0, 1]), and 100 random samples of sizes 35-40 were taken, the standard deviation of the sample means would be . A) undetermined; there is not enough information to answer B) between 0.0142 and 0.0151 C) between 0.0456 and 0.0488 D) between 0.0122 and 0.0131 E) between 0.0466 and 0.0498 Answer: C Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. Bloom's level: Application 95) The employees in certain division of Cybertronics Inc. need to complete a certification online. On average, it takes 20 hours to complete the coursework and successfully pass all tests, with a standard deviation of 6 hours. If you select a random sample of size 30, the probability that the employees in your sample have taken, on average, more than 20.5 hours is . A) 0.47 B) 0.45 C) 0.41 D) 0.32 E) 0.01 Answer: D Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. Bloom's level: Application

96) Certain transportation company has a fleet of 210 vehicles. The average age of the vehicles is 4.25 years, with a standard deviation of 18 months. In a random sample of 40 vehicles, what is the probability that the average age of vehicles in the sample will be less than 4 years? A) 0.146 B) 0.163 C) 0.180 D) 0.197 E) 0.214 Answer: A Diff: 2 Response: See section 7.2 Sampling Distribution of x̄ Learning Objective: 7.2: Describe the distribution of a sample's mean using the central limit theorem, correcting for a finite population if necessary. Bloom's level: Application 97) In Norway, approximately 22% of vehicles are electric vehicles. Which is closest to the probability of randomly selecting 250 cars and finding out that 60 or more are electric? A) 0.233 B) 0.223 C) 0.213 D) 0.203 E) 0.193 Answer: B Diff: 2 Response: See section 7.3 Sampling Distribution of p Learning Objective: 7.3: Describe the distribution of a sample's proportion using the z formula for sample proportions. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 8 Statistical Inference: Estimation for Single Populations 1) When a statistic calculated from sample data is used to estimate a population parameter, it is called a point estimate. Answer: TRUE Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 2) When a range of values is used to estimate a population parameter, it is called a range estimate. Answer: FALSE Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 3) If the population is not normal but its standard deviation, σ is known and the sample size, n is large (n ≥ 30), z-distribution values may be used to determine interval estimates for the population mean. Answer: TRUE Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 4) If the population is normal and its standard deviation, σ, is known but the sample size is small, z-distribution values may not be used to determine interval estimates for the population mean. Answer: FALSE Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 5) If the population is normal and its standard deviation, σ, is known and the sample size, n, is large (n ≥ 30), interval estimates for the population mean must be determined using z-values. Answer: TRUE Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary.

6) Suppose a random sample of 16 is selected from a population with a normal distribution with a known population standard deviation σ of 10. Assume that the sample mean is 4.2. Based on a 90% confidence interval for the population mean, we can conclude that 0.1 is a plausible number for the population mean μ. Answer: TRUE Diff: 3 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 7) You are thinking of using a t-table to find a 95 percent confidence interval for the mean μ of a population. The distribution of the population is normal and the population standard deviation is unknown. A random sample of size n is drawn from this population. You may use the tdistribution only if the sample size n is small. Answer: TRUE Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 8) When the population standard deviation, σ, is unknown the sample standard deviation, s, is used in determining the interval estimate for the population mean. Answer: TRUE Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 9) An assumption underlying the use of t-statistic in sample-based estimation is that the population is normally distributed. Answer: TRUE Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 10) A t-distribution is similar to a normal distribution, but with fatter tails. Answer: FALSE Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution.

11) In order to find values in the t distribution table, you must determine the appropriate degrees of freedom based on the sample sizes. Answer: TRUE Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 12) If the degrees of freedom in a t distribution increase, the difference between the t values and the z values will also increase. Answer: FALSE Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 13) In determining the interval estimates for a population proportion using the sample proportion, it is appropriate to use the z-distribution. Answer: TRUE Diff: 1 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 14) A market researcher computed a confidence interval for a population proportion using a 95% confidence level. Her boss decided that she wanted a 99% confidence level instead. The new interval with 99% confidence level will be wider than the original one with a 95% confidence level. Answer: TRUE Diff: 1 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 15) In determining the interval estimates for a population variance using the sample variance, it is appropriate to use the values from a chi-square distribution rather than a t-distribution. Answer: TRUE Diff: 1 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 16) Use of the chi-square statistic to estimate the population variance is extremely robust to the assumption that the population is normally distributed. Answer: FALSE Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 3

17) Like a t-distribution, a chi-square distribution is symmetrical and extends from minus infinity to plus infinity. Answer: FALSE Diff: 1 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 18) In estimating the sample size necessary to estimate a population mean, the error of estimation, E, is equal to the difference between the sample mean and the sample standard deviation. Answer: FALSE Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 19) Given the error we are willing to tolerate, the sample size is determined by the mean, µ of the population and the confidence level. Answer: FALSE Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 20) Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of a new toothpaste package. Her staff reports that 17% of a random sample of 200 households prefers the new package to all other package designs. If Catherine concludes that 17% of all households prefer the new package, she is using _ . A) a point estimate B) a range estimate C) a statistical parameter D) an interval estimate E) an exact estimate Answer: A Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary.

21) Brian Vanecek, VP of Operations at Portland Trust Bank, is evaluating the service level provided to walk-in customers. Accordingly, his staff recorded the waiting times for 45 randomly selected walk-in customers, and calculated that their mean waiting time was 15 minutes. If Brian concludes that the average waiting time for all walk-in customers is 15 minutes, he is using a . A) a range estimate B) a statistical parameter C) an interval estimate D) a point estimate E) an exact estimate Answer: D Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 22) Eugene Gates, Marketing Director of Mansfield Motors Manufacturers, Inc.'s Electrical Division, is leading a study to assess the relative importance of product features. An item on a survey questionnaire distributed to 100 of Mansfield's customers asked them to rate the importance of "ease of maintenance" on a scale of 1 to 10 (with 1 meaning "not important" and 10 meaning "highly important"). His staff assembled the following statistics.

Mean Standard Deviation

Ease of Maintenance 7.5 1.5

If Eugene concludes that the average rate of "ease of maintenance" for all customers is 7.5, he is using . A) a range estimate B) a statistical parameter C) a point estimate D) an interval estimate E) a guesstimate Answer: C Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary.

23) The z value associated with a twosided 90% confidence interval is . A) 1.28 B) 1.645 C) 1.96 D) 2.575 E) 2.33 Answer: B Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 24) The z value associated with a twosided 95% confidence interval is . A) 1.28 B) 1.645 C) 1.96 D) 2.575 E) 2.33 Answer: C Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 25) The z value associated with a twosided 80% confidence interval is . A) 1.645 B) 1.28 C) 0.84 D) 0.29 E) 2.00 Answer: B Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 26) The z value associated with a twosided 88% confidence interval is . A) 1.28 B) 1.55 C) 1.17 D) 0.88 E) 1.90 Answer: B Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 6

27) Suppose a researcher is interested in understanding the variation in the price of store brand milk. A random sample of 36 grocery stores is selected from a population and the mean price of store brand milk is calculated. The sample mean is $3.13. Assume that the population standard deviation is $0.23. Construct a 99% confidence interval to estimate the population mean. A) $3.03 to $3.23 B) $3.12 to $3.14 C) $3.05 to $3.21 D) $2.90 to $3.36 E) $3.06 to $3.20 Answer: A Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 28) Suppose a researcher is interested in understanding the variation in the price of store brand milk. A random sample of 36 grocery stores is selected from a population and the mean price of store brand milk is calculated. The sample mean is $3.13. Assume that the population standard deviation is $0.23. Construct a 92% confidence interval to estimate the population mean. A) $3.03 to $3.23 B) $3.12 to $3.14 C) $3.05 to $3.21 D) $2.90 to $3.36 E) $3.06 to $3.20 Answer: E Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 29) Suppose a researcher is interested in understanding the variation in the price of store brand milk. A random sample of 36 grocery stores is selected from a population and the mean price of store brand milk is calculated. The sample mean is $3.13. Assume that the population standard deviation is $0.23. Construct a 95% confidence interval to estimate the population mean. A) $3.03 to $3.23 B) $3.12 to $3.14 C) $3.05 to $3.21 D) $2.90 to $3.36 E) $3.06 to $3.20 Answer: C Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary.

30) Brian Vanecek, VP of Operations at Portland Trust Bank, is evaluating the service level provided to walk-in customers. Accordingly, his staff recorded the waiting times for 64 randomly selected walk-in customers and determined that their mean waiting time was 15 minutes. Assume that the population standard deviation is 4 minutes. The 90% confidence interval for the population mean of waiting times is . A) 14.27 to 15.73 B) 14.18 to 15.82 C) 9.88 to 20.12 D) 13.86 to 16.14 E) 18.12 to 19.87 Answer: B Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 31) Brian Vanecek, VP of Operations at Portland Trust Bank, is evaluating the service level provided to walk-in customers. Accordingly, his staff recorded the waiting times for 64 randomly selected walk-in customers and determined that their mean waiting time was 15 minutes. Assume that the population standard deviation is 4 minutes. The 95% confidence interval for the population mean of waiting times is . A) 14.02 to 15.98 B) 7.16 to 22.84 C) 14.06 to 15.94 D) 8.42 to 21.58 E) 19.80 to 23.65 Answer: A Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 32) James Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. His staff randomly selected personnel files for 100 tellers in the southeast region and determined that their mean training time was 25 hours. Assume that the population standard deviation is 5 hours. The 88% confidence interval for the population mean of training times is . A) 17.25 to 32.75 B) 24.22 to 25.78 C) 24.42 to 25.59 D) 19.15 to 30.85 E) 21.00 t0 32.00 Answer: B Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 8

33) James Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. His staff randomly selected personnel files for 100 tellers in the southeast region and determined that their mean training time was 25 hours. Assume that the population standard deviation is 5 hours. The 92% confidence interval for the population mean of training times is . A) 16.25 to 33.75 B) 24.30 to 25.71 C) 17.95 to 32.05 D) 24.12 to 25.88 E) 24.45 to 27.32 Answer: D Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 34) James Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. His staff randomly selected personnel files for 100 tellers in the southeast region and determined that their mean training time was 25 hours. Assume that the population standard deviation is 5 hours. The 95% confidence interval for the population mean of training times is . A) 15.20 to 34.80 B) 24.18 to 25.82 C) 24.02 to 25.98 D) 16.78 to 33.23 E) 23.32 to 35.46 Answer: C Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 35) A random sample of 64 items is selected from a population of 400 items. The sample mean is 200. The population standard deviation is 48. From these data, the 95% confidence interval to estimate the population mean would be _. A) 189.21 to 210.79 B) 188.24 to 211.76 C) 190.13 to 209.87 D) 190.94 to 209.06 E) 193.45 to 211.09 Answer: B Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 9

36) A random sample of 64 items is selected from a population of 400 items. The sample mean is 200. The population standard deviation is 48. From these data, the 90% confidence interval to estimate the population mean would be _. A) 189.21 to 210.79 B) 188.24 to 211.76 C) 190.13 to 209.87 D) 190.94 to 209.06 E) 193.45 to 211.09 Answer: C Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 37) The z-distribution is used to estimate a population mean for large samples if the population standard deviation is known. "Large" is usually defined as . A) at least 10 B) at least 5% of the population size C) at least 30 D) at least 12 E) at least 100 Answer: C Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. 38) If the standard deviation, σ is known the z-distribution values may not be used to determine interval estimates for the population mean when _ . A) n < 30 B) the distribution is not normal C) the distribution is skewed D) n is big E) n is small (<30) and the distribution is not normal Answer: E Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary.

39) The table t value associated with the upper 5% of the t distribution and 12 degrees of freedom is . A) 2.179 B) 1.782 C) 1.356 D) 3.055 E) 3.330 Answer: B Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 40) The table t value associated with the upper 5% of the t distribution and 14 degrees of freedom is . A) 2.977 B) 2.624 C) 2.145 D) 1.761 E) 1.345 Answer: D Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 41) The table t value associated with the upper 10% of the t distribution and 23 degrees of freedom is . A) 1.319 B) 1.714 C) 2.069 D) 1.321 E) 2.332 Answer: A Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution.

42) A researcher is interested in estimating the mean weight of a semi-tractor truck to determine the potential load capacity. She takes a random sample of 17 trucks and computes a sample mean of 20,000 pounds with sample standard deviation of 1,500. She decides to construct a 98% confidence interval to estimate the mean. The degrees of freedom associated with this problem are . A) 18 B) 17 C) 16 D) 15 E) 20 Answer: C Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 43) A researcher is interested in estimating the mean weight of a semi-tractor truck to determine the potential load capacity. She takes a random sample of 17 trucks and computes a sample mean of 20,000 pounds with sample standard deviation of 1,500. The 95% confidence interval for the population mean weight of a semi-tractor truck is . A) 19,232 to 20,768 B) 19,365 to 20,635 C) 19,229 to 20,771 D) 19,367 to 20,633 E) 18,500 to 21,500 Answer: C Diff: 2 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 44) A researcher is interested in estimating the mean weight of a semi-tractor truck to determine the potential load capacity. She takes a random sample of 17 trucks and computes a sample mean of 20,000 pounds with sample standard deviation of 1,500. The 90% confidence interval for the population mean weight of a semi-tractor truck is . A) 19,365 to 20,635 B) 19,367 to 20,633 C) 19,514 to 20,486 D) 19,515 to 20,485 E) 18,500 to 21,500 Answer: A Diff: 2 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution.

45) The weights of aluminum castings produced by a process are normally distributed. A random sample of 5 castings is selected; the sample mean weight is 2.21 pounds; and the sample standard deviation is 0.12 pound. The 98% confidence interval for the population mean casting weight is . A) 1.76 to 2.66 B) 2.01 to 2.41 C) 2.08 to 2.34 D) 1.93 to 2.49 E) 2.49 to 2.67 Answer: B Diff: 2 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 46) Life tests performed on a sample of 13 batteries of a new model indicated: (1) an average life of 75 months, and (2) a standard deviation of 5 months. Other battery models, produced by similar processes, have normally distributed life spans. The 98% confidence interval for the population mean life of the new model is . A) 63.37 to 86.63 B) 61.60 to 88.41 C) 71.77 to 78.23 D) 71.28 to 78.72 E) 79.86 to 81.28 Answer: D Diff: 2 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. 47) Life tests performed on a sample of 13 batteries of a new model indicated: (1) an average life of 75 months, and (2) a standard deviation of 5 months. Other battery models, produced by similar processes, have normally distributed life spans. The 90% confidence interval for the population mean life of the new model is . A) 66.78 to 83.23 B) 72.72 to 77.28 C) 72.53 to 77.47 D) 66.09 to 83.91 E) 73.34 to 76.25 Answer: C Diff: 2 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution.

48) A researcher wants to estimate the proportion of the population which possesses a given characteristic. A random sample of size 800 is taken resulting in 360 items which possess the characteristic. The point estimate for this population proportion is . A) 0.55 B) 0.45 C) 0.35 D) 0.65 E) 0.70 Answer: B Diff: 1 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 49) A researcher wants to estimate the proportion of the population which possesses a given characteristic. A random sample of size 1800 is taken resulting in 450 items which possess the characteristic. The point estimate for this population proportion is . A) 0.55 B) 0.45 C) 0.35 D) 0.25 E) 0.15 Answer: D Diff: 1 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 50) A large national company is considering negotiating cellular phone rates for its employees. The Human Resource department would like to estimate the proportion of its employee population who own an Apple iPhone. A random sample of size 250 is taken and 40% of the sample own and iPhone. The 90% confidence interval to estimate the population proportion is . A) 0.35 to 0.45 B) 0.34 to 0.46 C) 0.38 to 0.42 D) 0.39 to 0.41 E) 0.40 to 0.45 Answer: A Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic.

51) A large national company is considering negotiating cellular phone rates for its employees. The Human Resource department would like to estimate the proportion of its employee population who own an Apple iPhone. A random sample of size 250 is taken and 40% of the sample own and iPhone. The 95% confidence interval to estimate the population proportion is . A) 0.35 to 0.45 B) 0.34 to 0.46 C) 0.37 to 0.43 D) 0.39 to 0.41 E) 0.40 to 0.42 Answer: B Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 52) A large trucking company wants to estimate the proportion of its tractor truck population with refrigerated carrier capacity. A random sample of 200 tractor trucks is taken and 30% of the sample have refrigerated carrier capacity. The 95% confidence interval to estimate the population proportion is . A) 0.53 to 0.67 B) 0.25 to 0.35 C) 0.24 to 0.36 D) 0.27 to 0.33 E) 0.28 to 0.34 Answer: C Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 53) A large trucking company wants to estimate the proportion of its tracker truck population with refrigerated carrier capacity. A random sample of 200 tracker trucks is taken and 30% of the sample have refrigerated carrier capacity. The 90% confidence interval to estimate the population proportion is . A) 0.53 to 0.67 B) 0.25 to 0.35 C) 0.24 to 0.36 D) 0.27 to 0.33 E) 0.33 to 0.39 Answer: B Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic.

54) A random sample of 225 items from a population results in 60% possessing a given characteristic. Using this information, the researcher constructs a 99% confidence interval to estimate the population proportion. The resulting confidence interval is . A) 0.54 to 0.66 B) 0.59 to 0.61 C) 0.57 to 0.63 D) 0.52 to 0.68 E) 0.68 to 0.76 Answer: D Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 55) A random sample of 225 items from a population results in 60% possessing a given characteristic. Using this information, the researcher constructs a 90% confidence interval to estimate the population proportion. The resulting confidence interval is . A) 0.546 to 0.654 B) 0.536 to 0.664 C) 0.596 to 0.604 D) 0.571 to 0.629 E) 0.629 to 0.687 Answer: A Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 56) Elwin Osbourne, CIO at GFS, Inc., is studying employee use of GFS e-mail for non-business communications. A random sample of 200 e-mail messages was selected. Thirty of the messages were not business related. The point estimate for this population proportion is . A) 0.150 B) 0.300 C) 0.182 D) 0.667 E) 0.786 Answer: A Diff: 1 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic.

57) Elwin Osbourne, CIO at GFS, Inc., is studying employee use of GFS e-mail for non-business communications. A random sample of 200 e-mail messages was selected. Thirty of the messages were not business related. The 90% confidence interval for the population proportion is . A) 0.108 to 0.192 B) 0.153 to 0.247 C) 0.091 to 0.209 D) 0.145 to 0.255 E) 0.255 to 0.265 Answer: A Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 58) Elwin Osbourne, CIO at GFS, Inc., is studying employee use of GFS e-mail for non-business communications. A random sample of 200 e-mail messages was selected. Thirty of the messages were not business related. The 95% confidence interval for the population proportion is . A) 0.108 to 0.192 B) 0.153 to 0.247 C) 0.091 to 0.209 D) 0.101 to 0.199 E) 0.199 to 0.201 Answer: D Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 59) Elwin Osbourne, CIO at GFS, Inc., is studying employee use of GFS e-mail for non-business communications. A random sample of 200 e-mail messages was selected. Thirty of the messages were not business related. The 98% confidence interval for the population proportion is . A) 0.108 to 0.192 B) 0.153 to 0.247 C) 0.091 to 0.209 D) 0.145 to 0.255 E) 0.250 to 0.275 Answer: C Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic.

60) Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of a new toothpaste package. She randomly selects a sample of 200 households. Forty households prefer the new package to all other package designs. The point estimate for this population proportion is . A) 0.20 B) 0.25 C) 0.40 D) 0.45 E) 0.55 Answer: A Diff: 1 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 61) Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of a new toothpaste package. She randomly selects a sample of 200 households. Forty households prefer the new package to all other package designs. The 90% confidence interval for the population proportion is . A) 0.199 to 0.201 B) 0.153 to 0.247 C) 0.164 to 0.236 D) 0.145 to 0.255 E) 0.185 to 0.275 Answer: B Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. 62) Brian Vanecek, VP of Operations at Portland Trust Bank, is evaluating the service level provided to walk-in customers. Brian would like to minimize the variance of waiting time for these customers, since this would mean each customer received the same level of service. Accordingly, his staff recorded the waiting times for 15 randomly selected walk-in customers, and determined that their mean waiting time was 15 minutes and that the standard deviation was 4 minutes. Assume that waiting time is normally distributed. The 90% confidence interval for the population variance of waiting times is . A) 9.46 to 34.09 B) 56.25 to 64.87 C) 11.05 to 16.03 D) 8.58 to 39.79 E) 12.50 to 42.35 Answer: A Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance.

63) Brian Vanecek, VP of Operations at Portland Trust Bank, is evaluating the service level provided to walk-in customers. Brian would like to minimize the variance of waiting time for these customers, since this would mean each customer received the same level of service. Accordingly, his staff recorded the waiting times for 15 randomly selected walk-in customers, and determined that their mean waiting time was 15 minutes and that the standard deviation was 4 minutes. Assume that waiting time is normally distributed. The 95% confidence interval for the population variance of waiting times is . A) 9.46 to 34.09 B) 56.25 to 64.87 C) 11.05 to 16.03 D) 8.58 to 39.80 E) 12.50 to 42.35 Answer: D Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 64) Velma Vasquez, fund manager of the Vasquez Value Fund, manages a portfolio of 250 common stocks. Velma relies on various statistics, such as variance, to assess the overall risk of stocks in an economic sector. Her staff reported that for a sample 14 utility stocks the mean annualized return was 14% and that the variance was 3%. Assume that annualized returns are normally distributed. The 90% confidence interval for the population variance of annualized returns for utility stocks is . A) 0.018 to 0.064 B) 0.016 to 0.078 C) 0.017 to 0.066 D) 0.016 to 0.075 E) 0.020 to 0.080 Answer: C Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance.

65) Velma Vasquez, fund manager of the Vasquez Value Fund, manages a portfolio of 250 common stocks. Velma relies on various statistics, such as variance, to assess the overall risk of stocks in an economic sector. Her staff reported that for a sample 14 utility stocks the mean annualized return was 14% and that the variance was 3%. Assume that annualized returns are normally distributed. The 95% confidence interval for the population variance of annualized returns for utility stocks is . A) 0.018 to 0.064 B) 0.016 to 0.078 C) 0.017 to 0.066 D) 0.016 to 0.075 E) 0.020 to 0.080 Answer: B Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 66) James Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. His staff randomly selected personnel files for 10 tellers in the southwest region, and determined that their mean training time was 25 hours and that the standard deviation was 5 hours. Assume that training times are normally distributed. The 90% confidence interval for the population variance of training times is . A) 11.83 to 83.33 B) 2.37 to 16.67 C) 2.66 to 13.51 D) 13.30 to 67.67 E) 15.00 to 68.00 Answer: D Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance.

67) James Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. His staff randomly selected personnel files for 10 tellers in the southwest region, and determined that their mean training time was 25 hours and that the standard deviation was 5 hours. Assume that training times are normally distributed. The 95% confidence interval for the population variance of training times is . A) 11.83 to 83.32 B) 2.37 to 16.67 C) 2.66 to 13.51 D) 13.30 to 67.57 E) 15.40 to 68.28 Answer: A Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 68) Given n = 17, s2 = 18.56, and that the population is normally distributed, the 80% confidence interval for the population variance is . A) 11.4372 ≤ σ 2 ≤ 36.3848 B) 23.5418 ≤ σ 2 ≤ 9.31223 C) 12.6141 ≤ σ 2 ≤ 31.8892 D) 11.2929 ≤ σ 2 ≤ 37.2989 E) 14.2929 ≤ σ 2 ≤ 39.2989 Answer: C Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 69) Given n = 12, s2 = 44.90, and that the population is normally distributed, the 99% confidence interval for the population variance is . A) 19.0391 ≤ σ 2 ≤ 175.2888 B) 23.0881 ≤ σ 2 ≤ 122.3495 C) 25.6253 ≤ σ 2 ≤ 103.0993 D) 18.4588 ≤ σ 2 ≤ 189.7264 E) 14.2929 ≤ σ 2 ≤ 139.2989 Answer: D Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance.

70) Given n = 20, s = 32, and that the population is normally distributed, the 90% confidence interval for the population variance is . 2 A) 645.45 ≤ σ ≤ 1923.10 B) 599.36 ≤ σ 2 ≤ 2135.39 C) 592.23 ≤ σ 2 ≤ 2184.47 D) 652.01 ≤ σ 2 ≤ 1887.42 E) 642.09 ≤ σ 2 ≤ 3982.30 Answer: A Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 71) Suppose the fat content of a hotdog follows normal distribution. Ten random measurements give a mean of 21.77 and standard deviation of 3.69. The 90% confidence interval for the population variance of fat content of a hotdog is . A) 5.2 to 21.3 B) 7.2 to 36.9 C) 19.63 to 23.91 D) 19.85 to 23.69 E) 2.69 to 5.1 Answer: B Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. 72) A researcher wants to determine the sample size necessary to adequately conduct a study to estimate the population mean to within 5 points. The range of population values is 80 and the researcher plans to use a 90% level of confidence. The sample size should be at least . A) 44 B) 62 C) 216 D) 692 E) 700 Answer: A Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion.

73) A study is going to be conducted in which a population mean will be estimated using a 92% confidence interval. The estimate needs to be within 12 of the actual population mean. The population variance is estimated to be around 2500. The necessary sample size should be at least . A) 15 B) 47 C) 54 D) 638 E) 700 Answer: C Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 74) In estimating the sample size necessary to estimate p, if there is no good approximation for the value of p available, the value of should be used as an estimate of p in the formula. A) 0.10 B) 0.50 C) 0.40 D) 1.96 E) 2.00 Answer: B Diff: 1 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 75) A researcher wants to estimate the percent of the population that uses the radio to stay informed on local news issues. The researcher wants to estimate the population proportion with a 95% level of confidence. He estimates from previous studies that no more than 30% of the population stay informed on local issues through the radio. The researcher wants the estimate to have an error of no more than .03. The necessary sample size is at least . A) 27 B) 188 C) 211 D) 897 E) 900 Answer: D Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion.

76) Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of a new toothpaste package. She plans to use a 95% confidence interval estimate of the proportion of households which prefer the new packages; she will accept a 0.05 error. Previous studies indicate that new packaging has an approximately 70% acceptance rate. The sample size should be at least . A) 27 B) 59 C) 323 D) 427 E) 500 Answer: C Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 77) Elwin Osbourne, CIO at GFS, Inc., is studying employee use of GFS e-mail for non-business communications. He plans to use a 95% confidence interval estimate of the proportion of e-mail messages that are non-business; he will accept a 0.05 error. Previous studies indicate that approximately 30% of employee e-mail is not business related. Elwin should sample e-mail messages. A) 323 B) 12 C) 457 D) 14 E) 100 Answer: A Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 78) Elwin Osbourne, CIO at GFS, Inc., is studying employee use of GFS e-mail for non-business communications. He plans to use a 98% confidence interval estimate of the proportion of e-mail messages that are non-business; he will accept a 0.05 error. Previous studies indicate that approximately 30% of employee e-mail is not business related. Elwin should sample approximately e-mail messages. A) 323 B) 12 C) 455 D) 14 E) 100 Answer: C Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 24

79) A researcher wants to estimate the percent of the population that uses the radio to stay informed on local news issues. The researcher wants to estimate the population proportion with a 90% level of confidence. He estimates from previous studies that no more than 30% of the population stay informed on local issues through the radio. The researcher wants the estimate to have an error of no more than .02. The approximate sample size is at least . A) 29 B) 47 C) 298 D) 1421 E) 1500 Answer: D Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 80) An insurance company is interested in conducting a study to estimate the population proportion of teenagers who obtain a driving permit within 1 year of their 16th birthday. A level of confidence of 99% will be used and an error of no more than .04 is desired. There is no knowledge as to what the population proportion will be. The approximate sample size should be at least . A) 1037 B) 160 C) 41 D) 259 E) 289 Answer: A Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion.

81) A researcher wants to estimate the percent of the population that uses the internet to stay informed on world news issues. The researcher wants to estimate the population proportion with a 98% level of confidence. He estimates from previous studies that at least 65% of the population stay informed on world issues through the internet. He also wants the error to be no more than .03. The approximate sample size should be at least . A) 41 B) 313 C) 1677 D) 1369 E) 1500 Answer: D Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 82) A study is going to be conducted in which the mean of a lifetime of batteries produced by a certain method will be estimated using a 90% confidence interval. The estimate needs to be within +/- 2 hours of the actual population mean. The population standard deviation σ is estimated to be around 25. The necessary sample size should be at least . A) 100 B) 21 C) 923 D) 35 E) 423 Answer: E Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. 83) The z value associated with a two-sided 99% confidence interval is . A) 1.28 B) 1.645 C) 1.96 D) 2.576 E) 2.33 Answer: D Diff: 1 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. Bloom's level: Knowledge

84) The employees of Cybertronics Inc. need to complete a certification online. A random sample of 49 employees gives an average time for completion of all the coursework and passing the tests of 20 hours. Assume that the population standard deviation is 6 hours and the population of employees is fairly large. Construct a 95% confidence interval for the average time required to complete the certification. A) 18.28 to 21.72 B) 18.32 to 21.68 C) 18.36 to 21.64 D) 18.40 to 21.60 E) 19.76 to 20.24 Answer: B Diff: 2 Response: See section 8.1 Estimating the Population Mean using the z Statistic (σ Known) Learning Objective: 8.1: Estimate the population mean with a known population standard deviation using the z statistic, correcting for a finite population if necessary. Bloom's level: Application 85) The employees of Cybertronics Inc. need to complete a certification online. A random sample of 16 employees gives an average time for completion of all the coursework and passing the tests of 20 hours. The population standard deviation is unknown but the sample standard deviation is 6 hours. You can assume that the population of employees is fairly large. Construct a 95% confidence interval for the average time required to complete the certification. A) 17.06 to 22.94 B) 16.80 to 23.20 C) 17.68 to 22.32 D) 17.99 to 22.01 E) 18.31 to 21.69 Answer: B Diff: 2 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. Bloom's level: Application 86) The t score (or table value for Student's t distribution) associated with the upper 5% and 48 degrees of freedom is . A) 1.677 B) 1.687 C) 1.697 D) 1.707 E) 1.717 Answer: A Diff: 1 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. Bloom's level: Application 27

87) For a new product, you need to determine the average diameter of a specialized electronic component, which will be a critical component of the new product. You measure the diameter in a sample of size 15 and find an average diameter of 0.24 mm, with a standard deviation of 0.02 mm. Other studies indicate that the diameter of similar products is normally distributed. The 99% confidence interval for the average diameter of this electronic component is closest to . A) 0.232 to 0.248 B) 0.230 to 0.250 C) 0.228 to 0.252 D) 0.225 to 0.255 E) 0.224 to 0.256 Answer: D Diff: 2 Response: See section 8.2 Estimating the Population Mean using the t Statistic (σ Unknown) Learning Objective: 8.2: Estimate the population mean with an unknown population standard deviation using the t statistic and properties of the t distribution. Bloom's level: Application 88) A researcher wants to estimate the proportion of manufacturing companies that use the six sigma method. For this purpose, she takes a random sample of 50 manufacturing companies and finds out that 28 of them employ this method. The point estimate for the proportion of the population that uses this method is . A) 0.88 B) 0.56 C) 0.44 D) 0.28 E) 0.22 Answer: B Diff: 1 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. Bloom's level: Application 89) A researcher wants to estimate the proportion of manufacturing companies that use the six sigma method. For this purpose, she takes a random sample of 50 manufacturing companies and finds out that 28 of them employ this method. The 95% confidence interval for the proportion of manufacturing companies that use six sigma is . A) 0.462 to 0.628 B) 0.452 to 0.638 C) 0.442 to 0.648 D) 0.432 to 0.658 E) 0.422 to 0.698 Answer: E Diff: 2 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. Bloom's level: Application 28

90) A researcher wants to estimate the proportion of manufacturing companies that use the six sigma method. For this purpose, she will take a random sample of 50 manufacturing companies and find out how many of them use this method. She estimates that the proportion will be anywhere from 0.4 to 0.55. Of all the proportions in that range, which one would produce the widest 95% confidence interval for the population proportion? A) 0.4 B) 0.45 C) 0.5 D) 0.55 E) 0.6 Answer: C Diff: 3 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. Bloom's level: Application 91) A researcher wants to estimate the proportion of the manufacturing companies that use the six sigma method. For this purpose, she will take a random sample of 50 manufacturing companies and find out how many of them use this method. She estimates that the proportion will be anywhere from 0.4 to 0.55. Of all the proportions in that range, which one would produce the narrowest 95% confidence interval for the population proportion? A) 0.4 B) 0.45 C) 0.5 D) 0.55 E) 0.6 Answer: A Diff: 3 Response: See section 8.3 Estimating the Population Proportion Learning Objective: 8.3: Estimate a population proportion using the z statistic. Bloom's level: Application

92) You need to determine the population variance of the diameters of a specialized electronic component used for a new product. Other studies indicate that the diameters of this product are roughly normally distributed. You take a sample of 15 units and find out that the sample average diameter is 0.24 mm, and the sample standard deviation is 0.2 mm. What is the 95% confidence interval for the population variance? A) 0.0214 to 0.0995 B) 0.0230 to 0.1066 C) 0.1466 to 0.3154 D) 0.1666 to 0.3354 E) 0.1866 to 0.3554 Answer: A Diff: 2 Response: See section 8.4 Estimating the Population Variance Learning Objective: 8.4: Use the chi-square distribution to estimate the population variance given the sample variance. Bloom's level: Application 93) If you need to estimate the population mean within 2.5 units and with a confidence level of 95%. The minimum value observed is 15 and the maximum, 65. What sample size would you use? A) 9 B) 10 C) 81 D) 96 E) 97 Answer: E Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. Bloom's level: Application

94) A researcher wants to estimate the proportion of Millennials who regularly listen to podcasts (defined as at least two entire podcasts per week). Previous studies indicate that only 18% of Millennials listen regularly to podcasts, and this researcher wants to estimate if that figure has increased. For this purpose, the researcher will use a 98% confidence level and wants to estimate the population proportion within 0.05 (i.e., 5 percentage points) of the actual value. The approximate sample size is at least . A) 47 B) 48 C) 318 D) 320 E) 331 Answer: D Diff: 2 Response: See section 8.5 Estimating Sample Size Learning Objective: 8.5: Determine the sample size needed in order to estimate the population mean and population proportion. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 9 Statistical Inference: Hypothesis Testing for Single Populations 1) Hypotheses are tentative explanations of a principle operating in nature. Answer: TRUE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 2) The first step in testing a hypothesis is to establish a true null hypothesis and a false alternative hypothesis. Answer: FALSE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 3) In testing hypotheses, the researcher initially assumes that the alternative hypothesis is true and uses the sample data to reject it. Answer: FALSE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 4) The null and the alternative hypotheses must be mutually exclusive and collectively exhaustive. Answer: TRUE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors.

5) Generally speaking, the hypotheses that business researchers want to prove are stated in the alternative hypothesis. Answer: TRUE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 6) The probability of committing a Type I error is called the power of the test. Answer: FALSE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 7) When a true null hypothesis is rejected, the researcher has made a Type I error. Answer: TRUE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 8) When a false null hypothesis is rejected, the researcher has made a Type II error. Answer: FALSE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 9) When a researcher fails to reject a false null hypothesis, a Type II error has been committed. Answer: TRUE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors.

10) Power is equal to (1 - β), the probability of a test rejecting the null hypothesis that is indeed false. Answer: TRUE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 11) The rejection region for a hypothesis test becomes smaller if the level of significance is changed from 0.01 to 0.05. Answer: FALSE Diff: 2 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 12) Whenever hypotheses are established such that the alternative hypothesis is "μ>8", where μ is the population mean, the hypothesis test would be a two-tailed test. Answer: FALSE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 13) Whenever hypotheses are established such that the alternative hypothesis is "μ ≠ 8", where μ is the population mean, the hypothesis test would be a two-tailed test. Answer: TRUE Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 14) The rejection and nonrejection regions are divided by a point called the critical value. Answer: TRUE Diff: 2 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors.

15) The probability of type II error becomes bigger if the level of significance is changed from 0.01 to 0.05. Answer: FALSE Diff: 2 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. 16) Whenever hypotheses are established such that the alternative hypothesis is "μ > 8", where μ is the population mean, the p-value is the probability of observing a sample mean greater than the observed sample mean assuming that μ = 8. Answer: TRUE Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 17) If a null hypothesis was not rejected at the 0.10 level of significance, it will be rejected at a 0.05 level of significance based on the same sample results. Answer: FALSE Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 18) If a null hypothesis was rejected at the 0.025 level of significance, it will be rejected at a 0.01 level of significance based on the same sample results. Answer: FALSE Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 19) If a null hypothesis is not rejected at the 0.05 level of significance, the p-value is bigger than 0.05 Answer: TRUE Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic.

20) In many cases a business researcher gathers data to test a hypothesis about a single population mean and the value of the population standard deviation is unknown. In this case the researcher cannot use the z test. Answer: TRUE Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 21) When the population standard deviation (σ ) is unknown, the value of s - 1 is used to compute the t value. Answer: FALSE Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 22) In testing a hypothesis about a population mean with an unknown population standard deviation (σ ) the degrees of freedom is used in the denominator of the test statistic. Answer: FALSE Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 23) When using the t test to test a hypothesis about a population mean with an unknown population standard deviation (σ ) the degrees of freedom is defined as n - 1. Answer: TRUE Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 24) A z test of proportions is used when a hypothesis test is conducted on a population proportion. Answer: TRUE Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic.

25) When conducting a hypothesis test on a population proportion, the value of q is defined as p + 1. Answer: FALSE Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 26) In conducting the z test of proportions, the sample proportion is computed by dividing the number of items being counted by the estimated total population. Answer: FALSE Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 27) Business researchers sometimes need to test for equality of population variance. The hypothesis test about a population variance is performed using a chi-square test. Answer: TRUE Diff: 1 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 28) In testing a hypothesis about a population variance, the chi-square test is fairly robust to the assumption the population is normally distributed. Answer: FALSE Diff: 1 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 29) If car manufacturer of cars orders the windshields from another company. The width of the windshields received need to be within 0.2 mm of the average width. The contract between the car manufacturer and the windshield suppler should include a clause related to the delivered windshields' median. Answer: FALSE Diff: 1 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic.

30) If the observed chi-squared value is more than the critical chi-squared value found on the related table, then the null hypothesis is rejected. Answer: TRUE Diff: 1 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 31) The value of committing a Type II error is defined by the researcher prior to the study. Answer: FALSE Diff: 1 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 32) The probability of committing a Type II error changes for each alternative value of the parameter. Answer: TRUE Diff: 1 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 33) Increasing the sample size reduces the probability of committing a Type I and Type II simultaneously. Answer: FALSE Diff: 2 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 34) If the null hypothesis is in reality false, but the sample data leads the analyst to fail to reject the null, then the analysts has made a Type II error. Answer: TRUE Diff: 1 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 35) A business is testing a new product that in reality would make the company money. Based on market research data, the business analyst committed a Type II error by rejecting the null. Answer: FALSE Diff: 1 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 7

36) A "scientific hypothesis" . A) is a synonym for the term "scientific theory" B) is disproven if a counterexample is found C) is a tentative explanation of some natural phenomena D) is a proven explanation of some natural phenomena E) may or may not be testable Answer: C Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. Bloom's level: Knowledge 37) A statistically significant result is . A) a result that is likely due to chance B) a result that is unlikely due to chance C) the same as a substantive result D) a result that is important for decision makers E) a result that leads to the rejection of the alternative hypothesis Answer: B Diff: 1 Response: See section 9.1 Introduction to Hypothesis Testing Learning Objective: 9.1: Develop both one- and two-tailed statistical hypotheses that can be tested in a business setting by examining the rejection and non-rejection regions in light of Type I and Type II errors. Bloom's level: Knowledge

38) Suppose that Ho: μ = 0.01 Ha: μ > 0.01 The value 0.01 is the maximum safe level of some naturally occurring lethal pollutant in drinking water for human consumption. You need to decide if you will use water coming from a specific source for one of the beverages your company produces, based on sample measures of this pollutant taken in random different locations and times at the source of water. In this case you would want to . A) maximize the power of the test B) maximize β C) minimize μ D) maximize the significance, α E) maximize 1 - α Answer: A Diff: 2 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. Bloom's level: Application 39) If the p-value for a one-tailed test is 0.019, then the null hypothesis . A) can be rejected at a significance level of 0.01 B) would be rejected or not rejected depending on the value of the statistic (the observed value) C) would be rejected or not rejected depending on the value of the statistic and the critical value D) can not be rejected at a significance level of 0.10 E) may or may not be rejected, but there is no enough information to answer this question Answer: D Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. Bloom's level: Application

40) The p-value is . A) the probability that the alternative hypothesis is true B) the probability that the null hypothesis is true C) the probability of a statistic being at most as extreme as the observed value when Ho is true D) the probability of a statistic being at most as extreme as the observed value when Ho is false E) the probability of a statistic being at least as extreme as the observed value when Ho is true Answer: E Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. Bloom's level: Knowledge 41) The customer help center in your company receives calls from customers who need help with some of the customized software solutions your company provides. Your company claims that the average waiting time is 7 minutes at the busiest time, from 8 a.m. to 10 a.m., Monday through Thursday. One of your main clients has recently complained that every time she calls during the busy hours, the waiting time exceeds 7 minutes. You conduct a statistical study to determine the average waiting time with a sample of 35 calls for which you obtain an average waiting time of 8.15 minutes. If the population standard deviation is known to be 4.2 minutes, and α = 0.05, the p-value is approximately . A) 0.025 B) 0.026 C) 0.05 D) 0.053 E) 0.10 Answer: D Diff: 3 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. Bloom's level: Application

42) The customer help center in your company receives calls from customers who need help with some of the customized software solutions your company provides. Your company claims that the average waiting time is 7 minutes at the busiest time, from 8 a.m. to 10 a.m., Monday through Thursday. One of your main clients has recently complained that every time she calls during the busy hours, the waiting time exceeds 7 minutes. You conduct a statistical study to determine the average waiting time with a sample of 35 calls for which you obtain an average waiting time of 8.15 minutes. If the population standard deviation is known to be 4.2 minutes, and α = 0.05, the appropriate decision is to . A) increase the sample size B) reduce the sample size C) fail to reject the 7-minute average waiting time claim D) maintain status quo E) reject the 7-minute claim Answer: C Diff: 3 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. Bloom's level: Application 43) Consider the following null and alternative hypotheses. Ho: μ = 67 Ha: μ > 67 These hypotheses . A) indicate a one-tailed test with a rejection area in the right tail B) indicate a one-tailed test with a rejection area in the left tail C) indicate a two-tailed test D) are established incorrectly E) are not mutually exclusive Answer: A Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic.

44) Consider the following null and alternative hypotheses. Ho: μ = 67 Ha: μ > 67 These hypotheses . A) indicate a one-tailed test with a rejection area in the right tail B) indicate a one-tailed test with a rejection area in the left tail C) indicate a two-tailed test D) are established incorrectly E) are not mutually exclusive Answer: B Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 45) Consider the following null and alternative hypotheses. Ho: μ = 67 Ha: μ > 67 These hypotheses . A) indicate a one-tailed test with a rejection area in the right tail B) indicate a one-tailed test with a rejection area in the left tail C) indicate a two-tailed test D) are established incorrectly E) are not mutually exclusive Answer: C Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 46) In a two-tailed hypothesis about a population mean with a sample size of 100, σ is known, and α = 0.10, the rejection region would be . A) z > 1.64 B) z > 1.28 C) z < -1.28 and z > 1.28 D) z < -1.64 and z > 1.64 E) z < -2.33 and z > 2.33 Answer: D Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic.

47) In a two-tailed hypothesis about a population mean with a sample size of 100, σ is known, and α = 0.05, the rejection region would be . A) z > 1.64 B) z > 1.96 C) z < -1.96 and z > 1.96 D) z < -1.64 and z > 1.64 E) z < -2.33 and z > 2.33 Answer: C Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 48) Suppose you are testing the null hypothesis that a population mean is less than or equal to 46, against the alternative hypothesis that the population mean is greater than 46. If the sample size is 25, σ is known, and α = .01, the critical value of z is . A) 1.645 B) -1.645 C) 1.28 D) -2.33 E) 2.33 Answer: E Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 49) Suppose you are testing the null hypothesis that a population mean is less than or equal to 46, against the alternative hypothesis that the population mean is greater than 46. The sample size is 25 and α =.05. If the sample mean is 50 and the population standard deviation is 8, the observed z value is . A) 2.5 B) -2.5 C) 6.25 D) -6.25 E) 12.5 Answer: A Diff: 2 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic.

50) Suppose you are testing the null hypothesis that a population mean is greater than or equal to 60, against the alternative hypothesis that the population mean is less than 60. The sample size is 64 and α = .05. If the sample mean is 58 and the population standard deviation is 16, the observed z value is . A) -1 B) 1 C) -8 D) 8 E) 58 Answer: A Diff: 2 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 51) A researcher is testing a hypothesis of a single mean. The critical z value for α = .05 in a onetailed test is 1.645. The observed z value from sample data is 1.13. The decision made by the researcher based on this information is to the null hypothesis. A) reject B) fail to reject C) redefine D) change the alternate hypothesis E) restate Answer: B Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 52) A researcher is testing a hypothesis of a single mean. The critical z value for α = .05 in a twotailed test is +1.96. The observed z value from sample data is 1.85. The decision made by the researcher based on this information is to the null hypothesis. A) reject B) fail to reject C) redefine D) change the alternate hypothesis E) restate Answer: B Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic.

53) A researcher is testing a hypothesis of a single mean. The critical z value for α = .05 in a twotailed test is +1.96. The observed z value from sample data is 2.85. The decision made by the researcher based on this information is to the null hypothesis. A) reject B) fail to reject C) redefine D) change the alternate hypothesis E) restate Answer: A Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 54) A researcher is testing a hypothesis of a single mean. The critical z value for α = .05 in a twotailed test is +1.96. The observed z value from sample data is -2.11. The decision made by the researcher based on this information is to the null hypothesis. A) reject B) fail to reject C) redefine D) change the alternate hypothesis E) restate Answer: A Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 55) A coffee-dispensing machine is supposed to deliver 8 ounces of liquid into each paper cup, but a consumer believes that the actual mean amount is less. The consumer obtained a sample of 49 cups of the dispensed liquid with average of 7.75 ounces. If the population variance of the dispensed liquid per cup is 0.81 ounces, and α = 0.05, the p-value is approximately . A) 0.05 B) 0.026 C) 0.06 D) 0.015 E) 0.10 Answer: B Diff: 3 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic.

56) A coffee-dispensing machine is supposed to deliver 8 ounces of liquid into each paper cup, but a consumer believes that the actual mean amount is less. The consumer obtained a sample of 49 cups of the dispensed liquid with average of 7.75 ounces. If the population variance of the dispensed liquid delivered per cup is 0.81 ounces, and α = 0.05, the appropriate decision is to . A) increase the sample size B) reduce the sample size C) fail to reject the 8-ounces claim D) maintain status quo E) reject the 8-ounces claim Answer: E Diff: 3 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 57) The local swim team is considering offering a new semi-private class aimed at entry-level swimmers, but needs at minimum number of swimmers to sign up in order to be cost effective. Last year's data showed that during 8 swim sessions the average number of entry-level swimmers attending was 15. Suppose the instructor wants to conduct a hypothesis test. The alternative hypothesis for this hypothesis test is: "the population mean is less than 15". The sample size is 8, σ is known, and α =.05, the critical value of z is _ . A) 1.645 B) -1.645 C) 1.96 D) -1.96 E) 2.05 Answer: B Diff: 2 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic.

58) The local swim team is considering offering a new semi-private class aimed at entry-level swimmers, but needs a minimum number of swimmers to sign up in order to be cost effective. Last year's data showed that during 8 swim sessions the average number of entry-level swimmers attending was 15. Suppose the instructor wants to conduct a hypothesis test and the alternative hypothesis is "the population mean is greater than 15." If the sample size is 5, σ is known, and α = .01, the critical value of z is . A) 2.575 B) -2.575 C) 2.33 D) -2.33 E) 2.45 Answer: C Diff: 2 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 59) A home building company routinely orders standard interior doors with a height of 80 inches. Recently the installers have complained that the doors are not the standard height. The quality control inspector for the home building company is concerned that the manufacturer is supplying doors that are not 80 inches in height. In an effort to test this, the inspector is going to gather a sample of the recently received doors and measure the height. The alternative hypothesis for the statistical test to determine if the doors are not 80 inches is . A) the mean height is > 80 inches B) the mean height is < 80 inches C) the mean height is = 80 inches D) the mean height is ≠ 80 inches E) the mean height is ≥ 80 inches Answer: D Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic.

60) Jennifer Cantu, VP of Customer Services at Tri-State Auto Insurance, Inc., monitors the claims processing time of the claims division. Her standard includes "a mean processing time of 5 days or less." Each week, her staff checks for compliance by analyzing a random sample of 60 claims. Jennifer's null hypothesis is . A) μ > 5 B) σ > 5 C) n = 60 D) μ < 5 E) μ =5 Answer: E Diff: 1 Response: See section 9.2 Testing Hypotheses about a Population Mean using the z Statistic (σ Known) Learning Objective: 9.2: Reach a statistical conclusion in hypothesis testing problems about a population mean with a known population standard deviation using the z statistic. 61) The customer help center in your company receives calls from customers who need help with some of the customized software solutions your company provides. Your company claims that the average waiting time is 7 minutes at the busiest time, from 8 a.m. to 10 a.m., Monday through Thursday. One of your main clients has recently complained that every time she calls during the busy hours, the waiting time exceeds 7 minutes. You conduct a statistical study to determine the average waiting time with a sample of 35 calls, for which you obtain an average waiting time of 8.15 minutes. Suppose that you can assume that waiting times are normally distributed. The sample standard deviation is 4.2 minutes. The null hypothesis is . A) n ≠ 35 B) n = 35 C) μ = 7 D) μ ≠ 7 E) μ > 7 Answer: C Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. Bloom's level: Knowledge

62) In performing a hypothesis test where the null hypothesis is that the population mean is 23 against the alternative hypothesis that the population mean is not equal to 23, a random sample of 17 items is selected. The sample mean is 24.6 and the sample standard deviation is 3.3. It can be assumed that the population is normally distributed. The degrees of freedom associated with this are . A) 17 B) 16 C) 15 D) 2 E) 1 Answer: B Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 63) In performing a hypothesis test where the null hypothesis is that the population mean is 4.8 against the alternative hypothesis that the population mean is not equal to 4.8, a random sample of 25 items is selected. The sample mean is 4.1 and the sample standard deviation is 1.4. It can be assumed that the population is normally distributed. The degrees of freedom associated with this are . A) 25 B) 24 C) 26 D) 2 E) 1 Answer: B Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic.

64) In performing a hypothesis test where the null hypothesis is that the population mean is 4.8 against the alternative hypothesis that the population mean is not equal to 4.8, a random sample of 25 items is selected. The sample mean is 4.1 and the sample standard deviation is 1.4. It can be assumed that the population is normally distributed. The level of significance is selected to be 0.10. The critical value found in the "t" table "t" for this problem is . A) 1.318 B) 1.711 C) 2.492 D) 2.797 E) 3.227 Answer: B Diff: 2 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 65) In performing a hypothesis test where the null hypothesis is that the population mean is 4.8 against the alternative hypothesis that the population mean is not equal to 4.8, a random sample of 25 items is selected. The sample mean is 4.1 and the sample standard deviation is 1.4. It can be assumed that the population is normally distributed. The observed "t" value for this problem is . A) -12.5 B) 12.5 C) -2.5 D) -0.7 E) 0.7 Answer: C Diff: 2 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 66) In performing hypothesis tests about the population mean, if the population standard deviation is not known, a t test can be used to test the mean if . A) n is small B) the sample is random C) the population mean is known D) the population is normally distributed E) the population is chi-square distributed Answer: D Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 20

67) Suppose a researcher is testing a null hypothesis that μ = 61. A random sample of n = 36 is taken resulting in a sample mean of 63 and s = 9. The observed test statistic is . A) -0.22 B) 0.22 C) 1.33 D) 8.08 E) 7.58 Answer: C Diff: 2 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 68) The local oil changing business is very busy on Saturday mornings and is considering expanding. A national study of similar businesses reported the mean number of customers waiting to have their oil changed on Saturday morning is 3.6. Suppose the local oil changing business owner, wants to perform a hypothesis test. The null hypothesis is the population mean is 3.6 and the alternative hypothesis that the population mean is not equal to 3.6. The owner takes a random sample of 16 Saturday mornings during the past year and determines the sample mean is 4.2 and the sample standard deviation is 1.4. It can be assumed that the population is normally distributed. The observed "t" value for this problem is . A) 0.05 B) 0.43 C) 1.71 D) 1.33 E) 0.71 Answer: C Diff: 2 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic.

69) The local oil changing business is very busy on Saturday mornings and is considering expanding. A national study of similar businesses reported the mean number of customers waiting to have their oil changed on Saturday morning is 3.6. Suppose the local oil changing business owner, wants to perform a hypothesis test. The null hypothesis is the population mean is 3.6 and the alternative hypothesis that the population mean is not equal to 3.6. The owner takes a random sample of 16 Saturday mornings during the past year and determines the sample mean is 4.2 and the sample standard deviation is 1.4. It can be assumed that the population is normally distributed and the level of significance is 0.05. The critical value found in the "t" table for this problem is . A) 1.753 B) 2.947 C) 2.120 D) 2.131 E) 2.311 Answer: D Diff: 2 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 70) The local oil changing business is very busy on Saturday mornings and is considering expanding. A national study of similar businesses reported the mean number of customers waiting to have their oil changed on Saturday morning is 3.6. Suppose the local oil changing business owner, wants to perform a hypothesis test. The null hypothesis is the population mean is 3.6 and the alternative hypothesis is the population mean is not equal to 3.6. The owner takes a random sample of 16 Saturday mornings during the past year and determines the sample mean is 4.2 and the sample standard deviation is 1.4. It can be assumed that the population is normally distributed. The level of significance is 0.05. The decision rule for this problem is to reject the null hypothesis if the observed "t" value is . A) less than -2.131 or greater than 2.131 B) less than -1.761 or greater than 1.761 C) less than -1.753 or greater than 1.753 D) less than -2.120 or greater than 2.120 E) less than -3.120 or greater than 3.120 Answer: A Diff: 2 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic.

71) A coffee-dispensing machine is supposed to deliver 8 ounces of liquid into each paper cup, but a consumer believes that the actual mean amount is less. The consumer obtained a sample of 16 cups of the dispensed liquid with sample mean of 7.75 ounces and sample variance of 0.81 ounces. If the dispensed liquid delivered per cup is normally distributed, the appropriate decision at α = 0.05 is to . A) increase the sample size B) reduce the sample size C) fail to reject the 8-ounces claim D) maintain status quo E) reject the 8-ounces claim Answer: C Diff: 3 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 72) The weight of a USB flash drive is 30 grams and is normally distributed. Periodically, quality control inspectors at Dallas Flash Drives randomly select a sample of 17 USB flash drives. If the mean weight of the USB flash drives is too heavy or too light the machinery is shut down for adjustment; otherwise, the production process continues. The last sample showed a mean and standard deviation of 31.9 and 1.8 grams, respectively. Using α = 0.10, the critical "t" values are . A) -2.120 and 2.120 B) -2.131 and 2.131 C) -1.753 and 1.753 D) -1.746 and 1.746 E) -2.567 and 2.567 Answer: D Diff: 2 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic.

73) The weight of a USB flash drive is 30 grams and is normally distributed. Periodically, quality control inspectors at Dallas Flash Drives randomly select a sample of 17 USB flash drives. If the mean weight of the USB flash drives is too heavy or too light the machinery is shut down for adjustment; otherwise, the production process continues. The last sample showed a mean and standard deviation of 31.9 and 1.8 grams, respectively. The null hypothesis is . A) n ≠ 17 B) n = 17 C) μ = 30 D) μ ≠ 30 E) μ ≥ 34.9 Answer: C Diff: 1 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic. 74) The weight of a USB flash drive is 30 grams and is normally distributed. Periodically, quality control inspectors at Dallas Flash Drives randomly select a sample of 17 USB flash drives. If the mean weight of the USB flash drives is too heavy or too light the machinery is shut down for adjustment; otherwise, the production process continues. The last sample showed a mean and standard deviation of 31.9 and 1.8 grams, respectively. Using α = 0.10, the appropriate decision is to . A) reject the null hypothesis and shut down the process B) reject the null hypothesis and do not shut down the process C) fail to reject the null hypothesis and shut down the process D) fail to reject the null hypothesis and do not shut down the process) E) do nothing Answer: A Diff: 3 Response: See section 9.3 Testing Hypotheses about a Population Mean using the t Statistic (σ unknown) Learning Objective: 9.3: Reach a statistical conclusion in hypothesis testing problems about a population mean with an unknown population standard deviation using the t statistic.

75) The customer help center in your company receives calls from customers who need help with some of the customized software solutions your company provides. Previous studies had indicated that 20% of customers who call the help center are Hispanics whose native language is Spanish and therefore would prefer to talk to a Spanish-speaking representative. This figure coincides with the national proportion, as shown by multiple larger polls. You want to test the hypothesis that 20% of the callers would prefer to talk to a Spanish-speaking representative. You conduct a statistical study with a sample of 35 calls and find out that 11 of the callers would prefer a Spanish-speaking representative. The significance level for this test is 0.01. The value of the test statistic obtained is . A) 0.008 B) 0.29 C) 0.58 D) 1.69 E) 1.73 Answer: D Diff: 2 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. Bloom's level: Application 76) A political scientist wants to prove that a candidate is currently carrying more than 60% of the vote in the state. She has her assistants randomly sample 200 eligible voters in the state by telephone and only 90 declare that they support her candidate. The observed z value for this problem is . A) -4.33 B) 4.33 C) 0.45 D) -.31 E) 2.33 Answer: A Diff: 2 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic.

77) A company believes that it controls more than 30% of the total market share for one of its products. To prove this belief, a random sample of 144 purchases of this product is contacted. It is found that 50 of the 144 purchases were of this company's brand. If a researcher wants to conduct a statistical test for this problem, the alternative hypothesis would be . A) the population proportion is less than 0.30 B) the population proportion is greater than 0.30 C) the population proportion is not equal to 0.30 D) the population mean is less than 40 E) the population mean is greater than 40 Answer: B Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 78) A company believes that it controls more than 30% of the total market share for one of its products. To prove this belief, a random sample of 144 purchases of this product is contacted. It is found that 50 of the 144 purchases were of this company's brand. If a researcher wants to conduct a statistical test for this problem, the observed z value would be . A) 0.05 B) 0.103 C) 0.35 D) 1.24 E) 1.67 Answer: D Diff: 2 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 79) A company believes that it controls more than 30% of the total market share for one of its products. To prove this belief, a random sample of 144 purchases of this product is contacted. It is found that 50 of the 144 purchases were of this company's brand. If a researcher wants to conduct a statistical test for this problem, the test would be . A) a one-tailed test B) a two-tailed test C) an alpha test D) a finite population test E) a finite sample test Answer: A Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic.

80) A small restaurant owner believes no more than 36% of his customers travel over 10 miles to his business. He is interested in expanding his customer base through marketing. However, he would like to test his hypothesis prior to investing money in a marketing initiative. He intends to use the following null and alternative hypotheses. Ho: p = 0.36 Ha: p > 0.36 These hypotheses . A) indicate a one-tailed test with a rejection area in the right tail B) indicate a one-tailed test with a rejection area in the left tail C) indicate a two-tailed test D) are established incorrectly E) are not mutually exclusive Answer: A Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 81) A small restaurant owner believes at least 36 % of his customers would be willing to order take out service if it were available. He is interested in surveying his customer base. He intends to use the following null and alternative hypotheses. Ho: p = 0.36 Ha: p < 0.36 These hypotheses . A) indicate a one-tailed test with a rejection area in the right tail B) indicate a one-tailed test with a rejection area in the left tail C) indicate a two-tailed test D) are established incorrectly E) are not mutually exclusive Answer: B Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic.

82) A small restaurant owner believes exactly 36 % of his customers come to the restaurant because of his daily half-price specials. He is interested in expanding his daily specials and increasing the price. However, he would like to test his hypothesis prior to expanding the daily special offerings. He intends to use the following null and alternative hypotheses. Ho: p = 0.36 Ha: p ≠ 0.36 These hypotheses . A) indicate a one-tailed test with a rejection area in the right tail B) indicate a one-tailed test with a rejection area in the left tail C) indicate a two-tailed test D) are established incorrectly E) are not mutually exclusive Answer: C Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 83) Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), monitors the default rate on personal loans at the AFB member banks. One of her standards is "no more than 5% of personal loans should be in default." On each Friday, the default rate is calculated for a sample of 500 personal loans. Last Friday's sample contained 30 defaulted loans. Ophelia's null hypothesis is . A) p > 0.05 B) p = 0.05 C) n = 30 D) n = 500 E) n = 0.05 Answer: B Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic.

84) Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), monitors the default rate on personal loans at the AFB member banks. One of her standards is "no more than 5% of personal loans should be in default." On each Friday, the default rate is calculated for a sample of 500 personal loans. Last Friday's sample contained 30 defaulted loans. Using α = 0.10, the critical z value is . A) 1.645 B) -1.645 C) 1.28 D) -1.28 E) 2.28 Answer: C Diff: 2 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 85) Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), monitors the default rate on personal loans at the AFB member banks. One of her standards is "no more than 5% of personal loans should be in default." On each Friday, the default rate is calculated for a sample of 500 personal loans. Last Friday's sample contained 30 defaulted loans. Using α = 0.10, the observed z value is . A) 1.03 B) -1.03 C) 0.046 D) -0.046 E) 1.33 Answer: A Diff: 2 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 86) Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), monitors the default rate on personal loans at the AFB member banks. One of her standards is "no more than 5% of personal loans should be in default." On each Friday, the default rate is calculated for a sample of 500 personal loans. Last Friday's sample contained 30 defaulted loans. Using α = 0.10, the appropriate decision is to . A) reduce the sample size B) increase the sample size C) reject the null hypothesis D) fail to reject the null hypothesis E) do nothing Answer: D Diff: 3 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 29

87) Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), monitors the default rate on personal loans at the AFB member banks. One of her standards is "no more than 5% of personal loans should be in default." On each Friday, the default rate is calculated for a sample of 500 personal loans. Last Friday's sample contained 38 defaulted loans. Using α = 0.10, the appropriate decision is to . A) reduce the sample size B) increase the sample size C) reject the null hypothesis D) fail to reject the null hypothesis E) do nothing Answer: C Diff: 3 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 88) The executives of CareFree Insurance, Inc. feel that "a majority of our employees perceive a participatory management style at CareFree." A random sample of 200 CareFree employees is selected to test this hypothesis at the 0.05 level of significance. Eighty employees rate the management as participatory. The null hypothesis is . A) n = 30 B) n = 200 C) p = 0.50 D) p < 0.50 E) n > 200 Answer: C Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 89) The executives of CareFree Insurance, Inc. feel that "a majority of our employees perceive a participatory management style at CareFree." A random sample of 200 CareFree employees is selected to test this hypothesis at the 0.05 level of significance. Eighty employees rate the management as participatory. The appropriate decision is to . A) fail to reject the null hypothesis B) reject the null hypothesis C) reduce the sample size D) increase the sample size E) do nothing Answer: B Diff: 3 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 30

90) The executives of CareFree Insurance, Inc. feel that "a majority of our employees perceive a participatory management style at CareFree." A random sample of 200 CareFree employees is selected to test this hypothesis at the 0.05 level of significance. Ninety employees rate the management as participatory. The appropriate decision is to . A) fail to reject the null hypothesis B) reject the null hypothesis C) reduce the sample size D) increase the sample size E) maintain status quo Answer: A Diff: 3 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 91) Elwin Osbourne, CIO at GFS, Inc., suspects that at least 25% of e-mail messages sent by GFS employees are not business related. A random sample of 300 e-mail messages was selected to test this hypothesis at the 0.01 level of significance. Fifty-four of the messages were not business related. The null hypothesis is . A) β = 30 B) n = 300 C) p < 0.25 D) p ≠ 0.25 E) p = 0.25 Answer: E Diff: 1 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 92) Elwin Osbourne, CIO at GFS, Inc., suspects that at least 25% of e-mail messages sent by GFS employees are not business related. A random sample of 300 e-mail messages was selected to test this hypothesis at the 0.01 level of significance. Fifty-four of the messages were not business related. The appropriate decision is to . A) increase the sample size B) gather more data C) reject the null hypothesis D) fail to reject the null hypothesis E) maintain status quo Answer: C Diff: 3 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic.

93) Elwin Osbourne, CIO at GFS, Inc., suspects that at least 25% of e-mail messages sent by GFS employees are not business related. A random sample of 300 e-mail messages was selected to test this hypothesis at the 0.01 level of significance. Sixty of the messages were not business related. The appropriate decision is to . A) increase the sample size B) gather more data C) maintain status quo D) fail to reject the null hypothesis E) reject the null hypothesis Answer: D Diff: 3 Response: See section 9.4 Testing Hypotheses about a Proportion Learning Objective: 9.4: Reach a statistical conclusion in hypothesis testing problems about a population proportion using the z statistic. 94) Discrete Components, Inc. manufactures a line of electrical resistors. Presently, the carbon composition line is producing 100-ohm resistors. The population variance of these resistors "must not exceed 4" to conform to industry standards. Periodically, the quality control inspectors check for conformity by randomly selecting 10 resistors from the line, and calculating the sample variance. The last sample had a variance of 4.36. Assume that the population is normally distributed. Using α = 0.05, the null hypothesis is . A) μ = 100 B) σ ≤ 10 C) s2 ≥ 4 D) σ2 = 4 E) n = 100 Answer: D Diff: 1 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic.

95) Albert Miller, VP of Production of a company that produces components for auto braking systems is examining the diameter of a specialized electrical wire produced with newly acquired machines in a new production line. For safety reasons, it is important that the variance in the diameter doesn't exceed 0.15 inches. Albert knows that the variance of the other production lines is 0.15 and he wants to make sure the new machine also delivers products whose variance doesn't exceed the safety limit. His staff randomly selects a sample of 25 wires and find out that the standard deviation of the sample is 0.42 inches. Assume that wire diameters are normally distributed. Using α = 0.10, the appropriate decision is . A) increase the sample size B) reduce the sample size C) fail to reject the null hypothesis D) maintain status quo E) reject the null hypothesis Answer: C Diff: 3 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 96) Discrete Components, Inc. manufactures a line of electrical resistors. Presently, the carbon composition line is producing 100-ohm resistors. The population variance of these resistors "must not exceed 4" to conform to industry standards. Periodically, the quality control inspectors check for conformity by randomly select 10 resistors from the line, and calculating the sample variance. The last sample had a variance of 4.36. Assume that the population is normally distributed. Using α = 0.05, the critical value of chi-square is . A) 18.31 B) 16.92 C) 3.94 D) 3.33 E) 19.82 Answer: B Diff: 2 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic.

97) Discrete Components, Inc. manufactures a line of electrical resistors. Presently, the carbon composition line is producing 100-ohm resistors. The population variance of these resistors "must not exceed 4" to conform to industry standards. Periodically, the quality control inspectors check for conformity by randomly select 10 resistors from the line, and calculating the sample variance. The last sample had a variance of 4.36. Assume that the population is normally distributed. Using α = 0.05, the observed value of chi-square is . A) 1.74 B) 1.94 C) 10.90 D) 9.81 E) 8.91 Answer: D Diff: 2 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 98) Discrete Components, Inc. manufactures a line of electrical resistors. Presently, the carbon composition line is producing 100-ohm resistors. The population variance of these resistors "must not exceed 4" to conform to industry standards. Periodically, the quality control inspectors check for conformity by randomly select 10 resistors from the line, and calculating the sample variance. The last sample had a variance of 4.36. Assume that the population is normally distributed. Using α = 0.05, the appropriate decision is _. A) increase the sample size B) reduce the sample size C) reject the null hypothesis D) fail to reject the null hypothesis E) maintain status quo Answer: D Diff: 3 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic.

99) David Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. Based on a recent census of personnel, David knows that the variance of teller training time in the southeast region is 8, and he wonders if the variance in the southwest region is the same number. His staff randomly selected personnel files for 15 tellers in the southwest region, and determined that their mean training time was 25 hours and that the standard deviation was 4 hours. Assume that teller training time is normally distributed. Using α = 0.10, the null hypothesis is . A) μ = 25 B) σ2 = 8 C) σ2 = 4 D) σ2 ≤ 8 E) s2 = 16 Answer: B Diff: 1 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 100) David Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. Based on a recent census of personnel, David knows that the variance of teller training time in the southeast region is 8, and he wonders if the variance in the southwest region is the same number. His staff randomly selected personnel files for 15 tellers in the southwest region, and determined that their mean training time was 25 hours and that the standard deviation was 4 hours. Assume that teller training time is normally distributed. Using α = 0.10, the critical values of chi-square are . A) 7.96 and 26.30 B) 6.57 and 23.68 C) -1.96 and 1.96 D) -1.645 and 1.645 E) -6.57 and 23.68 Answer: B Diff: 2 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic.

101) David Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB banks. Based on a recent census of personnel, David knows that the variance of teller training time in the southeast region is 8, and he wonders if the variance in the southwest region is the same number. His staff randomly selected personnel files for 15 tellers in the southwest region, and determined that their mean training time was 25 hours and that the standard deviation was 4 hours. Assume that teller training time is normally distributed. Using α = 0.10, the observed value of chi-square is . A) 28.00 B) 30.00 C) 56.00 D) 60.00 E) 65.00 Answer: A Diff: 2 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 102) David Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB. Based on a recent census of personnel, David knows that the variance of teller training time in the southeast region is 8, and he wonders if the variance in the southwest region is the same number. His staff randomly selected personnel files for 15 tellers in the southwest region, and determined that their mean training time was 25 hours and that the standard deviation was 4 hours. Assume that teller training time is normally distributed. Using α = 0.10, the appropriate decision is . A) increase the sample size B) reduce the sample size C) fail to reject the null hypothesis D) maintain status quo E) reject the null hypothesis Answer: E Diff: 3 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic.

103) A company has a contract to supply specialized batteries to a manufacturer with a mean width of 54mm and a variance of 1.8 or less with an alpha of 0.05. The company randomly tests 20 of these batteries to ensure that they are meeting the requirements of the contract. What would be the null hypothesis each time the company conducts the test of the variances? A) σ2 = 0.05 B) σ2 ≥ 1.34 C) s2 = 1.8 D) σ2 = 1.8 E) μ = 54 Answer: D Diff: 1 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 104) A company has a contract to supply specialized batteries to a manufacturer with a mean width of 54mm and a variance of 1.8 or less with an alpha of 0.05. The company randomly tests 20 of these batteries to ensure that they are meeting the requirements of the contract. If the most recent test had a variance of 1.9, what would be the critical value of this chi-squared test? A) 30.14 B) 32.85 C) 1.645 D) -30.14 E) -1.645 Answer: A Diff: 2 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 105) A company has a contract to supply specialized batteries to a manufacturer with a mean width of 54mm and a variance of 1.8 or less with an alpha of 0.05. The company randomly tests 20 of these batteries to ensure that they are meeting the requirements of the contract. If the most recent test had a variance of 1.9, what would be the observed chi-squared value? A) 32.85 B) 17.91 C) 20.06 D) 30.14 E) 1.645 Answer: C Diff: 2 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic.

106) A company has a contract to supply specialized batteries to a manufacturer with a mean width of 54mm and a variance of 1.8 or less with an alpha of 0.05. The company randomly tests 20 of these batteries to ensure that they are meeting the requirements of the contract. If the most recent test had a variance of 1.9, what should the company do? A) Continue production B) Test the mean C) Break the contract D) Stop production to fix issues E) Ask to revise the contract Answer: A Diff: 3 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 107) A company has a contract to supply specialized batteries to a manufacturer with a mean width of 54mm and a standard deviation of 1.5 or less with an alpha of 0.10. The company randomly tests 25 of these batteries to ensure that they are meeting the requirements of the contract. If the most recent test had a standard deviation of 1.8 what would be the critical value of this chi-squared test? A) -33.196 B) 36.415 C) 1.645 D) 33.196 E) 1.282 Answer: D Diff: 2 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 108) A company has a contract to supply specialized batteries to a manufacturer with a mean width of 54mm and a standard deviation of 1.5 or less with an alpha of 0.10. The company randomly tests 25 of these batteries to ensure that they are meeting the requirements of the contract. If the most recent test had a standard deviation of 1.8 what would be the observed chisquared value? A) 34.56 B) 36.42 C) 16.67 D) 33.20 E) -34.56 Answer: A Diff: 2 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic.

109) A company has a contract to supply specialized batteries to a manufacturer with a mean width of 54mm and a standard deviation of 1.5 or less with an alpha of 0.10. The company randomly tests 25 of these batteries to ensure that they are meeting the requirements of the contract. If the most recent test had a standard deviation of 1.8, what should the company do? A) Continue production B) Test the mean C) Break the contract D) Stop production to fix issues E) Ask to revise the contract Answer: D Diff: 3 Response: See section 9.5 Testing Hypotheses about a Variance Learning Objective: 9.5: Reach a statistical conclusion in hypothesis testing problems about a population variance using the chi-square statistic. 110) The lifetime of a squirrel follows a normal distribution with mean μ months and a standard deviation σ = 7 months. To test the alternative hypothesis that μ > 40, 25 squirrels are randomly selected. The null hypothesis is rejected when the sample mean is bigger than 43. Assuming that μ = 45, the probability of type II error is approximately . A) 0.076 B) 0.016 C) 0.05 D) 0.011 E) 0.983 Answer: A Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 111) The lifetime of a squirrel follows a normal distribution with mean μ = 40 months and a standard deviation σ = 7 months. To test the hypothesis that μ > 40, 25 squirrels are randomly selected. Assuming that μ = 45, and α = 0.1, the probability of type II error is approximately . A) 0.076 B) 0.016 C) 0.05 D) 0.011 E) 0.983 Answer: D Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis.

112) The lifetime of a squirrel follows a normal distribution with mean μ = 40 months and a standard deviation σ = 7 months. To test the hypothesis that μ > 40, 25 squirrels are randomly selected. Assuming that μ = 45, and α = 0.1, the power is approximately . A) 0.076 B) 0.016 C) 0.05 D) 0.011 E) 0.989 Answer: E Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 113) A laptop battery lifespan follows a normal distribution with mean μ = 22 months and a standard deviation σ = 6 months. To test the alternative hypothesis that μ < 22 with α = .05, 36 laptop batteries are randomly selected. Assuming that μ = 21, the probability of type II error is approximately . A) -0.65 B) 0.2422 C) 0.64 D) 0.7422 E) 0.2508 Answer: D Diff: 2 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 114) A laptop battery lifespan follows a normal distribution with mean μ = 22 months and a standard deviation σ = 6 months. To test the alternative hypothesis that μ < 22 with α = .01, 36 laptop batteries are randomly selected. Assuming that μ = 24, the probability of type II error is approximately . A) -.01 B) 2.33 C) -4.33 D) 0.01 E) 0.00 Answer: E Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis.

115) A laptop battery lifespan follows a normal distribution with mean μ = 20 months and a standard deviation σ = 6 months. To test the alternative hypothesis that μ > 20 with α = .05, 36 laptop batteries are randomly selected. Assuming that μ = 21, the probability of type II error is approximately . A) 0.00 B) 0.2595 C) 0.2036 D) 0.6449 E) 0.7405 Answer: E Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 116) A laptop battery lifespan follows a normal distribution with mean μ = 20 months and a standard deviation σ = 6 months. To test the alternative hypothesis that μ > 20 with α = .05, 36 laptop batteries are randomly selected. Assuming that μ = 21, the power of this is approximately . A) 0.00 B) 0.2595 C) 0.2036 D) 0.6449 E) 0.7405 Answer: B Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 117) A recent survey suggests 30% of boat owners do not regularly use their boat after the second year of ownership. A local boat dealer wants to test the alternative hypothesis that p ≠ .30 with α = .05. He surveys 60 boat owners and finds 42% of the owners who have owned their boat for three years report they do not regularly use their boat. Assuming that p = .35, the probability of type II error is approximately . A) 0.8542 B) 0.1458 C) 0.3577 D) 0.1423 E) 0.4965 Answer: A Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis.

118) A recent survey suggests 30% of boat owners do not regularly use their boat after the second year of ownership. A local boat dealer wants to test the alternative hypothesis that p > .30 with α = .05. He surveys 60 boat owners and finds 42% of the owners who have owned their boat for three years report they do not regularly use their boat. Assuming that p = .35, the probability of type II error is approximately . A) 0.2211 B) 0.1421 C) 0.8579 D) 0.7789 E) 0.4965 Answer: D Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 119) A recent survey suggests 30% of boat owners do not regularly use their boat after the second year of ownership. A local boat dealer wants to test the alternative hypothesis that p > .30 with α = .05. He surveys 60 boat owners and finds 42% of the owners who have owned their boat for three years report they do not regularly use their boat. Assuming that p = .35, the power is approximately . A) 0.2211 B) 0.1421 C) 0.8579 D) 0.7789 E) 0.4965 Answer: A Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis.

120) A car manufacturer looks at past sales and realizes that the most common car color is silver with 24% of purchasers selecting that color. The dealerships in one state believe that car purchasers in their area believe that the proportion of purchasers selecting silver is greater than 24%. Based on a survey of 84 car purchasers in the state, the dealerships find that 26% of them would select silver. If the true proportion selecting silver is 27%, what is the probability of the dealerships making a Type II error, given an alpha of 0.05? A) 0.4587 B) 0.1154 C) 0.8322 D) 0.8423 E) 0.1678 Answer: C Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 121) A car manufacturer looks at past sales and realizes that the most common car color is silver with 24% of purchasers selecting that color. The dealerships in one state believe that car purchasers in their area believe that the proportion of purchasers selecting silver is greater than 24%. Based on a survey of 84 car purchasers in the state, the dealerships find that 26% of them would select silver. If the true proportion selecting silver is 27%, what is the power, given an alpha of 0.05? A) 0.4587 B) 0.1154 C) 0.8322 D) 0.8423 E) 0.1678 Answer: E Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 122) Which of the following best describes a Type II error? A) Rejecting the null when it is false B) Failing to reject the null when it is false C) Rejecting the null when it is true D) Failing to reject the null when it is true E) Rejecting the alternative when it is true Answer: B Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis.

123) Which if the following would result in a higher probability of making a Type II error? A) When the sample size is large B) When the alpha is large C) When the alternative value is far from the hypothesized value D) When the alternative value is close to the hypothesized value E) When the alternative value is under 10 Answer: D Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis. 124) Which of the following best describes the power? A) Rejecting the null when it is false B) Failing to reject the null when it is false C) Rejecting the null when it is true D) Failing to reject the null when it is true E) Rejecting the alternative when it is true Answer: A Diff: 3 Response: See section 9.6 Solving for Type II Errors. Learning Objective: 9.6: Solve for possible Type II errors when failing to reject the null hypothesis.

Business Statistics, 11e (Black) Chapter 10 Statistical Inference about Two Populations 1) An appropriate sampling plan to determine if there is a difference in the speed of a wireless router from two different manufacturers, consists of a network manager drawing independent samples of wireless routers from the two manufacturers and comparing the difference in the sample means for the connection speed. Answer: TRUE Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 2) The difference in two sample means is normally distributed for sample sizes ≥ 30, only if the populations are normally distributed. Answer: FALSE Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 3) If the sample sizes are greater than 30 and the population variances are known, the basis for statistical inferences about the difference in two population means using two independent random samples is the z-statistic, regardless of the shapes of the population distributions. Answer: TRUE Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 4) If the sample sizes are small, but the populations are normally distributed and the population variances are known, the z-statistic can be used as the basis for statistical inferences about the difference in two population means using two independent random samples. Answer: TRUE Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic.

5) If a 98% confidence interval for the difference in the two population means does not contain zero, then the null hypothesis of a zero difference between the two population means cannot be rejected at a 0.02 level of significance. Answer: FALSE Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 6) If a 90% confidence interval for the difference in the two population means contains zero, then the null hypothesis of zero difference between the two population means cannot be rejected at a 0.10 level of significance. Answer: TRUE Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 7) If the populations are normally distributed but the population variances are unknown the zstatistic can be used as the basis for statistical inferences about the difference in two population means using two independent random samples. Answer: FALSE Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. 8) If the populations are normally distributed but the population variances are unknown the tstatistic can be used as the basis for statistical inferences about the difference in two population means using two independent random samples. Answer: TRUE Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test.

9) If the variances of the two populations are not equal, it is appropriate to use the "pooled" formula to determine the t-statistic for the hypothesis test of the difference in the two population means. Answer: FALSE Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. 10) If the populations are normally distributed and the variances of the two populations are equal, it is appropriate to use the "pooled" formula to determine the t-statistic for the hypothesis test of the difference in the two population means. Answer: TRUE Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. 11) If the populations are normally distributed and the variances of the two populations are not equal, it is appropriate to use the "unpooled" formula to determine the t-statistic for the hypothesis test of the difference in the two population means. Answer: TRUE Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. 12) In order to construct an interval estimate of the difference in the means of two normally distributed populations with unknown but equal variances, using two independent samples of size n1 and n2, we must use a t distribution with (n1 + n2 -1) degrees of freedom. Answer: FALSE Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test.

13) In order to construct an interval estimate of the difference in the means of two normally distributed populations with unknown but equal variances, using two independent samples of size n1 and n2, we must use a t distribution with (n1 + n2 - 2) degrees of freedom. Answer: TRUE Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. 14) In a set of matched samples, each data value in one sample is related to or matched with a corresponding data value in the other sample. Answer: TRUE Diff: 1 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 15) Hypothesis tests conducted on sets of matched samples are sometimes referred to as correlated t tests. Answer: TRUE Diff: 1 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 16) Sets of matched samples are also referred to as dependent samples. Answer: TRUE Diff: 1 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 17) In conducting a matched-pairs hypothesis test, the null and alternative hypotheses always represent one-tailed tests. Answer: FALSE Diff: 1 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations.

18) When testing for the difference between two population proportions we use a pooled estimate of the proportion. Answer: TRUE Diff: 1 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 19) When finding a confidence interval for the difference between two population proportions we use a pooled estimate of the proportion. Answer: FALSE Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 20) Testing the difference in two population proportions is useful whenever the researcher is interested in comparing the proportion of one population that has certain characteristic with the proportion of the second population that has the same characteristic. Answer: TRUE Diff: 1 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 21) To test hypotheses about the equality of two population variances, the ratio of the variances of the samples from the two populations is tested using the F test. Answer: TRUE Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 22) The F test of two population variances is extremely robust to the violations of the assumption that the populations are normally distributed. Answer: FALSE Diff: 2 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 23) The F statistic is a ratio of two independent sample variances. Answer: TRUE Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 5

24) The ratio of two independent sample variances follows the F distribution. Answer: TRUE Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 25) Golf course designer Roberto Langabeer is evaluating two sites, Palmetto Dunes and Ocean Greens, for his next golf course. He wants to prove that Palmetto Dunes residents (population 1) play golf more often than Ocean Greens residents (population 2). Roberto plans to test this hypothesis using a random sample of 81 individuals from each suburb. His null hypothesis is . 2 < σ A) σ1 22 B) μ1- μ2 > 0 C) p1 - p2 = 0 D) μ1 - μ2 = 0 E) s1 - s2 = 0 Answer: D Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 26) Golf course designer Roberto Langabeer is evaluating two sites, Palmetto Dunes and Ocean Greens, for his next golf course. He wants to prove that Palmetto Dunes residents (population 1) play golf more often than Ocean Greens residents (population 2). Roberto plans to test this hypothesis using a random sample of 81 individuals from each suburb. His alternative hypothesis is . A) σ12 < σ22 B) μ1- μ2 > 0 C) p1 - p2 = 0 D) μ1 - μ2 = 0 E) s1 - s2 > 0 Answer: B Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic.

27) Golf course designer Roberto Langabeer is evaluating two sites, Palmetto Dunes and Ocean Greens, for his next golf course. He wants to prove that Palmetto Dunes residents (population 1) play golf more often than Ocean Greens residents (population 2). Roberto commissions a market survey to test this hypothesis. The market researcher used a random sample of 64 individuals from each suburb, and reported the following: 1 = 15 times per month and 2 = 14 times per month. Assume that σ1 = 2 and σ2 = 3. With α = .01, the critical z value is . A) -1.96 B) 1.96 C) -2.33 D) -1.33 E) 2.33 Answer: E Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 28) Golf course designer Roberto Langabeer is evaluating two sites, Palmetto Dunes and Ocean Greens, for his next golf course. He wants to prove that Palmetto Dunes residents (population 1) play golf more often than Ocean Greens residents (population 2). Roberto commissions a market survey to test this hypothesis. The market researcher used a random sample of 64 individuals from each suburb, and reported the following: 1 = 15 times per month and 2 = 14 times per month. Assume that σ1 = 2 and σ2 = 3. With α = .01, the observed z value is . A) 2.22 B) 12.81 C) 4.92 D) 3.58 E) 1.96 Answer: A Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic.

29) Golf course designer Roberto Langabeer is evaluating two sites, Palmetto Dunes and Ocean Greens, for his next golf course. He wants to prove that Palmetto Dunes residents (population 1) play golf more often than Ocean Greens residents (population 2). Roberto commissions a market survey to test this hypothesis. The market researcher used a random sample of 64 individuals from each suburb, and reported the following: 1 = 15 times per month and 2 = 14 times per month. Assume that σ1 = 2 and σ2 = 3. With α = .01, the appropriate decision is . A) reject the null hypothesis σ12 < σ22 B) accept the alternate hypothesis μ1- μ2 > 0 C) reject the alternate hypothesis n1 = n2 = 64 D) fail to reject the null hypothesis μ1 - μ2 = 0 E) do nothing Answer: D Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 30) Golf course designer Roberto Langabeer is evaluating two sites, Palmetto Dunes and Ocean Greens, for his next golf course. He wants to prove that Palmetto Dunes residents (population 1) play golf more often than Ocean Greens residents (population 2). Roberto commissions a market survey to test this hypothesis. The market researcher used a random sample of 64 individuals from each suburb, and reported the following: 1 = 16 times per month and 2 = 14 times per month. Assume that σ1 = 4 and σ2 = 3. With α = .01, the observed z value is . A) 18.29 B) 6.05 C) 5.12 D) 3.40 E) 3.20 Answer: E Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic.

31) Golf course designer Roberto Langabeer is evaluating two sites, Palmetto Dunes and Ocean Greens, for his next golf course. He wants to prove that Palmetto Dunes residents (population 1) play golf more often than Ocean Greens residents (population 2). Roberto commissions a market survey to test this hypothesis. The market researcher used a random sample of individuals from each suburb, and reported the following: 1 = 16 times per month and 2 = 14 times per month. Assume that σ1 = 4 and σ2 = 3. With α = .01, the appropriate decision is . A) do nothing B) reject the null hypothesis σ1 < σ2 C) accept the alternate hypothesis 1- μ2 > 0 D) reject the alternate hypothesis n1 = n2 = 64 E) do not reject the null hypothesis μ1 - μ2 = 0 Answer: C Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 32) Lucy Baker is analyzing demographic characteristics of two television programs, American Idol (population 1) and 60 Minutes (population 2). Previous studies indicate no difference in the ages of the two audiences. (The mean age of each audience is the same.) Lucy plans to test this hypothesis using a random sample of 100 from each audience. Her null hypothesis is . A) μ1 - μ2 0 B) μ1 - μ2 > 0 C) μ1 - μ2 = 0 D) μ1 - μ2 < 0 E) μ1 - μ2 < 1 Answer: C Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic.

33) Lucy Baker is analyzing demographic characteristics of two television programs, American Idol (population 1) and 60 Minutes (population 2). Previous studies indicate no difference in the ages of the two audiences. (The mean age of each audience is the same.) Lucy plans to test this hypothesis using a random sample of 100 from each audience. Her alternate hypothesis is . A) μ1 - μ2 < 0 B) μ1 - μ2 > 0 C) μ1 - μ2 = 0 D) μ1 - μ2 ≠ 0 E) μ1 - μ2 = 1 Answer: D Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 34) Lucy Baker is analyzing demographic characteristics of two television programs, American Idol (population 1) and 60 Minutes (population 2). Previous studies indicate no difference in the ages of the two audiences. (The mean age of each audience is the same.) Her staff randomly selected 100 people from each audience, and reported the following: 1 = 43 years and 2 = 45 years. Assume that σ1 = 5 and σ2 = 8. With a two-tail test and α = .05, the critical z values are . A) -1.64 and 1.64 B) -1.96 and 1.96 C) -2.33 and 2.33 D) -2.58 and 2.58 E) -2.97 and 2.97 Answer: B Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic.

35) Lucy Baker is analyzing demographic characteristics of two television programs, American Idol (population 1) and 60 Minutes (population 2). Previous studies indicate no difference in the ages of the two audiences. (The mean age of each audience is the same.) Her staff randomly selected 100 people from each audience, and reported the following: 1 = 43 years and 2 = 45 years. Assume that σ1 = 5 and σ2 = 8. Assuming a two-tail test and α = .05, the observed z value is . A) -2.12 B) -2.25 C) -5.58 D) -15.38 E) -20.68 Answer: A Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 36) Lucy Baker is analyzing demographic characteristics of two television programs, American Idol (population 1) and 60 Minutes (population 2). Previous studies indicate no difference in the ages of the two audiences. (The mean age of each audience is the same.) Her staff randomly selected 100 people from each audience, and reported the following: 1 = 43 years and 2 = 45 years. Assume that σ1 = 5 and σ2 = 8. With a two-tail test and α = .05, the appropriate decision is . A) do not reject the null hypothesis μ1 - μ2 = 0 B) reject the null hypothesis μ1 - μ2 > 0 C) reject the null hypothesis μ1 - μ2 = 0 D) do not reject the null hypothesis μ1 - μ2 < 0 E) do nothing Answer: C Diff: 3 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic.

37) A researcher wants to estimate the difference in the means of two populations. A random sample of 36 items from the first population results in a sample mean of 430. A random sample of 49 items from the second population results in a sample mean of 460. The population standard deviations are 120 for the first population and 140 for the second population. From this information, a 95% confidence interval for the difference in population means is . A) -95.90 to 35.90 B) -85.44, 25.44 C) -76.53 to 16.53 D) -102.83 to 42.43 E) 98.45 to 125.48 Answer: B Diff: 3 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. 38) A researcher is interested in testing to determine if the mean price of a casual lunch is different in the city than it is in the suburbs. The null hypothesis is that there is no difference in the population means (i.e. the difference is zero). The alternative hypothesis is that there is a difference (i.e. the difference is not equal to zero). He randomly selects a sample of 9 lunch tickets from the city population resulting in a mean of $14.30 and a standard deviation of $3.40. He randomly selects a sample of 14 lunch tickets from the suburban population resulting in a mean of $11.80 and a standard deviation $2.90. He is using an alpha value of .10 to conduct this test. Assuming that the populations are normally distributed and that the population variances are approximately equal, the degrees of freedom for this problem are . A) 23 B) 22 C) 21 D) 2 E) 1 Answer: C Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test.

39) A researcher is interested in testing to determine if the mean price of a casual lunch is different in the city than it is in the suburbs. The null hypothesis is that there is no difference in the population means (i.e. the difference is zero). The alternative hypothesis is that there is a difference (i.e. the difference is not equal to zero). He randomly selects a sample of 9 lunch tickets from the city population resulting in a mean of $14.30 and a standard deviation of $3.40. He randomly selects a sample of 14 lunch tickets from the suburban population resulting in a mean of $11.80 and a standard deviation $2.90. He is using an alpha value of .10 to conduct this test. Assuming that the populations are normally distributed and that the population variances are approximately equal, the critical t value from the table is . A) 1.323 B) 1.721 C) 1.717 D) 1.321 E) 2.321 Answer: B Diff: 2 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. 40) A researcher wishes to determine the difference in two population means. To do this, she randomly samples 9 items from each population and computes a 90% confidence interval. The sample from the first population produces a mean of 780 with a standard deviation of 240. The sample from the second population produces a mean of 890 with a standard deviation of 280. Assume that the values are normally distributed in each population. The point estimate for the difference in the means of these two populations is . A) -110 B) 40 C) -40 D) 0 E) 240 Answer: A Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test.

41) A researcher wishes to determine the difference in two population means. To do this, she randomly samples 9 items from each population and computes a 90% confidence interval. The sample from the first population produces a mean of 780 with a standard deviation of 240. The sample from the second population produces a mean of 890 with a standard deviation of 280. Assume that the values are normally distributed in each population and that the population variances are approximately equal. The critical t value used from the table for this is . A) 1.860 B) 1.734 C) 1.746 D) 1.337 E) 2.342 Answer: C Diff: 2 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. 42) A researcher believes a new diet should improve weight gain. To test his hypothesis a random sample of 10 people on the old diet and an independent random sample of 10 people on the new diet were selected. The selected people on the old diet gain an average of 5 pounds with a standard deviation of 2 pounds, while the average gain for selected people on the new diet was 8 pounds with a standard deviation of 1.5 pounds. Assume that the values are normally distributed in each population and that the population variances are approximately equal. Using α = 0.05, the critical t value used from the table for this is . A) -1.96 B) -1.645 C) -2.100 D) -3.79 E) -1.734 Answer: E Diff: 2 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test.

43) A researcher believes a new diet should improve weight gain. To test his hypothesis a random sample of 10 people on the old diet and an independent random sample of 10 people on the new diet were selected. The selected people on the old diet gain an average of 5 pounds with a standard deviation of 2 pounds, while the average gain for selected people on the new diet was 8 pounds with a standard deviation of 1.5 pounds. Assume that the values are normally distributed in each population and that the population variances are approximately equal. Using α = 0.05, the observed t value for this test is . A) -1.96 B) -1.645 C) -2.100 D) -3.79 E) -1.734 Answer: D Diff: 2 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. 44) A researcher wants to conduct a before/after study on 11 subjects to determine if a new cholesterol medication results in higher HDL cholesterol readings. The null hypothesis is that the average difference is zero while the alternative hypothesis is that the average difference is not zero. Scores are obtained on the subjects both before and after taking the medication. After subtracting the after scores from the before scores, the average difference is computed to be 2.40 with a sample standard deviation of 1.21. Assume that the differences are normally distributed in the population. The degrees of freedom for this test are . A) 11 B) 10 C) 9 D) 20 E) 2 Answer: B Diff: 1 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations.

45) A researcher wants to conduct a before/after study on 11 subjects to determine if a new cholesterol medication results in higher HDL cholesterol readings. The null hypothesis is that the average difference is zero while the alternative hypothesis is that the average difference is not zero. Scores are obtained on the subjects both before and after taking the medication. After subtracting the after scores from the before scores, the average difference is computed to be 2.40 with a sample standard deviation of 1.21. Assume that the differences are normally distributed in the population. The observed t value for this test is . A) -21.82 B) -6.58 C) -2.4 D) 1.98 E) 2.33 Answer: B Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 46) A researcher wants to conduct a before/after study on 11 subjects to determine if a new cholesterol medication results in higher HDL cholesterol readings. The null hypothesis is that the average difference is zero while the alternative hypothesis is that the average difference is not zero. Scores are obtained on the subjects both before and after taking the medication. After subtracting the after scores from the before scores, the average difference is computed to be 2.40 with a sample standard deviation of 1.21. A 0.05 level of significance is selected. Assume that the differences are normally distributed in the population. The table t value for this test is . A) 1.812 B) 2.228 C) 2.086 D) 2.262 E) 3.2467 Answer: B Diff: 1 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations.

47) A researcher is conducting a matched-pairs study. She gathers data on each pair in the study resulting in: Pair 1 2 3 4 5

Group 1 10 8 11 8 9

Group 2 12 9 11 10 12

Assume that the data are normally distributed in the population. The sample standard deviation (sd) of the differences is . A) 1.30 B) 1.14 C) 1.04 D) 1.02 E) 1.47 Answer: B Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 48) A researcher is conducting a matched-pairs study. She gathers data on each pair in the study resulting in: Pair 1 2 3 4 5

Group 1 10 8 11 8 9

Group 2 12 9 11 10 12

Assume that the data are normally distributed in the population. The degrees of freedom in this problem are . A) 4 B) 8 C) 5 D) 9 E) 3 Answer: A Diff: 1 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations.

49) A researcher is conducting a matched-pairs study. She gathers data on each pair in the study resulting in: Pair 1 2 3 4 5

Group 1 10 8 11 8 9

Group 2 12 9 11 10 12

Assume that the data are normally distributed in the population. The level of significance is selected to be 0.10. If a two-tailed test is performed, the null hypothesis would be rejected if the observed value of t is . A) less than -1.533 or greater than 1.533 B) less than -2.132 or greater than 2.132 C) less than -2.776 or greater than 2.776 D) less than -1.860 or greater than 1.860 E) less than -2.000 or greater than 2.000 Answer: B Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 50) A researcher is conducting a matched-pairs study. She gathers data on each pair in the study resulting in: Pair 1 2 3 4 5

Group 1 10 8 11 8 9

Group 2 12 9 11 10 12

Assume that the data are normally distributed in the population. The level of significance is selected to be 0.10. If the alternative hypothesis is that the average difference is greater than zero, the null hypothesis would be rejected if the observed value of t is . A) greater than 1.533 B) less than -1.533 C) greater than 2.132 D) less than -2.132 E) equal to 2.333 Answer: A Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 18

51) A researcher is estimating the average difference between two population means based on matched-pairs samples. She gathers data on each pair in the study resulting in: Pair 1 2 3 4 5

Group 1 10 8 11 8 9

Group 2 12 9 11 10 12

Assume that the data are normally distributed in the population. To obtain a 95% confidence interval, the table t value would be . A) 2.132 B) 1.86 C) 2.306 D) 2.976 E) 2.776 Answer: E Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 52) A researcher is estimating the average difference between two population means based on matched-pairs samples. She gathers data on each pair in the study resulting in: Pair 1 2 3 4 5

Group 1 10 8 11 8 9

Group 2 12 9 11 10 12

Assume that the data are normally distributed in the population. To obtain a 90% confidence interval, the table t value would be . A) 1.86 B) 1.397 C) 1.533 D) 2.132 E) 3.346 Answer: D Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 19

53) A researcher is estimating the average difference between two population means based on matched-pairs samples. She gathers data on each pair in the study resulting in: Pair 1 2 3 4 5

Group 1 10 8 11 8 9

Group 2 12 9 11 10 12

Assume that the data are normally distributed in the population. A 95% confidence interval would be . A) -3.02 to -0.18 B) -1.6 to -1.09 C) -2.11 to 1.09 D) -2.11 to -1.09 E) -3.23 to 2.23 Answer: A Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. 54) Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the East Coast Warehouse has consistently out-performed the West Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 372 orders within 24 hours. Maureen's null hypothesis is . A) p1 - p2 = 0 B) μ1 - μ2 = 0 C) p1 - p2 > 0 D) μ1 - μ2 < 0 E) μ1 - μ2 ≥ 0 Answer: A Diff: 1 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions.

55) Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the West Coast Warehouse has consistently out-performed the East Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 372 orders within 24 hours. Maureen's alternative hypothesis is . A) p1 - p2 0 B) μ1 - μ2 > 0 C) p1 - p2 > 0 D) μ1 - μ2 0 E) μ1 - μ2 ≥ 0 Answer: C Diff: 1 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 56) Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the West Coast Warehouse has consistently out-performed the East Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 372 orders within 24 hours. Assuming α = 0.05, the critical z value is . A) -1.96 B) -1.64 C) 1.64 D) 1.96 E) 2.33 Answer: C Diff: 1 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions.

57) Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the West Coast Warehouse has consistently out-performed the East Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 372 orders within 24 hours. Assuming α = 0.05, the observed z value is . A) -3.15 B) 2.42 C) 1.53 D) 0.95 E) 1.08 Answer: D Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 58) Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the West Coast Warehouse has consistently out-performed the East Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 372 orders within 24 hours. Assuming α = 0.05, the appropriate decision is . A) do not reject the null hypothesis μ1 - μ2 = 0 B) do not reject the null hypothesis p1 - p2 = 0 C) reject the null hypothesis μ1 - μ2 = 0 D) reject the null hypothesis p1 - p2 = 0 E) do not reject the null hypothesis p1 - p2 ≥ 0 Answer: B Diff: 3 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions.

59) Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the West Coast Warehouse has consistently out-performed the East Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 356 orders within 24 hours. Assuming α = 0.05, the observed z value is . A) -3.15 B) 2.42 C) 1.53 D) 0.95 E) 1.05 Answer: B Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 60) Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the West Coast Warehouse has consistently out-performed the East Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 356 orders within 24 hours. Assuming α = 0.05, the appropriate decision is . A) reject the null hypothesis p1 - p2 = 0 B) reject the null hypothesis μ1 - μ2 < 0 C) do not reject the null hypothesis μ1 - μ2 = 0 D) do not reject the null hypothesis p1 - p2 = 0 E) do not reject the null hypothesis p1 - p2 ≥ 0 Answer: A Diff: 3 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions.

61) Suppose that .06 of each of two populations possess a given characteristic. Samples of size 400 are randomly drawn from each population. The probability that the difference between the first sample proportion which possess the given characteristic and the second sample proportion which possess the given characteristic being more than +.03 is . A) 0.4943 B) 0.9943 C) 0.0370 D) 0.5057 E) 0.5700 Answer: C Diff: 3 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 62) Suppose that .06 of each of two populations possess a given characteristic. Samples of size 400 are randomly drawn from each population. The standard deviation for the sampling distribution of differences between the first sample proportion and the second sample proportion (used to calculate the z score) is . A) 0.00300 B) 0.01679 C) 0.05640 D) 0.00014 E) 0.12000 Answer: B Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 63) Suppose that .06 of each of two populations possess a given characteristic. Samples of size 400 are randomly drawn from each population. What is the probability that the differences in sample proportions will be greater than 0.02? A) 0.4535 B) 0.9535 C) 0.1168 D) 0.5465 E) 0.4650 Answer: C Diff: 3 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions.

64) A university administrator believes that business students are more likely to be working and going to school than their liberal arts majors. This information may lead to the business school offering courses in the evening hours while the liberal arts college maintains a daytime schedule. To test this theory, the proportion of business students who are working at least 20 hours per week is compared to the proportion of liberal arts students who are working at least 20 hours per week. A random sample of 600 from the business school has been taken and it is determined that 480 students work at least 20 hours per week. A random sample of 700 liberal arts students showed that 350 work at least 20 hours per week. The observed z for this is . A) 0.300 B) 0.624 C) 0.638 D) 11.22 E) 13.42 Answer: D Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 65) A researcher is interested in estimating the difference in two population proportions. A sample of 400 from each population results in sample proportions of .61 and .64. The point estimate of the difference in the population proportions is . A) -0.030 B) 0.625 C) 0.000 D) 0.400 E) 0.500 Answer: A Diff: 1 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 66) A researcher is interested in estimating the difference in two population proportions. A sample of 400 from each population results in sample proportions of .61 and .64. A 90% confidence interval for the difference in the population proportions is . A) -0.10 to 0.04 B) -0.09 to 0.03 C) -0.11 to 0.05 D) -0.07 to 0.01 E) -0.08 to 0.12 Answer: B Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 25

67) A random sample of 400 items from a population shows that 160 of the sample items possess a given characteristic. A random sample of 400 items from a second population resulted in 110 of the sample items possessing the characteristic. Using these data, a 99% confidence interval is constructed to estimate the difference in population proportions which possess the given characteristic. The resulting confidence interval is . A) 0.06 to 0.19 B) 0.05 to 0.22 C) 0.09 to 0.16 D) 0.04 to 0.21 E) 0.05 to 0.23 Answer: D Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. 68) Collinsville Construction Company purchases steel rods for its projects. Based on previous tests, Claude Carter, Quality Assurance Manager, has recommended not purchasing rods from Redding Rods, Inc. (population 1), and switching to Stockton Steel (population 2), since Stockton's rods had less variability in length. Claude has sampled the outputs from Redding's and Stockton's rod manufacturing processes. The results for Redding were s12 = 0.10 with n1 = 8, and, for Stockton, the results were s22 = 0.05 with n2 = 10. Assume that rod lengths are normally distributed in the population. Claude's null hypothesis is . A) σ12 = σ22 B) σ12 ≠ σ22 C) σ12 > σ22 D) σ12 < σ22 E) s12 < s22 Answer: A Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution.

69) Collinsville Construction Company purchases steel rods for its projects. Based on previous tests, Claude Carter, Quality Assurance Manager, has recommended not purchasing rods from Redding Rods, Inc. (population 1), and switching to Stockton Steel (population 2), since Stockton's rods had less variability in length. Claude has sampled the outputs from Redding's and Stockton's rod manufacturing processes. The results for Redding were s12 = 0.10 with n1 = 8, and, for Stockton, the results were s22 = 0.05 with n2 = 10. Assume that rod lengths are normally distributed in the population. Claude's alternative hypothesis is . 2 2 A) σ1 = σ2 B) σ12 ≠ σ22 C) σ12 > σ22 D) 1σ2 < σ22 E) s12 < s22 Answer: C Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 70) Collinsville Construction Company purchases steel rods for its projects. Based on previous tests, Claude Carter, Quality Assurance Manager, has recommended not purchasing rods from Redding Rods, Inc. (population 1), and switching to Stockton Steel (population 2), since Stockton's rods had less variability in length. Claude has sampled the outputs from Redding's and Stockton's rod manufacturing processes. The results for Redding were s12 = 0.10 with n1 = 8, and, for Stockton, the results were s22 = 0.05 with n2 = 10. Assume that rod lengths are normally distributed in the population. If α = 0.05, the critical F value is . A) 3.68 B) 3.29 C) 3.50 D) 3.79 E) 3.99 Answer: B Diff: 2 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution.

71) Collinsville Construction Company purchases steel rods for its projects. Based on previous tests, Claude Carter, Quality Assurance Manager, has recommended not purchasing rods from Redding Rods, Inc. (population 1), and switching to Stockton Steel (population 2), since Stockton's rods had less variability in length. Claude has sampled the outputs from Redding's and Stockton's rod manufacturing processes. The results for Redding were s12 = 0.10 with n1 = 8, and, for Stockton, the results were s22 = 0.05 with n2 = 10. Assume that rod lengths are normally distributed in the population. If α = 0.05, the observed F value is . A) 0.50 B) 2.00 C) 1.41 D) 0.91 E) 0.71 Answer: B Diff: 2 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 72) Collinsville Construction Company purchases steel rods for its projects. Based on previous tests, Claude Carter, Quality Assurance Manager, has recommended not purchasing rods from Redding Rods, Inc. (population 1), and switching to Stockton Steel (population 2), since Stockton's rods had less variability in length. Claude has sampled the outputs from Redding's and Stockton's rod manufacturing processes. The results for Redding were s12 = 0.10 with n1 = 8, and, for Stockton, the results were s22 = 0.05 with n2 = 10. Assume that rod lengths are normally distributed in the population. If α = 0.05, the appropriate decision is . 2 2 A) reject the null hypothesis σ1 = σ2 B) reject the null hypothesis σ12 < σ22 C) do not reject the null hypothesis σ12 = σ22 D) do not reject the null hypothesis σ12 < σ22 E) do nothing Answer: C Diff: 3 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution.

73) Collinsville Construction Company purchases steel rods for its projects. Based on previous tests, Claude Carter, Quality Assurance Manager, has recommended not purchasing rods from Redding Rods, Inc. (population 1), and switching to Stockton Steel (population 2), since Stockton's rods had less variability in length. Claude has sampled the outputs from Redding's and Stockton's rod manufacturing processes. The results for Redding were s12 = 0.15 with n1 = 8, and, for Stockton, the results were s22 = 0.04 with n2 = 10. Assume that rod lengths are normally distributed in the population. If α = 0.05, the observed F value is _ . A) 0.27 B) 0.52 C) 1.92 D) 3.75 E) 4.25 Answer: D Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 74) Collinsville Construction Company purchases steel rods for its projects. Based on previous tests, Claude Carter, Quality Assurance Manager, has recommended not purchasing rods from Redding Rods, Inc. (population 1), and switching to Stockton Steel (population 2), since Redding's rods had more variability in length. Claude has sampled the outputs from Redding's and Stockton's rod manufacturing processes. The results for Redding were s12 = 0.15 with n1 = 8, and, for Stockton, the results were s22 = 0.05 with n2 = 10. Assume that rod lengths are normally distributed in the population. If α = 0.05, the appropriate decision is . 2 2 A) reject the null hypothesis σ1 ≤ = σ2 B) reject the null hypothesis σ12 > σ22 C) do not reject the null hypothesis σ12 = σ22 D) do not reject the null hypothesis σ12 > σ22 E) do nothing Answer: C Diff: 3 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution.

75) Tamara Hill, fund manager of the Hill Value Fund, manages a portfolio of 250 common stocks. Tamara is searching for a "low risk" issue to add to the portfolio, i.e., one with a price variance less than that of the S&P 500 index. Moreover, she assumes an issue is not "low risk" until demonstrated otherwise. Her staff reported that during the last nine quarters, the price variance for the S&P 500 index (population 1) was 25, and for the last seven quarters, the price variance for XYC common (population 2) was 8. Assume that stock prices are normally distributed in the population. Using α = 0.05, Tamara's null hypothesis is . 2 2 A) σ1 = σ2 B) σ12 ≠ σ22 C) σ12 > σ22 D) σ12 < σ22 E) s12 < s22 Answer: A Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 76) Tamara Hill, fund manager of the Hill Value Fund, manages a portfolio of 250 common stocks. Tamara is searching for a "low risk" issue to add to the portfolio, i.e., one with a price variance less than that of the S&P 500 index. Moreover, she assumes an issue is not "low risk" until demonstrated otherwise. Her staff reported that during the last nine quarters, the price variance for the S&P 500 index (population 1) was 25, and for the last seven quarters, the price variance for XYC common (population 2) was 8. Assume that stock prices are normally distributed in the population. Using α = 0.05, Tamara's alternate hypothesis is . 2 2 A) σ1 = σ2 B) σ12 ≠ σ22 C) σ12 > σ22 D) σ12 < 22 E) s12 < s22 Answer: C Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution.

77) Tamara Hill, fund manager of the Hill Value Fund, manages a portfolio of 250 common stocks. Tamara is searching for a "low risk" issue to add to the portfolio, i.e., one with a price variance less than that of the S&P 500 index. Moreover, she assumes an issue is not "low risk" until demonstrated otherwise. Her staff reported that during the last nine quarters, the price variance for the S&P 500 index (population 1) was 25, and for the last seven quarters, the price variance for XYC common (population 2) was 8. Assume that stock prices are normally distributed in the population. Using α = 0.05, the critical F value is . A) 3.68 B) 3.58 C) 4.15 D) 3.29 E) 4.89 Answer: C Diff: 2 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 78) Tamara Hill, fund manager of the Hill Value Fund, manages a portfolio of 250 common stocks. Tamara is searching for a "low risk" issue to add to the portfolio, i.e., one with a price variance less than that of the S&P 500 index. Moreover, she assumes an issue is not "low risk" until demonstrated otherwise. Her staff reported that during the last nine quarters, the price variance for the S&P 500 index (population 1) was 25, and for the last seven quarters, the price variance for XYC common (population 2) was 8. Assume that stock prices are normally distributed in the population. Using α = 0.05, the observed F value is . A) 3.13 B) 0.32 C) 1.77 D) 9.77 E) 9.87 Answer: A Diff: 2 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution.

79) Tamara Hill, fund manager of the Hill Value Fund, manages a portfolio of 250 common stocks. Tamara is searching for a "low risk" issue to add to the portfolio, i.e., one with a price variance less than that of the S&P 500 index. Moreover, she assumes an issue is not "low risk" until demonstrated otherwise. Her staff reported that during the last nine quarters, the price variance for the S&P 500 index (population 1) was 25, and for the last seven quarters, the price variance for XYC common (population 2) was 8. Assume that stock prices are normally distributed in the population. Using α = 0.05, the appropriate decision is . A) reject the null hypothesis σ12 = σ22 B) reject the null hypothesis σ12 ≠ σ22 C) do not reject the null hypothesis σ12 = σ22 D) do not reject the null hypothesis σ12 ≠σ22 E) do nothing Answer: C Diff: 3 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 80) Tamara Hill, fund manager of the Hill Value Fund, manages a portfolio of 250 common stocks. Tamara is searching for a "low risk" issue to add to the portfolio, i.e., one with a price variance less than that of the S&P 500 index. Moreover, she assumes an issue is not "low risk" until demonstrated otherwise. Her staff reported that during the last nine quarters, the price variance for the S&P 500 index (population 1) was 25, and for the last seven quarters, the price variance for XYC common (population 2) was 6. Assume that stock prices are normally distributed in the population. Using α = 0.05, the observed F value is . A) 17.36 B) 2.04 C) 0.24 D) 4.77 E) 4.17 Answer: E Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution.

81) Tamara Hill, fund manager of the Hill Value Fund, manages a portfolio of 250 common stocks. Tamara is searching for a "low risk" issue to add to the portfolio, i.e., one with a price variance less than that of the S&P 500 index. Moreover, she assumes an issue is not "low risk" until demonstrated otherwise. Her staff reported that during the last nine quarters, the price variance for the S&P 500 index (population 1) was 25, and for the last seven quarters, the price variance for XYC common (population 2) was 6. Assume that stock prices are normally distributed in the population. Using α = 0.05, the appropriate decision is . A) reject the null hypothesis σ12 = σ22 B) reject the null hypothesis σ12 ≠ σ22 C) do not reject the null hypothesis σ12 = σ22 D) do not reject the null hypothesis σ12 ≠ σ22 E) maintain status quo Answer: A Diff: 3 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. 82) A researcher believes a new diet should improve weight gain. To test his hypothesis, a random sample of 10 people on the old diet and an independent random sample of 10 people on the new diet were selected. The selected people on the old diet gained an average of 5 pounds with a standard deviation of 2 pounds, while the average gain for selected people on the new diet was 8 pounds with a standard deviation of 1.5 pounds. Assuming that the values are normally distributed in each population, the researcher would like to use the t procedure with pooled standard deviation. To use this procedure, it must be shown that the variances from the two populations can be assumed to be equal. Using the sample data to test this assumption at α = 0.05, the observed F value is . A) 1.78 B) 3.79 C) -3.79 D) 3.18 E) 1.33 Answer: A Diff: 1 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution.

83) Your company is evaluating two cloud-based, secured data storage services. "Pie in the Sky," the newer service, claims its uploading and downloading speeds are faster than the older service, "Cloudy but Steady Skies." You need to make a decision based on published access times for both services at different times and for varying file sizes. Your alternative hypothesis is . 2 A) σ1 < σ22 B) μ1 - μ2 < 0 C) p1 - p2 = 0 D) μ1 - μ2 = 0 E) s1 - s2 = 0 Answer: B Diff: 1 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. Bloom's level: Application 84) Your company is evaluating two cloud-based secured data storage services. "Pie in the Sky," the newer service, claims its uploading and downloading speeds are faster than the older service, "Cloudy but Steady Skies." You need to make a decision based on published access times for both services at different times and for varying file sizes. To make your decision, you purchase a statistical study, which indicates that the average download time for Pie in the Sky is 0.77 sec. per MB and for Cloudy but Steady Skies is 0.84. Assume that σ1 = 0.2 and σ2 = 0.3. With α = .05, the critical z value is . A) 1.645 B) -1.645 C) 1.96 D) -1.96 E) 2.33 Answer: B Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. Bloom's level: Application

85) Your company is evaluating two cloud-based secured data storage services. "Pie in the Sky," the newer service, claims its uploading and downloading speeds are faster than the older service, "Cloudy but Steady Skies." You need to make a decision based on published access times for both services at different times and for varying file sizes. To make your decision, you purchase a statistical study, which indicates that the average download time for Pie in the Sky is 0.77 sec. per MB and for Cloudy but Steady Skies is 0.84. Assume that n1 = n2 = 50, σ1 = 0.2 and σ2 = 0.3. With α = .05, the observed z value is . A) -9.71 B) -1.37 C) -0.7 D) 1.37 E) 1.96 Answer: B Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. Bloom's level: Application 86) Your company is evaluating two cloud-based secured data storage services. "Pie in the Sky," the newer service, claims its uploading and downloading speeds are faster than the older service, "Cloudy but Steady Skies." You need to make a decision based on published access times for both services at different times and for varying file sizes. To make your decision, you purchase a statistical study, which indicates that average download time for Pie in the Sky is 0.77 sec. per MB and for Cloudy but Steady Skies is 0.84. Assume that n1 = n2 = 50, σ1 = 0.2 and σ2 = 0.3. With α = .05, the appropriate decision is . 2 2 A) reject the null hypothesis σ1 < σ2 B) accept the alternate hypothesis μ1 - μ2 > 0 C) reject the alternate hypothesis n1 = n2 = 50 D) fail to reject the null hypothesis μ1 - μ2 = 0 E) do nothing Answer: D Diff: 2 Response: See section 10.1 Hypothesis Testing and Confidence Intervals about the Difference in Two Means using the z Statistic (Population Variances Known) Learning Objective: 10.1: Test hypotheses and develop confidence intervals about the difference in two means with known population variances using the z statistic. Bloom's level: Application

87) You are interested in determining whether the mean price of electricity offered by solar companies is different in the southern states than in the northern states. You select a random sample of 8 solar companies from the southern states and 11 from the northern states. For the southern states, the average price is 12.2 cents per kWh (kilowatt hour) and the standard deviation is 0.8 cents per kWh. For the northern states, the average and standard deviation are 11.7 and 1.0 cents per kWh respectively. If you use a significance level α = 0.10 and assuming the values are normally distributed in both populations, the critical t value from the table is . A) 1.330 B) 1.729 C) 1.734 D) 1.740 E) 1.747 Answer: D Diff: 2 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. Bloom's level: Application 88) You are interested in determining the difference in two population means. You select a random sample of 8 items from the first population and 8 from the second population and then compute a 95% confidence interval. The sample from the first population has an average of 12.2 and a standard deviation of 0.8. The sample from the second population has an average of 11.7 and a standard deviation of 1.0. Assume that the values are normally distributed in each population. The point estimate for the difference in means of these two populations is . A) -0.2 B) 0.2 C) 0.5 D) -0.5 E) 0.06 Answer: C Diff: 1 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. Bloom's level: Application

89) You are interested in determining the difference in two population means. You select a random sample of 8 items from the first population and 8 from the second population and then compute a 95% confidence interval. The sample from the first population has an average of 12.2 and a standard deviation of 0.8. The sample from the second population has an average of 11.7 and a standard deviation of 1.0. Assume that the values are normally distributed in each population and that the population variances are approximately equal. The corresponding critical t value from the table is _ . A) 1.753 B) 1.761 C) 2.120 D) 2.131 E) 2.145 Answer: E Diff: 2 Response: See section 10.2 Hypothesis Testing and Confidence Intervals about the Difference in Two Means: Independent Samples and Population Variances Unknown Learning Objective: 10.2: Test hypotheses and develop confidence intervals about the difference in two means of independent samples with unknown population variances using the t test. Bloom's level: Application 90) You are evaluating investing in a cognitive training company. For this reason, you want to determine whether users who complete at least 75% of the recommended daily training for two months show improved levels of reading comprehension and problem-solving skills. You select a random sample of new users and get test scores for each participant in the sample. The test score is a composite of reading comprehension and problem-solving skills. Two months later, you randomly select 15 users from the original sample who have completed 75% or more of the recommended training in the last two months and have them take a test similar to the initial test. You are interested in determining whether the average test score before training is different than the average test score after training for this sample. The after-training average is 92.8, which is 2.7 points higher than the before-training average. The sample standard deviation of the differences is 1.2. You can assume that the differences are normally distributed in the population. The degrees of freedom for this test are . A) 30 B) 29 C) 28 D) 15 E) 14 Answer: E Diff: 1 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. Bloom's level: Application

91) You are evaluating investing in a cognitive training company. For this reason, you want to determine whether users who complete at least 75% of the recommended daily training for two months show improved levels of reading comprehension and problem-solving skills. You select a random sample of new users and get test scores for each participant in the sample. The test score is a composite of reading comprehension and problem-solving skills. Two months later, you randomly select 15 users from the original sample who have completed 75% or more of the recommended training in the last two months and have them take a test similar to the initial test. You are interested in determining whether the average test score before training is different than the average test score after training for this sample. The after-training average is 92.8, which is 2.7 points higher than the before-training average. The sample standard deviation of the differences is 1.2. You can assume that the differences are normally distributed in the population. The observed t value for this test is . A) 7.26 B) 8.71 C) 9.55 D) 9.81 E) 33.75 Answer: B Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. Bloom's level: Application 92) You are evaluating investing in a cognitive training company. For this reason, you want to determine whether users who complete at least 75% of the recommended daily training for two months show improved levels of reading comprehension and problem-solving skills. You select a random sample of new users and get test scores for each participant in the sample. The test score is a composite of reading comprehension and problem-solving skills. Two months later, you randomly select 15 users from the original sample who have completed 75% or more of the recommended training in the last two months and have them take a test similar to the initial test. You are interested in determining whether the average test score before training is different than the average test score after training for this sample. The after-training average is 92.8, which is 2.7 points higher than the before-training average. The sample standard deviation of the differences is 1.2. You use a significance level of 0.10, and you can assume that the differences are normally distributed in the population. The t-value from the table for this test is . A) 1.761 B) 1.746 C) 1.753 D) 1.345 E) 1.339 Answer: A Diff: 2 Response: See section 10.3 Statistical Inferences for Two Related Populations Learning Objective: 10.3: Test hypotheses and develop confidence intervals about the difference in two dependent populations. Bloom's level: Application 38

93) Suppose that the proportion of young adults who read at least one book per month is 0.15, and this proportion is the same in Boston and New York. Suppose that samples of 400 are randomly drawn from each city. The standard deviation of the differences for the sampling distribution between the first sample proportion and the second sample proportion (used to calculate the z score) is . A) 0.0459 B) 0.0435 C) 0.0402 D) 0.0335 E) 0.0252 Answer: E Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. Bloom's level: Application 94) Maxwell Cantor, VP of human resources of Asimov Aerospace Industries Inc., is reviewing the technical certifications of employees in different divisions of the company. His goal is to have at least 90% of technical employees with up-to-date certifications. The north-east division typically has maintained higher rates of up-to-date certifications than the southern division. He selects a random sample of 250 employees from the north-east division and 300 from the southern division and finds out that the number of employees with up-to-date certifications are 230 in the north-east division and 265 in the southern division. Maxwell's null hypothesis is . A) μ1 - μ2 = 0 B) p1 - p2 > 0 C) μ1 - μ2 < 0 D) μ1 - μ2 ≥ = 0 E) p1- p2 = 0 Answer: E Diff: 1 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. Bloom's level: Application

95) Maxwell Cantor, VP of human resources of Asimov Aerospace Industries Inc., is reviewing the technical certifications of employees in different divisions of the company. His goal is to have at least 90% of technical employees with up-to-date certifications. The north-east division typically has maintained higher rates of up-to-date certifications than the southern division. He selects a random sample of 250 employees from the north-east division and 300 from the southern division and finds out that the number of employees with up-to-date certifications are 230 in the north-east division and 265 in the southern division. Maxwell's alternative hypothesis is . A) μ1 - μ2 = 0 B) p1 - p2 > 0 C) μ1 - μ2 < 0 D) μ1 - μ2 > 0 E) p1 - p2 = 0 Answer: B Diff: 1 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. Bloom's level: Application 96) Maxwell Cantor, VP of human resources of Asimov Aerospace Industries Inc., is reviewing the technical certifications of employees in different divisions of the company. His goal is to have at least 90% of technical employees with up-to-date certifications. The north-east division typically has maintained higher rates of up-to-date certifications than the southern division. He selects a random sample of 250 employees from the north-east division and 300 from the southern division and finds out that the number of employees with up-to-date certifications are 230 in the north-east division and 265 in the southern division. Assuming α = 0.01, the critical z value is . A) -2.33 B) 1.645 C) 1.96 D) 2.33 E) 2.576 Answer: D Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. Bloom's level: Application

97) Maxwell Cantor, VP of human resources of Asimov Aerospace Industries Inc., is reviewing the technical certifications of employees in different divisions of the company. His goal is to have at least 90% of technical employees with up-to-date certifications. The north-east division typically has maintained higher rates of up-to-date certifications than the southern division. He selects a random sample of 250 employees from the north-east division and 300 from the southern division and finds out that the number of employees with up-to-date certifications are 230 in the north-east division and 265 in the southern division. Assuming α = 0.01, the observed z value is . A) 1.027 B) 1.219 C) 1.427 D) 1.619 E) 1.827 Answer: C Diff: 2 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. Bloom's level: Application 98) Maxwell Cantor, VP of human resources of Asimov Aerospace Industries Inc., is reviewing the technical certifications of employees in different divisions of the company. His goal is to have at least 90% of technical employees with up-to-date certifications. The north-east division typically has maintained higher rates of up-to-date certifications than the southern division. He selects a random sample of 250 employees from the north-east division and 300 from the southern division and finds out that the number of employees with up-to-date certifications are 230 in the north-east division and 265 in the southern division. Assuming α = 0.01, the appropriate decision is . A) do not reject the null hypothesis μ1 - μ2 = 0 B) do not reject the null hypothesis p1 - p2 = 0 C) reject the null hypothesis μ1 - μ2 = 0 D) reject the null hypothesis p1 - p2 = 0 E) do not reject the null hypothesis p1 - p2 > 0 Answer: B Diff: 3 Response: See section 10.4 Statistical Inferences about Two Population Proportions, p1 - p2 Learning Objective: 10.4: Test hypotheses and develop confidence intervals about the difference in two population proportions. Bloom's level: Application

99) Suppose that you can purchase a specialized electronic component from two providers. The electrical resistance of this component needs to be 0.15 ohms (Ω). Both providers offer components with a mean resistance of 0.15 Ω. You are interested in comparing the consistencies of both providers. To this end, you randomly select 15 components from the first provider and 18 from the second one. You can assume that the resistance is approximately normally distributed in the population. If you use a significance level of 0.10, the critical F value from the table for this test is . A) 1.93 B) 2.27 C) 2.33 D) 2.40 E) 2.57 Answer: C Diff: 2 Response: See section 10.5 Testing Hypotheses about Two Population Variances Learning Objective: 10.5: Test hypotheses about the difference in two population variances using the F distribution. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 11 Analysis of Variance and Design of Experiments 1) In an experimental design, classification variables are independent variables. Answer: TRUE Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 2) In an experimental design, treatment variables are response variables. Answer: FALSE Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 3) In an experimental design, a characteristic of the subjects that was present prior to the experiment and is not the result of the experimenter's manipulations or control is called a classification variable. Answer: TRUE Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 4) In an experimental design, a variable that the experimenter controls or modifies in the experiment is called a treatment variable. Answer: TRUE Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 5) An experimental design contains only independent variables. Answer: FALSE Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables.

6) Analysis of variance may be used to test the differences in the means of more than two independent populations. Answer: TRUE Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 7) In analysis of variance tests an F distribution forms the basis for making the decisions. Answer: TRUE Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 8) The statistical methods of analysis of variance assume that the populations are normally distributed. Answer: TRUE Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 9) The statistical methods of analysis of variance assume equal sample means. Answer: FALSE Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 10) Determining the table value for the F distribution requires two values for degrees of freedom. Answer: TRUE Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 11) The Tukey-Kramer procedure is based on construction of confidence intervals for each pair of treatment means at a time. Answer: FALSE Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments.

12) The Tukey-Kramer procedure allows us to simultaneously examine all pairs of population means after the ANOVA test has been completed without increasing the true α level. Answer: TRUE Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 13) A completely randomized design has been analyzed by using a one-way ANOVA. There are three treatment groups in the design, and each sample size is four. The mean for group 1 is 25.00 and for group 3 it is 27.50. MSE is 3.19. Using α = 0.05 there is a significant difference between these two groups. Answer: FALSE Diff: 3 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 14) A completely randomized design has been analyzed by using a one-way ANOVA. There are three treatment groups in the design, and each sample size is four. The mean for group 1 is 23.50 and for group 3 it is 27.50. MSE is 3.19. Using α = 0.05 there is a significant difference between these two groups. Answer: TRUE Diff: 3 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 15) If 5 groups are tested two at a time, the total number of paired comparisons is 9. Answer: FALSE Diff: 1 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 16) In a randomized complete block design the conclusion might be that blocking is not necessary. Answer: TRUE Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 3

17) The F value for treatment will always increase if we include a blocking effect. Answer: FALSE Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 18) Interaction effects in a factorial design can be analyzed in randomized block design. Answer: FALSE Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 19) An experimental design where two or more treatments are considered simultaneously is called a factorial design. Answer: TRUE Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 20) A pet adoption agency is considering the probability of a pet being adopted when considering its age and its size. A factorial design can help the agency consider the interaction effects between adoption and size. Answer: FALSE Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 21) In a two-way ANOVA test, three sets of hypotheses are tested simultaneously. Answer: TRUE Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables.

22) When considering the interaction effects, the null hypothesis is that those effects are zero. Answer: TRUE Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 23) Suppose the owners of a new bed and breakfast establishment are interested in conducting an experiment to determine the most effective advertisement strategy for increasing the number of reservations. The bed and breakfast owners intend to rotate advertisements for 12 weeks between a travel website, a travel magazine and a local billboard. Customers making reservations will be asked where they saw the advertisement. In this experiment, the dependent variable is . A) advertisement venue B) bed and breakfast establishment C) travel website D) number of reservations E) number of customer calls Answer: D Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 24) Suppose the owners of a new bed and breakfast establishment are interested in conducting an experiment to determine the most effective advertisement strategy for increasing the number of reservations. The bed and breakfast owners intend to rotate advertisements for 12 weeks between a travel website, a travel magazine and a local billboard. Customers making reservations will be asked where they saw the advertisement. In this experiment, the independent variable is . A) advertisement venue B) bed and breakfast establishment C) travel website D) number of reservations E) number of customer calls Answer: A Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables.

25) Suppose the owners of a new bed and breakfast establishment are interested in conducting an experiment to determine the most effective advertisement strategy for increasing the number of reservations. The bed and breakfast owners intend to rotate advertisements for 12 weeks between a travel website, a travel magazine and a local billboard. Customers making reservations will be asked where they saw the advertisement. In this experiment, the independent variable has how many levels? A) 1 B) 2 C) 3 D) 4 E) 0 Answer: C Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 26) Suppose the owners of a new bed and breakfast establishment are interested in conducting an experiment to determine the most effective advertisement strategy for increasing the number of reservations. The bed and breakfast owners intend to rotate advertisements for 12 weeks between a travel website, a travel magazine and a local billboard. Customers making reservations will be asked where they saw the advertisement. In this experiment, the independent variable is a . A) treatment variable B) classification variable C) experimental variable D) design variable E) research variable Answer: A Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables.

27) Suppose the owners of a new bed and breakfast establishment are interested in conducting an experiment to determine with method of transportation customers prefer to arrive at the bed and breakfast. In this experiment, they will ask customers how they traveled as well as their satisfaction with that mode of transportation. In this study, the independent variable is a . A) treatment variable B) classification variable C) experimental variable D) design variable E) research variable Answer: B Diff: 2 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 28) Which of the following factors would be considered a classification variable? A) Diets B) Exercise routines C) Heights D) Temperatures in the room E) Exercise time Answer: B Diff: 2 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 29) Which of the following would be an appropriate way for a researcher to use temperature as a classification variable? A) Increasing the room temperature each hour B) Selecting individuals from different climates C) Decreasing the room temperature each hour D) Selecting individuals based on whether they are wearing coats E) Changing the density of people in a set area Answer: B Diff: 2 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables.

30) The subcategories of independent variables are often called . A) levels B) dependents C) ANOVA D) experiments E) climates Answer: A Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 31) The ANOVA statistical technique is primarily used to determine why there are differences from item to item in the sample when looking at a key variable's . A) variance B) median C) mean D) standard deviation E) width Answer: C Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 32) In an experimental design, part of the total in the dependent variable is being explained by differences in the variable. A) width; key B) variance; key C) mean; independent D) variance; independent E) width; dependent Answer: D Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables.

33) A college administrator might want to determine if there are differences in credit hour loads based on student living arrangements as well as student grade level. In this case, the independent variable(s) is/are . A) credit hours B) credit hours and living arrangements C) living arrangements D) grade level E) living arrangements and grade level Answer: E Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 34) A college administrator might want to determine if there are differences in credit hour loads based on student living arrangements as well as student grade level. In this case, the dependent variable(s) is/are . A) credit hours B) credit hours and living arrangements C) living arrangements D) grade level E) living arrangements and grade level Answer: A Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. 35) Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children's cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. Kathleen's experimental design is a . A) factorial design B) randomized block design C) normalized block design D) completely randomized design E) fractional design Answer: D Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

36) Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children's cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. In Kathleen's experimental design "painting style" is . A) the dependent variable B) a concomitant variable C) a treatment variable D) a blocking variable E) a response variable Answer: C Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 37) Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children's cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. In Kathleen's experimental design "reduced length of stay" is . A) the dependent variable B) a concomitant variable C) a treatment variable D) a blocking variable E) a constant Answer: A Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

38) Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children's cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. Kathleen's null hypothesis is . A) μ 1 = μ 2 = μ 3 B) μ 1 ≠ μ 2 ≠ μ 3 C) μ 1 ≥ μ 2 ≥ μ 3 D) μ 1 ≤ μ 2 ≤ μ 3 E) μ 1 ≤ μ 2 ≥ μ 3 Answer: A Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 39) Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children's cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. Analysis of Kathleen's data yielded the following ANOVA table. Source of Variation Treatment Error Total

SS 33476.19 26546.18 60022.37

df 2 15 17

MS F 16738.1 9.457912 1769.745

2) Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children's cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. Analysis of Kathleen's data yielded the following ANOVA table. Source of Variation Treatment Error Total

SS 33476.19 26546.18 60022.37

df 2 15 17

MS F 16738.1 9.457912 1769.745

Using α = 0.05, the observed F value is . A) 16738.1 B) 1769.75 C) 33476.19 D) 26546.18 E) 9.457912 Answer: E Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

3) Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children's cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. Analysis of Kathleen's data yielded the following ANOVA table. Source of Variation Treatment Error Total

SS 33476.19 26546.18 60022.37

df 2 15 17

MS F 16738.1 9.457912 1769.745

Using α = 0.05, the appropriate decision is . A) reject the null hypothesis μ 1 = μ 2 = μ 3 B) reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 C) do not reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 D) do not reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 E) inconclusive Answer: A Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

42) Pate's Pharmacy, Inc. operates a regional chain of 120 pharmacies. Each pharmacy's floor plan includes a greeting card department which is relatively isolated. Sandra Royo, Marketing Manager, feels that the level of lighting in the greeting card department may affect sales in that department. She chooses three levels of lighting (soft, medium, and bright) and randomly assigns six pharmacies to each lighting level. Analysis of Sandra's data yielded the following ANOVA table. Source of Variation SS Treatment 3608.333 Error 13591.67 Total 17200

df 2 15 17

MS 1804.167 906.1111

Using α = 0.05, the critical F value is . A) 13.68 B) 19.43 C) 3.59 D) 19.45 E) 3.68 Answer: E Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 43) Pate's Pharmacy, Inc. operates a regional chain of 120 pharmacies. Each pharmacy's floor plan includes a greeting card department which is relatively isolated. Sandra Royo, Marketing Manager, feels that the level of lighting in the greeting card department may affect sales in that department. She chooses three levels of lighting (soft, medium, and bright) and randomly assigns six pharmacies to each lighting level. Analysis of Sandra's data yielded the following ANOVA table. Source of Variation SS Treatment 3608.333 Error 13591.67 Total 17200

df 2 15 17

MS 1804.167 906.1111

Using α = 0.05, the observed F value is . A) 0.5022 B) 0.1333 C) 1.9911 D) 7.5000 E) 1.000 Answer: C Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 14

44) Pate's Pharmacy, Inc. operates a regional chain of 120 pharmacies. Each pharmacy's floor plan includes a greeting card department which is relatively isolated. Sandra Royo, Marketing Manager, feels that the level of lighting in the greeting card department may affect sales in that department. She chooses three levels of lighting (soft, medium, and bright) and randomly assigns six pharmacies to each lighting level. Analysis of Sandra's data yielded the following ANOVA table. Source of Variation SS Treatment 3608.333 Error 13591.67 Total 17200

df 2 15 17

MS 1804.167 906.1111

Using α = 0.05, the appropriate decision is . A) do not reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 B) do not reject the null hypothesis μ 1 = μ 2 = μ 3 C) reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 D) reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 E) inclusive Answer: B Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 45) BigShots, Inc. is a specialty e-tailer that operates 87 catalog websites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a website may affect its sales. He chooses three levels of design style (neon, old world and sophisticated) and randomly assigns six catalog websites to each design style. Kevin's experimental design is a . A) factorial design B) randomized block design C) completely randomized design D) normalized block design E) partially randomized design Answer: C Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

46) BigShots, Inc. is a specialty e-tailer that operates 87 catalog websites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a website may affect its sales. He chooses three levels of design style (neon, old world and sophisticated) and randomly assigns six catalog websites to each design style. Kevin's null hypothesis is . A) μ 1 ≥ μ 2 ≥ μ 3 B) μ 1 ≠ μ 2 ≠ μ 3 C) μ 1 = μ 2 = μ 3 D) μ 1 ≤ μ 2 ≤ μ 3 E) μ 1 ≤ μ 2 ≥ μ 3 Answer: C Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 47) BigShots, Inc. is a specialty e-tailer that operates 87 catalog websites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a website may affect its sales. He chooses three levels of design style (neon, old world and sophisticated) and randomly assigns six catalog websites to each design style. Analysis of Kevin's data yielded the following ANOVA table. Source of Variation Between Groups Within Groups Total

SS df 68102.33 29177.67 97280

MS F 2 34051.17 17.50543 15 1945.178 17

Using α = 0.05, the appropriate decision is . A) inconclusive B) reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 C) reject the null hypothesis μ 1 = μ 2 = μ 3 D) do not reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 E) do not reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 Answer: C Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

48) BigShots, Inc. is a specialty e-tailer that operates 87 catalog websites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a website may affect its sales. He chooses three levels of design style (neon, old world and sophisticated) and randomly assigns six catalog websites to each design style. Analysis of Kevin's data yielded the following ANOVA table. Source of Variation Between Groups Within Groups Total

SS df 68102.33 29177.67 97280

MS F 2 34051.17 15 1945.178 17

Using α = 0.05, the critical F value is . A) 3.57 B) 19.43 C) 3.68 D) 19.45 E) 2.85 Answer: C Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 49) BigShots, Inc. is a specialty e-tailer that operates 87 catalog websites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a website may affect its sales. He chooses three levels of design style (neon, old world and sophisticated) and randomly assigns six catalog websites to each design style. Analysis of Kevin's data yielded the following ANOVA table. Source of Variation Between Groups Within Groups Total

SS df 68102.33 29177.67 97280

MS F 2 34051.17 15 1945.178 17

Using α = 0.05, the observed F value is . A) 0.5022 B) 0.1333 C) 1.9911 D) 17.5054 E) 22.4567 Answer: D Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

50) BigShots, Inc. is a specialty e-tailer that operates 87 catalog websites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a website may affect its sales. He chooses three levels of design style (neon, old world and sophisticated) and randomly assigns six catalog websites to each design style. Analysis of Kevin's data yielded the following ANOVA table. Source of Variation Between Groups Within Groups Total

SS df 384.3333 1359.667 1744

MS F 2 192.1667 15 90.64444 17

Using α = 0.05, the appropriate decision is . A) do not reject the null hypothesis μ 1 = μ 2 = μ3 B) do not reject the null hypothesis μ 1 ≠ μ 2 ≠ μ3 C) reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 D) reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 E) do nothing Answer: A Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 51) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. Cindy's experimental design is a . A) factorial design B) randomized block design C) completely randomized design D) normalized block design E) incomplete block design Answer: C Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

52) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. In Cindy's experiment, "average collection period" is . A) the dependent variable B) a treatment variable C) a blocking variable D) a concomitant variable E) a constant Answer: A Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 53) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. In Cindy's experiment, "sales discount rate" is . A) the dependent variable B) a treatment variable C) a blocking variable D) a concomitant variable E) a constant Answer: B Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 54) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. Cindy's null hypothesis is . A) μ1 = μ2 = μ3 = μ4 = μ5 B) μ1 ≠ μ2 ≠μ3 ≠ μ4 ≠ μ5 C) μ1 ≠ μ2 ≠ μ3 ≠ μ4 D) μ1 = μ2 = μ3 = μ4 E) μ1 ≠ μ2 = μ3 = μ4 Answer: D Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 19

55) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. An analysis of Cindy's data produced the following ANOVA table. Source of Variation Treatment Error Total

SS 1844.2 1299.6 3143.8

df 3 16 19

MS F 614.7333 7.568277 81.225

Using α = 0.01, the appropriate decision is . A) reject the null hypothesis μ1 = μ2 = μ3 = μ4 B) reject the null hypothesis μ1 ≠ μ2 ≠ μ3 ≠ μ4 C) do not reject the null hypothesis μ1 = μ2 = μ3 = μ4 = μ5 D) do not reject the null hypothesis μ1 ≠ μ2 ≠ μ3 ≠μ4 ≠ μ5 E) do nothing Answer: A Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 56) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. An analysis of Cindy's data produced the following ANOVA table. Source of Variation Treatment Error Total

SS 5.35 177.2 182.55

df 3 16 19

MS F 1.783333 11.075

Using α = 0.01, the critical F value is . A) 5.33 B) 6.21 C) 0.16 D) 5.29 E) 6.89 Answer: D Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 20

57) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. An analysis of Cindy's data produced the following ANOVA table. Source of Variation Treatment Error Total

SS 5.35 177.2 182.55

df 3 16 19

MS F 1.783333 11.075

Using α = 0.01, the observed F value is . A) 6.2102 B) 0.1610 C) 0.1875 D) 5.3333 E) 4.9873 Answer: B Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 58) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. An analysis of Cindy's data produced the following ANOVA table. Source of Variation Treatment Error Total

SS 5.35 177.2 182.55

df 3 16 19

MS F 1.783333 11.075

59) Suppose a researcher sets up a design in which there are five different treatments and a total of 32 measurements in the study. For alpha = .01, the critical F value is . A) 3.75 B) 3.78 C) 4.07 D) 4.11 E) 4.91 Answer: D Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 60) Data from a completely randomized design are shown in the following table.

1 27 26 23 24

Treatment Level 2 3 26 27 22 29 21 27 23 26

For a one-way ANOVA, the Total Sum of Squares (SST) is . A) 36.17 B) 28.75 C) 64.92 D) 18.03 E) 28.04 Answer: C Diff: 3 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

61) Data from a completely randomized design are shown in the following table 1 27 26 23 24

Treatment Level 2 3 26 27 22 29 21 27 23 26

For a one-way ANOVA, the Between Sum of Squares (SSC is . A) 36.17 B) 28.75 C) 64.92 D) 18.03 E) 28.04 Answer: A Diff: 3 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 62) Data from a completely randomized design are shown in the following table.

1 27 26 23 24

Treatment Level 2 3 26 27 22 29 21 27 23 26

For a one-way ANOVA, the Error Sum of Squares (SSE) is . A) 36.17 B) 28.75 C) 64.92 D) 18.03 E) 28.04 Answer: B Diff: 3 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

63) Data from a completely randomized design are shown in the following table. 1 27 26 23 24

Treatment Level 2 3 26 27 22 29 21 27 23 26

For a one-way ANOVA using α = 0.05, the critical F value is . A) 3.86 B) 3.59 C) 19.38 D) 4.26 E) 6.8 Answer: D Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 64) Data from a completely randomized design are shown in the following table.

1 27 26 23 24

Treatment Level 2 3 26 27 22 29 21 27 23 26

For a one-way ANOVA using α = 0.05, the observed F value is . A) 5.66 B) 3.19 C) 18.08 D) 4.34 E) 8.98 Answer: A Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

65) Data from a completely randomized design are shown in the following table. 1 27 26 23 24

Treatment Level 2 3 26 27 22 29 21 27 23 26

For a one-way ANOVA using α = 0.05, the appropriate decision is . A) do not reject the null hypothesis μ1 ≥ μ2 ≥ μ3 B) do not reject the null hypothesis μ1 ≤ μ2 ≤ μ3 C) reject the null hypothesis μ1 = μ2 = μ3 D) reject the null hypothesis μ1 ≠ μ2 ≠ μ3 E) do not reject the null hypothesis μ1 ≠ μ2 ≠ μ3 Answer: C Diff: 3 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 66) For the following ANOVA table, the dfTreatment value is Source of Variation Treatment Error Total

SS 150 40

20 23

A) 3 B) 43 C) 1.15 D) 460 E) 150 Answer: A Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

67) For the following ANOVA table, the MS Treatment value is Source of Variation Treatment Error Total

SS 150 40

20 23

A) 150 B) 50 C) 450 D) 3.49 E) 40 Answer: B Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 68) For the following ANOVA table, the MS Error value is Source of Variation Treatment Error Total

SS 150 40

. F

20 23

A) 20 B) 60 C) 800 D) 2 E) 200 Answer: D Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

69) For the following ANOVA table, the observed F value is Source of Variation Treatment Error Total

SS 150 40

. F

20 23

A) 0.5625 B) 50 C) 25 D) 0.02 E) 0.09 Answer: C Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 70) For the following ANOVA table, the dfError value is Source of Variation Treatment Error Total

SS 360 440

df 4

. F

A) 4 B) 20 C) 12 D) 64 E) 16 Answer: C Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

71) For the following ANOVA table, the MS Treatment value is Source of Variation Treatment Error Total

SS 360 440

df 4

A) 20 B) 200 C) 76 D) 84 E) 360 Answer: A Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 72) For the following ANOVA table, the MS Error value is Source of Variation Treatment Error Total

SS 360 440

df 4

. F

A) 4,320 B) 372 C) 348 D) 30 E) 4 Answer: D Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance.

73) For the following ANOVA table, the observed F value is Source of Variation Treatment Error Total

SS 360 440

df 4

. F

A) 0.67 B) 1.50 C) 6.00 D) 5.00 E) 4.00 Answer: A Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. 74) For the following ANOVA table, the critical value of the studentized range distribution using α = 0.05 is . Source of Variation Treatment Error Total

SS 36.17 28.75 64.92

df 2 9 11

MS 18.08 3.19

F 5.66

A) 1.86 B) 3.95 C) 9.17 D) 1.65 E) 1.79 Answer: B Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments.

75) For the following ANOVA table, the HSD value, assuming equal sample sizes and using α = 0.05 is . Source of Variation Treatment Error Total

SS 36.17 28.75 64.92

df 2 9 11

MS 18.08 3.19

F 5.66

A) 1.86 B) 3.94 C) 3.19 D) 1.645 E) 3.52 Answer: E Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 76) For the following ANOVA table, the critical value of the studentized range distribution using α = 0.01 is . Source of Variation Treatment Error Total

SS 0.1233 0.5904 0.7137

df 2 15 17

MS 0.06165 0.03936

F 1.566311

A) 3.01 B) 3.67 C) 4.17 D) 4.83 E) 5.25 Answer: D Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments.

77) For the following ANOVA table, the HSD value, assuming equal sample sizes and using α = 0.01, is . Source of Variation Treatment Error Total

SS 0.1233 0.5904 0.7137

df 2 15 17

MS 0.06165 0.03936

F 1.566311

A) 0.39 B) 0.55 C) 0.48 D) 0.43 E) 0.68 Answer: A Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 78) Posteriori pairwise comparisons are only made in situations where . A) there was a significant F value and it is after the experiment B) there was not a significant F value and it is after the experiment C) there was a significant F value and it is before the experiment D) the analyst wants to compare any two samples after the experiment E) there was not a significant F value and it is before the experiment Answer: A Diff: 1 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 79) The primary difference between when Tukey's HSD test and when Tukey-Kramer procedures should be used is . A) how many samples are being compared with each other B) the complexity of the calculations C) whether the variances are equal across all groups D) whether the sample sizes are equal E) which the researcher believes will provide the most accurate outcome Answer: D Diff: 1 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 31

80) If the confidence interval for the difference of means contains zero, then it can be concluded that . A) there is a significant difference in the variances B) there is not a significant difference in the means C) there is a significant difference in the means D) the groups are the same on all measures E) there is not a significant difference in the variances Answer: B Diff: 1 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 81) The results of an experiment comparing the cycle lengths of three different brands of washing machines (N = 33) indicated an overall mean square error of 2.58. The researcher believes that the brands 1 and 2 may be the most different as they have mean cycles of 31.8 (n = 12) and 35.9 (n = 10). Using an alpha of 0.05, what would be the q obtained for this problem? A) 3.44 B) 3.80 C) 2.86 D) 3.49 E) 3.82 Answer: D Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 82) The results of an experiment comparing the cycle lengths of three different brands of washing machines (N = 33) indicated an overall mean square error of 2.58. The researcher believes that the brands 1 and 2 may be the most different as they have mean cycles of 31.8 (n = 12) and 35.9 (n = 10). Using an alpha of 0.05, what would be the result of the Tukey-Kramer formula? A) 3.49 B) 0.64 C) 1.70 D) 1.49 E) 3.82 Answer: C Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments.

83) The results of an experiment comparing the cycle lengths of three different brands of washing machines (N = 33) indicated an overall mean square error of 2.58. The researcher believes that the brands 1 and 2 may be the most different as they have mean cycles of 31.8 (n = 12) and 35.9 (n = 10). Using an alpha of 0.05, would the research find evidence of a significant difference in these brands? A) Yes, as the difference in the means is larger than the critical value B) No, as the critical value is smaller than the overall sample size C) Yes, as the difference in the means is smaller than the critical value D) No, as the difference in the means is larger than the critical value E) No, as the difference in the means is smaller than the critical value Answer: A Diff: 3 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 84) A manufacturer of heaters wants to determine if the height of the heater warms a room more quickly. Four different heights were studied. The shortest heater group had n = 12 with an average time to heat the room of 15.9 minutes. The tallest heater group had n = 10 with an average time to heat the room of 17.3 minutes. Using an alpha of 0.01, MSE of 2.88, and N = 44, what would be the critical value of the studentized range distribution obtained for this problem? A) 4.80 B) 3.74 C) 4.70 D) 4.60 E) 3.70 Answer: C Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments.

85) A manufacturer of heaters wants to determine if the height of the heater warms a room more quickly. Four different heights were studied. The shortest heater group had n = 12 with an average time to heat the room of 15.9 minutes. The tallest heater group had n = 10 with an average time to heat the room of 17.3 minutes. Using an alpha of 0.01, MSE of 2.88, and N = 44, what would be the result of the Tukey-Kramer formula? A) 4.70 B) 2.01 C) 1.24 D) 1.40 E) 2.41 Answer: E Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 86) A manufacturer of heaters wants to determine if the height of the heater warms a room more quickly. Four different heights were studied. The shortest heater group had n = 12 with an average time to heat the room of 15.9 minutes. The tallest heater group had n = 10 with an average time to heat the room of 17.3 minutes. Using an alpha of 0.01, MSE of 2.88, and N = 44, would the research find evidence of a significant difference in these brands? A) Yes, as the difference in the means is larger than the critical value B) No, as the critical value is smaller than the overall sample size C) Yes, as the difference in the means is smaller than the critical value D) No, as the difference in the means is larger than the critical value E) No, as the difference in the means is smaller than the critical value Answer: E Diff: 3 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 87) If an experiment had a total of 7 groups, how many pairs of groups could be test with a pairwise comparison? A) 14 B) 49 C) 7 D) 21 E) 42 Answer: D Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. 34

88) BigShots, Inc. is a specialty e-tailer that operates 87 catalog websites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a website may affect its sales. He chooses three levels of design style (neon, old world and sophisticated) and randomly assigns six catalog websites to each design style. In Kevin's experiment "sales at a website" is . A) a blocking variable B) a concomitant variable C) a treatment variable D) the dependent variable E) the independent variable Answer: D Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 89) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%). Cindy wants to control for the size of the customer but not to test for it as the main variable, so she classified DCI's credit customers into three categories by total assets (small, medium, and large). Then, she randomly assigned four customers from each category to a sales discount rate. Cindy's experimental design is a . A) normalized block design B) completely randomized design C) factorial design D) randomized block design E) partially randomized design Answer: D Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

90) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount level offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%). First, she classified DCI's credit customers into three categories by total assets (small, medium, and large). Then, she randomly assigned four customers from each category to a sales discount rate. In Cindy's experiment "average collection period" is . A) a concomitant variable B) the dependent variable C) a treatment variable D) a blocking variable E) a constant Answer: B Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 91) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%). Cindy wants to control for the size of the customer but not to test for it as the main variable, so she classified DCI's credit customers into three categories by total assets (small, medium, and large). Then, she randomly assigned four customers from each category to a sales discount rate. In Cindy's experiment "total asset size of credit customer" is . A) a surrogate variable B) the dependent variable C) a blocking variable D) a treatment variable E) a constant Answer: C Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

92) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%). First, she classified DCI's credit customers into three categories by total assets (small, medium, and large). Then, she randomly assigned four customers from each category to a sales discount rate. In Cindy's experiment "sales discount rate" is . A) a surrogate variable B) the dependent variable C) a blocking variable D) a treatment variable E) a constant Answer: D Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 93) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%). First, she classified DCI's credit customers into three categories by total assets (small, medium, and large). Then, she randomly assigned four customers from each category to a sales discount rate. Cindy's null hypothesis is . A) μ 1 ≠ μ 2 ≠ μ 3 ≠ μ 4 B) μ 1 ≥ μ 2 ≥ μ 3 ≥ μ 4 C) μ 1 = μ 2 = μ 3 = μ 4 D) μ 1 ≤ μ 2 ≤ μ 3 ≤ μ 4 E) μ 1 ≤ μ 2 ≥ μ 3 ≤ μ 4 Answer: C Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

8) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%). First, she classified DCI's credit customers into three categories by totalassets (small, medium, and large). Then, she randomly assigned four customers from each category to a sales discount rate. An analysis of Cindy's data yielded the following ANOVA table. Source of Variation SS Treatment 64.91667 Block 10.5 Error 14.83333 Total 90.25

df 3 2 6 11

MS F 21.63889 8.752809 5.25 2.123596 2.472222

Using α = 0.05, the appropriate decision for treatment effects is . A) reject the null hypothesis μ 1 = μ 2 = μ 3 = μ 4 B) reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 ≠ μ 4 C) do not reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 ≥ μ 4 D) do not reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 ≤ μ 4 E) do nothing Answer: A Diff: 2 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

9) Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount rate offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%). First, she classified DCI's credit customers into three categories by totalassets (small, medium, and large). Then, she randomly assigned four customers from each category to a sales discount rate. An analysis of Cindy's data yielded the following ANOVA table. Source of Variation SS Treatment 64.91667 Block 10.5 Error 14.83333 Total 90.25

df 3 2 6 11

MS F 21.63889 8.752809 5.25 2.123596 2.472222

Using α = 0.05, the appropriate decision for block effects is . A) do not reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 B) do not reject the null hypothesis μ 1 = μ 2 = μ 3 C) reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 D) reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 E) do nothing Answer: B Diff: 2 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

96) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

The Total Sum of Squares (SST) is . A) 4.67 B) 12 C) 2.33 D) 28.67 E) 11 Answer: D Diff: 3 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 97) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

The Treatment Sum of Squares (SSC) is . A) 4.67 B) 12 C) 2.33 D) 28.67 E) 11 Answer: B Diff: 3 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

98) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

The Blocks Sum of Squares (SSR) is . A) 4.67 B) 12 C) 2.33 D) 28.67 E) 11 Answer: A Diff: 3 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 99) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

The Error Sum of Squares (SSE) is . A) 4.67 B) 12 C) 2.33 D) 28.67 E) 11 Answer: B Diff: 3 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

100) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

Using α = 0.05, the critical F value for the treatments null hypothesis is . A) 3.59 B) 4.76 C) 3.98 D) 5.14 E) 9.89 Answer: B Diff: 2 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 101) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

Using α = 0.05, the observed F value for the treatments null hypothesis is . A) 5.14 B) 0.37 C) 1.17 D) 0.22 E) 2.00 Answer: E Diff: 3 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

102) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

Using α = 0.05, the appropriate decision for the treatments is . A) do not reject the null hypothesis μ 1 = μ 2 = μ 3 B) do not reject the null hypothesis μ 1 = μ 2 = μ 3 = μ 4 C) do not reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 ≥ μ 4 D) do not reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 E) do nothing Answer: B Diff: 3 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 103) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

Using α = 0.05, the critical F value for the blocking effects null hypothesis is . A) 3.59 B) 4.76 C) 3.98 D) 5.14 E) 6.54 Answer: D Diff: 2 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

104) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

With α = 0.05, the observed F value for the blocking effects null hypothesis is . A) 0.37 B) 5.14 C) 1.17 D) 2.33 E) 2.00 Answer: C Diff: 3 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. 105) Data from a randomized block design are shown in the following table.

Block 1 Block 2 Block 3

1 8 6 7

Treatment Levels 2 3 5 10 6 9 8 8

4 7 5 9

Using α = 0.05, the appropriate decision for the blocking effects is . A) reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 B) do not reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 C) do not reject the null hypothesis μ 1 = μ 2 = μ 3 D) reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 E) do nothing Answer: C Diff: 3 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables.

106) While reviewing staffing plans for a new pilot plant, Colin Chenaux, VP of Operations at Clovis Chemicals, Inc., designed an experiment to test the effects of "supervisor's style" and "training method" on the productivity of operators. The treatment levels were: (1) authoritarian, and participatory for supervisor's style, and (2) technical manuals, training films, and multimedia for training method. Three qualified applicants were randomly selected and assigned to each of the six cells. Colin's experimental design is . A) randomized block design B) normalized block design C) completely randomized design D) factorial design E) fractional design Answer: D Diff: 2 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 107) While reviewing staffing plans for a new pilot plant, Colin Chenaux, VP of Operations at Clovis Chemicals, Inc., designed an experiment to test the effects of "supervisor's style" and "training method" on the productivity of operators. The treatment levels were: (1) authoritarian, and participatory for supervisor's style, and (2) technical manuals, training films, and multimedia for training method. Three qualified applicants were randomly selected and assigned to each of the six cells. In Colin's experiment, "operator productivity" is . A) a concomitant variable B) a treatment variable C) the dependent variable D) a blocking variable E) a constant Answer: C Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables.

108) While reviewing staffing plans for a new pilot plant, Colin Chenaux, VP of Operations at Clovis Chemicals, Inc., designed an experiment to test the effects of "supervisor's style" and "training method" on the productivity of operators. The treatment levels were: (1) authoritarian, and participatory for supervisor's style, and (2) technical manuals, training films, and multimedia for training method. Three qualified applicants were randomly selected and assigned to each of the six cells. In Colin's experiment, "training method" is . A) a treatment variable B) a surrogate variable C) the dependent variable D) a blocking variable E) a constant Answer: A Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 109) While reviewing staffing plans for a new pilot plant, Colin Chenaux, VP of Operations at Clovis Chemicals, Inc., designed an experiment to test the effects of "supervisor's style" and "training method" on the productivity of operators. The treatment levels were: (1) authoritarian, and participatory for supervisor's style, and (2) technical manuals, training films, and multimedia for training method. Three qualified applicants were randomly selected and assigned to each of the six cells. In Colin's experiment, "supervisor's style" is . A) the dependent variable B) a blocking variable C) a treatment variable D) a surrogate variable E) a constant Answer: C Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables.

110) While reviewing staffing plans for a new pilot plant, Colin Chenaux, VP of Operations at Clovis Chemicals, Inc., designed an experiment to test the effects of "supervisor's style" and "training method" on the productivity of operators. The treatment levels were: (1) authoritarian, and participatory for supervisor's style, and (2) technical manuals, training films, and multimedia for training method. Three qualified applicants were randomly selected and assigned to each of the six cells. Colin's null hypothesis for training methods is . A) μ 1 = μ 2 = μ 3 B) μ 1 ≠ μ 2 ≠ μ 3 C) μ 1 ≥ μ 2 ≥ μ 3 D) μ 1 ≤ μ 2 ≤ μ 3 E) μ 1 ≤ μ 2 ≥ μ 3 Answer: A Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 111) While reviewing staffing plans for a new pilot plant, Colin Chenaux, VP of Operations at Clovis Chemicals, Inc., designed an experiment to test the effects of "supervisor's style" and "training method" on the productivity of operators. The treatment levels were: (1) authoritarian, and participatory for supervisor's style, and (2) technical manuals, training films, and multimedia for training method. Three qualified applicants were randomly selected and assigned to each of the six cells. Analysis of Colin's data produced the following ANOVA table. Source of Variation Rows (supervisor's style) Column (training method) Interaction Within Total

SS 410.8889 120.7778 2.111111 109.3333 643.1111

df 1 2 2 12 17

MS F 410.8889 45.09756 60.38889 6.628049 1.055556 0.115854 9.111111

Using α = .05, the appropriate decision for "training method" effects is . A) reject the null hypothesis μ 1 = μ 2 = μ 3 B) reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 C) do not reject the null hypothesis μ 1 = μ 2 D) do not reject the null hypothesis μ 1 ≠ μ 2 E) do nothing Answer: A Diff: 2 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables.

112) While reviewing staffing plans for a new pilot plant, Colin Chenaux, VP of Operations at Clovis Chemicals, Inc., designed an experiment to test the effects of "supervisor's style" and "training method" on the productivity of operators. The treatment levels were: (1) authoritarian, and participatory for supervisor's style, and (2) technical manuals, training films, and multimedia for training method. Three qualified applicants were randomly selected and assigned to each of the six cells. Analysis of Colin's data produced the following ANOVA table. Source of Variation Rows (supervisor's style) Column (training method) Interaction Within Total

SS 410.8889 120.7778 2.111111 109.3333 643.1111

df 1 2 2 12 17

MS F 410.8889 45.09756 60.38889 6.628049 1.055556 0.115854 9.111111

Using α = .05, the appropriate decision for "supervisor's style" effects is . A) reject the null hypothesis μ 1 = μ 2 = μ 3 B) do not reject the null hypothesis μ 1 = μ 2 = μ 3 C) reject the null hypothesis μ 1 = μ 2 D) do not reject the null hypothesis μ 1 = μ 2 E) do nothing Answer: C Diff: 2 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 113) BigShots, Inc. is a specialty e-tailer that operates 87 catalog websites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a website may affect its sales. He chooses three levels of design style (neon, old world and sophisticated) and randomly assigns six catalog websites to each design style. In Kevin's experiment "style" is . A) the dependent variable B) a treatment variable C) a concomitant variable D) a blocking variable E) a response variable Answer: B Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables.

114) Which of the following statements is correct about factorial designs? A) If there are no main effects, then there are no interactions. B) If there are interactions, then there is at least one main effect. C) If there are main effects, then there is at least one interaction. D) If there are both interactions and main effects, the interactions should be interpreted first. Answer: D Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 115) A pet trainer wants to test the effects of "treats" and "voice tone" on the speed with which dogs are trained to roll over. For "treats," the trainer used pieces of hot dogs, biscuits, and bacon flavored treats. For "voice tone," the trainer used sing-song, high pitched, and low pitched. Six dogs were randomly assigned to each of the nine cells and the analysis produced the following ANOVA table. Source of Variation Rows (treats) Column (voice tone) Interaction

SS 3.5926 14.8148 19.1852

df 2 2 4

MS 1.79296 7.40741 4.79296

F 0.27 1.11 0.72

In this experiment, "voice tone" would be considered . A) an independent variable B) a control variable C) the experimental variable D) a treatment E) a surrogate variable Answer: D Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables.

116) A pet trainer wants to test the effects of "treats" and "voice tone" on the speed with which dogs are trained to roll over. For "treats," the trainer used pieces of hot dogs, biscuits, and bacon flavored treats. For "voice tone," the trainer used sing-song, high pitched, and low pitched. Six dogs were randomly assigned to each of the nine cells and the analysis produced the following ANOVA table. Source of Variation Rows (treats) Column (voice tone) Interaction

SS 3.5926 14.8148 19.1852

df 2 2 4

MS 1.79296 7.40741 4.79296

F 0.27 1.11 0.72

Using α = .05, the appropriate decision for "interaction" effect is that . A) a significant interaction is evident and possible to examine the two primary effects B) no significant interaction is evident and not possible to examine the two primary effects C) no significant interaction, so more analysis is needed D) reject the null hypothesis μ 1 ≠ μ 2 E) no significant interaction is evident and possible to examine the two primary effects Answer: E Diff: 2 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables.

117) A pet trainer wants to test the effects of "treats" and "voice tone" on the speed with which dogs are trained to roll over. For "treats," the trainer used pieces of hot dogs, biscuits, and bacon flavored treats. For "voice tone," the trainer used sing-song, high pitched, and low pitched. Six dogs were randomly assigned to each of the nine cells and the analysis produced the following ANOVA table. Source of Variation Rows (treats) Column (voice tone) Interaction

SS 3.5926 14.8148 19.1852

df 2 2 4

MS 1.79296 7.40741 4.79296

F 0.27 1.11 0.72

Using α = .05, the appropriate decision for "treats" effect is . A) reject the null hypothesis μ 1 = μ 2 = μ 3 B) reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 C) do not reject the null hypothesis μ 1 = μ 2 = μ 3 D) do not reject the null hypothesis μ 1 ≠ μ 2 E) do nothing Answer: C Diff: 2 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables.

5 117

118) A pet trainer wants to test the effects of "treats" and "voice tone" on the speed with which dogs are trained to roll over. For "treats," the trainer used pieces of hot dogs, biscuits, and bacon flavored treats. For "voice tone," the trainer used sing-song, high pitched, and low pitched. Six dogs were randomly assigned to each of the nine cells and the analysis produced the following ANOVA table. Source of Variation Rows (treats) Column (voice tone) Interaction

SS 3.5926 14.8148 19.1852

df 2 2 4

MS 1.79296 7.40741 4.79296

F 0.27 1.11 0.72

Using α = .05, the appropriate decision for "voice tone" effect is . A) reject the null hypothesis μ 1 = μ 2 = μ 3 B) reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 C) do not reject the null hypothesis μ 1 = μ 2 = μ 3 D) do not reject the null hypothesis μ 1 ≠ μ 2 E) do nothing Answer: C Diff: 2 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. 119) The head of the math department at a local community college is interested in conducting an experiment to determine effective teaching strategies for improving student math comprehension and math literacy. In particular, she is interested in a new pedagogical model called a "flipped classroom," in which the typical lecture and homework elements of the course are reversed. Short video lectures are viewed by students at home before the class session, while in-class time is devoted to exercises, projects, or discussions. For this purpose, the math head will have half of the sections of calculus II this coming semester be taught in the traditional setting, while the other half will use the flipped classroom model. In this experiment, the dependent variable is: . A) the instructor selected for each section B) the community college C) the math comprehension and literacy attained by students D) the pedagogical model used E) the number of students in each section Answer: C Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. Bloom's level: Application

5 118

120) The head of the math department at a local community college is interested in conducting an experiment to determine effective teaching strategies for improving student math comprehension and math literacy. In particular, she is interested in a new pedagogical model called a "flipped classroom," in which the typical lecture and homework elements of the course are reversed. Short video lectures are viewed by students at home before the class session, while in-class time is devoted to exercises, projects, or discussions. For this purpose, the math head will have half of the sections of calculus II this coming semester be taught in the traditional setting, while the other half will use the flipped classroom model. In this experiment, the independent variable is: . A) the instructor selected for each section B) the community college C) the math comprehension and literacy attained by students D) the pedagogical model used E) the number of students in each section Answer: D Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. Bloom's level: Application 121) The head of the math department at a local community college is interested in conducting an experiment to determine effective teaching strategies for improving student math comprehension and math literacy. In particular, she is interested in a new pedagogical model called a "flipped classroom," in which the typical lecture and homework elements of the course are reversed. Short video lectures are viewed by students at home before the class session, while in-class time is devoted to exercises, projects, or discussions. For this purpose, the math head will have half of the sections of calculus II this coming semester be taught in the traditional setting, while the other half will use the flipped classroom model. In this experiment, the independent variable has levels. A) 0 B) 0.5 C) 1 D) 1.5 E) 2 Answer: E Diff: 1 Response: See section 11.1 Introduction to Design of Experiments Learning Objective: 11.1: Describe an experimental design and its elements, including independent variables–both treatment and classification–and dependent variables. Bloom's level: Application

5 119

122) As director of the employee wellness and productivity program in your company, you are interested in comparing the effects of strength training, aerobic training, and yoga on decreasing rates of injury and absenteeism. The company has 9 divisions with roughly the same number of employees, and you randomly assign 3 divisions to participate in strength training, 3 to aerobic training, and 3 to yoga. Your null hypothesis is . A) μ 1 ≠ μ 2 ≠ μ 3 B) μ 1 ≥ μ 2 ≥ μ 3 C) μ 1 ≤ μ 2 ≤ μ 3 D) μ 1 = μ 2 = μ 3 E) μ 1 ≤ μ 2 ≥ μ 3 Answer: D Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. Bloom's level: Application 123) As director of the employee wellness and productivity program in your company, you are interested in comparing the effects of strength training, aerobic training, and yoga on decreasing rates of injury and absenteeism. The company has 9 divisions with roughly the same number of employees, and you randomly assign 3 divisions to participate in strength training, 3 to aerobic training, and 3 to yoga. Your alternative hypothesis is . A) μ 1 ≠ μ 2 ≠ μ 3 B) μ 1 ≥ μ 2 ≥ μ 3 C) μ 1 ≤ μ 2 ≤ μ 3 D) μ 1 = μ 2 = μ 3 E) at least one of the means is different from the others Answer: E Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. Bloom's level: Application

5 120

124) As director of the employee wellness and productivity program in your company, you are interested in comparing the effects of strength training, aerobic training, and yoga on decreasing rates of injury and absenteeism. The company has 18 divisions with roughly the same number of employees, and you randomly assign 6 divisions to participate in strength training, 6 to aerobic training, and 6 to yoga. Analysis of the data yielded the following ANOVA table. Source of Variation Treatment Error Total

SS 33476.19 26546.18 60022.37

df 2 15 17

MS 16738.1 1769.745

F 9.457912

Using α = 0.05, the critical F value is . A) 3.68 B) 3.74 C) 4.54 D) 4.60 E) 9.46 Answer: A Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. Bloom's level: Application 125) As director of the employee wellness and productivity program in your company, you are interested in comparing the effects of strength training, aerobic training, and yoga on decreasing rates of injury and absenteeism. The company has 18 divisions with roughly the same number of employees, and you randomly assign 6 divisions to participate in strength training, 6 to aerobic training, and 6 to yoga. Analysis of the data yielded the following ANOVA table. Source of Variation Treatment Error Total

SS 33476.19 26546.18 60022.37

df 2 15 17

MS 16738.1 1769.745

F 9.457912

Using α = 0.05, the observed F value is . A) 60022.37 B) 16738.1 C) 1769.745 D) 9.457912 E) 18 Answer: D Diff: 1 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. Bloom's level: Application 5 121

126) As director of the employee wellness and productivity program in your company, you are interested in comparing the effects of strength training, aerobic training, and yoga on decreasing rates of injury and absenteeism. The company has 18 divisions with roughly the same number of employees, and you randomly assign 6 divisions to participate in strength training, 6 to aerobic training, and 6 to yoga. Analysis of the data yielded the following ANOVA table. Source of Variation Treatment Error Total

SS 33476.19 26546.18 60022.37

df 2 15 17

MS 16738.1 1769.745

F 9.457912

Using α = 0.05, the appropriate decision is . A) reject the null hypothesis μ 1 ≠ μ 2 ≠ μ 3 B) do not reject the null hypothesis μ 1 ≥ μ 2 ≥ μ 3 C) reject the null hypothesis μ 1 = μ 2 = μ 3 D) do not reject the null hypothesis μ 1 ≤ μ 2 ≤ μ 3 E) inconclusive Answer: C Diff: 2 Response: See section 11.2 The Completely Randomized Design (One-Way ANOVA) Learning Objective: 11.2: Test a completely randomized design using a one-way analysis of variance. Bloom's level: Application 127) In an experiment where the means of 6 groups are being tested 2 at a time, tests are conducted. If each test is analyzed using an α = 0.01, the probability that at least one Type I error will be committed is . A) 12; 0.12 B) 12; 0.88 C) 15; 0.14 D) 15; 0.86 E) 36; 0.30 Answer: C Diff: 2 Response: See section 11.3 Multiple Comparison Tests Learning Objective: 11.3: Use multiple comparison techniques, including Tukey's honestly significant difference test and the Tukey-Kramer procedure, to test the difference in two treatment means when there is overall significant difference between treatments. Bloom's level: Application

5 122

128) A medical researcher is interested in how weight gain in children from third-world countries is affected by the source of protein (fish, roots and tubers, or nuts and oilseeds) and by level of protein intake (low, defined as less than 0.56 grams per kilogram of weight, or high). The researcher wants to test the relative efficacy of each protein source, controlling for the level of protein intake but not testing it as the main variable of interest. For this purpose, she classifies a group of children in an impoverished village in a third-world country into either high or low protein intake. Then, the researcher randomly assigns 50 children from each category to a group where the children will receive food once a day that will emphasize one of the three protein sources. You can assume that the total level of protein intake is not affected (it's effects take a longer time to show and depend largely on the other meals the children have at home). The researcher's experimental design is a . A) normalized block design B) completely randomized design C) factorial design D) randomized block design E) partially randomized design Answer: D Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. Bloom's level: Application 129) A medical researcher is interested in how weight gain in children from third-world countries is affected by the source of protein (fish, roots and tubers, or nuts and oilseeds) and by level of protein intake (low, defined as less than 0.56 grams per kilogram of weight, or high). The researcher wants to test the relative efficacy of each protein source, controlling for the level of protein intake but not testing it as the main variable of interest. For this purpose, she classifies a group of children in an impoverished village in a third-world country into either high or low protein intake. Then, the researcher randomly assigns 50 children from each category to a group where the children will receive food once a day that will emphasize one of the three protein sources. You can assume that the total level of protein intake is not affected (it's effects take a longer time to show and depend largely on the other meals the children have at home). In this experiment, the weight gain is . A) a concomitant variable B) the dependent variable C) a treatment variable D) a blocking variable E) a constant Answer: B Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. Bloom's level: Application

5 123

130) A medical researcher is interested in how weight gain in children from third-world countries is affected by the source of protein (fish, roots and tubers, or nuts and oilseeds) and by level of protein intake (low, defined as less than 0.56 grams per kilogram of weight, or high). The researcher wants to test the relative efficacy of each protein source, controlling for the level of protein intake but not testing it as the main variable of interest. For this purpose, she classifies a group of children in an impoverished village in a third-world country into either high or low protein intake. Then, the researcher randomly assigns 50 children from each category to a group where the children will receive food once a day that will emphasize one of the three protein sources. You can assume that the total level of protein intake is not affected (it's effects take a longer time to show and depend largely on the other meals the children have at home). In this experiment, the level of protein intake is . A) a concomitant variable B) the dependent variable C) a treatment variable D) a constant E) a blocking variable Answer: E Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. Bloom's level: Application 131) A medical researcher is interested in how weight gain in children from third-world countries is affected by the source of protein (fish, roots and tubers, or nuts and oilseeds) and by level of protein intake (low, defined as less than 0.56 grams per kilogram of weight, or high). The researcher wants to test the relative efficacy of each protein source, controlling for the level of protein intake but not testing it as the main variable of interest. For this purpose, she classifies a group of children in an impoverished village in a third-world country into either high or low protein intake. Then, the researcher randomly assigns 50 children from each category to a group where the children will receive food once a day that will emphasize one of the three protein sources. You can assume that the total level of protein intake is not affected (it's effects take a longer time to show and depend largely on the other meals the children have at home). In this experiment, the source of protein intake is . A) a surrogate variable B) the dependent variable C) a blocking variable D) a treatment variable E) a constant Answer: D Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. Bloom's level: Application

5 124

132) A medical researcher is interested in how weight gain in children from third-world countries is affected by the source of protein (fish, roots and tubers, or nuts and oilseeds) and by level of protein intake (low, defined as less than 0.56 grams per kilogram of weight, or high). The researcher wants to test the relative efficacy of each protein source, controlling for the level of protein intake but not testing it as the main variable of interest. For this purpose, she classifies a group of children in an impoverished village in a third-world country into either high or low protein intake. Then, the researcher randomly assigns 50 children from each category to a group where the children will receive food once a day that will emphasize one of the three protein sources. You can assume that the total level of protein intake is not affected (its effects take a longer time to show and depend largely on the other meals the children have at home). The researcher's alternative hypothesis is . A) μ 1 ≠ μ 2 ≠ μ 3 B) μ 1 ≥ μ 2 ≥ μ 3 C) μ 1 = μ 2 = μ 3 D) at least one of the average weight gains will be different than the others E) μ 1 ≤ μ 2 ≥ μ 3 Answer: D Diff: 1 Response: See section 11.4 The Randomized Block Design Learning Objective: 11.4: Test a randomized block design that includes a blocking variable to control for confounding variables. Bloom's level: Application 133) Experiments designed so that two or more treatments (independent variables) are explored simultaneously are called . A) concomitant B) interactive C) factorial D) simultaneous E) block Answer: C Diff: 1 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. Bloom's level: Application

5 125

134) At a local community college, the head of the math department is interested in determining the effects of two factors on the final exam performance. The two factors are: (1) whether the student completed all the suggested exercises (yes or no), and (2) how many cups of coffee the student took the morning of the exam (0, 1, or 2). Analysis of the data is summarized in the following ANOVA table: Source of Variation Rows (exercises) Column (coffee) Interaction Within Total

SS 2334.72 2936.11 2569.45 2466.66 10306.94

df 1 2 2 12 17

MS 2334.72 1486.06 1284.73 205.56

F 11.36 7.14 6.25

Using α = .05, the conclusions are: . A) a significant effect of completing the exercises but not of drinking coffee and no significant interaction B) a significant effect of drinking coffee but not of completing the exercises and no significant interaction C) a significant effect of both completing the exercises and drinking coffee but no significant interaction D) a significant effect of both completing the exercises and drinking coffee as well as a significant interaction E) no significant effect of completing the exercises or drinking coffee and no significant interaction Answer: D Diff: 3 Response: See section 11.5 A Factorial Design (Two-Way ANOVA) Learning Objective: 11.5: Test a factorial design using a two-way analysis of variance, noting the advantages and applications of such a design and accounting for possible interaction between two treatment variables. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 12 Simple Regression Analysis and Correlation 1) Correlation is a measure of the degree of a linear relationship between two variables. Answer: TRUE Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables. 2) If the correlation coefficient between two variables is -1, it means that the two variables are not related. Answer: FALSE Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables. 3) The strength of a linear relationship in a simple linear regression change if the units of the data are converted, say from feet to inches. Answer: FALSE Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables. 4) The process of constructing a mathematical model or function that can be used to predict or determine one variable by another variable is called regression analysis. Answer: TRUE Diff: 1 Response: See section 12.2 Introduction to Simple Regression Analysis Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable. 5) In regression analysis, the variable that is being predicted is usually referred to as the independent variable. Answer: FALSE Diff: 1 Response: See section 12.2 Introduction to Simple Regression Analysis Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

6) In regression analysis, the predictor variable is called the dependent variable. Answer: FALSE Diff: 1 Response: See section 12.2 Introduction to Simple Regression Analysis Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable. 7) The first step in simple regression analysis is usually to construct a scatter plot. Answer: TRUE Diff: 1 Response: See section 12.2 Introduction to Simple Regression Analysis Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable. 8) The slope of the regression line, ŷ = 21 - 5x, is 5. Answer: FALSE Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. 9) The slope of the regression line, ŷ = 21 - 5x, is 21. Answer: FALSE Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. 10) For the regression line, ŷ = 21 - 5x, 21 is the y-intercept of the line. Answer: TRUE Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. 11) The difference between the actual y value and the predicted ŷ value found using a regression equation is called the residual. Answer: TRUE Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

12) Data points that lie apart from the rest of the points are called deviants. Answer: FALSE Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. 13) One of the assumptions of simple regression analysis is that the error terms are exponentially distributed. Answer: FALSE Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. 14) In simple regression analysis the error terms are assumed to be independent and normally distributed with zero mean and constant variance. Answer: TRUE Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. 15) One of the major uses of residual analysis is to test some of the assumptions that are underlying the regression. Answer: TRUE Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. 16) The standard error of the estimate, denoted se, is the square root of the sum of the squares of the vertical distances between the actual y values and the predicted values of ŷ. Answer: FALSE Diff: 2 Response: See section 12.5 Standard Error of the Estimate Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model. 17) The proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x) is called the coefficient of correlation. Answer: FALSE Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 3

18) The coefficient of determination is the proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x). Answer: TRUE Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 19) In a simple regression the coefficient of correlation is the square root of the coefficient of determination. Answer: FALSE Diff: 2 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 20) In the simple regression model, ŷ = 21 - 5x, if the coefficient of determination is 0.81, we can say that the coefficient of correlation between y and x is 0.90. Answer: FALSE Diff: 2 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 21) The range of admissible values for the coefficient of determination is -1 to +1. Answer: FALSE Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 22) A t-test is used to determine whether the coefficients of the regression model are significantly different from zero. Answer: TRUE Diff: 1 Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model Learning Objective: 12.7: Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model. 23) To determine whether the overall regression model is significant, an F-test is used. Answer: TRUE Diff: 2 Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model Learning Objective: 12.7: Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model. 4

24) An F-value to test the overall significance of a regression model is computed by dividing the sum of squares regression (SSreg) by the sum of squares error (SSerr). Answer: FALSE Diff: 2 Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model Learning Objective: 12.7: Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model. 25) The variability in the estimated slope is smaller when the x-values are more spread out. Answer: FALSE Diff: 2 Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model Learning Objective: 12.7: Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model. 26) Given x, a 95% prediction interval for a single value of y is always wider than a 95% confidence interval for the average value of y. Answer: TRUE Diff: 1 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable. 27) Prediction intervals get narrower as we extrapolate outside the range of the data. Answer: FALSE Diff: 2 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable. 28) A confidence interval based on a specific value of x will reflect the range for the average value of the dependent variable. Answer: TRUE Diff: 2 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

29) A prediction interval based on a specific value of x will reflect an estimate of the dependent variable for one person or thing from the population. Answer: TRUE Diff: 2 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable. 30) Regression methods can be pursued to estimate trends that are linear in time. Answer: TRUE Diff: 1 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary. 31) Regression output from Minitab software directly displays the regression equation. Answer: TRUE Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it. 32) Regression output from Excel software directly shows the regression equation. Answer: FALSE Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it. 33) Regression output from Minitab software includes an ANOVA table. Answer: TRUE Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it. 34) Regression output from Excel software includes an ANOVA table. Answer: TRUE Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

7) According to the following graphic, X and Y have

A) strong negative correlation B) virtually no correlation C) strong positive correlation D) moderate negative correlation E) weak negative correlation Answer: C Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

8) According to the following graphic, X and Y have

A) strong negative correlation B) virtually no correlation C) strong positive correlation D) moderate negative correlation E) weak negative correlation Answer: B Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

9) From the following scatter plot, we can say that between y and x there is

A) perfect positive correlation B) virtually no correlation C) positive correlation D) negative correlation E) perfect negative correlation Answer: C Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

10) From the following scatter plot, we can say that between y and x there is

A) perfect positive correlation B) virtually no correlation C) positive correlation D) negative correlation E) perfect negative correlation Answer: D Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

39) From the following scatter plot, we can say that between y and x there is

. A) perfect positive correlation B) virtually no correlation C) positive correlation D) negative correlation E) perfect negative correlation Answer: B Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables. 40) The numerical value of the coefficient of correlation must be . A) between -1 and +1 B) between -1 and 0 C) between 0 and 1 D) equal to SSE/(n-2) E) between 0 and -1 Answer: A Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

41) If there is perfect negative correlation between two sets of numbers, then . A) r = 0 B) r = -1 C) r = +1 D) SSE = 1 E) MSE = 1 Answer: B Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables. 42) If there is positive correlation between two sets of numbers, then . A) r = 0 B) r < 0 C) r > 0 D) SSE = 1 E) MSE = 1 Answer: C Diff: 1 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables. 43) A quality manager is developing a regression model to predict the total number of defects as a function of the day of the week that the item is produced. Production runs are done 10 hours a day, 7 days a week. The explanatory variable is _ . A) day of week B) production run C) percentage of defects D) number of defects E) number of production runs Answer: A Diff: 1 Response: See section 12.2 Introduction to Simple Regression Analysis Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

44) A quality manager is developing a regression model to predict the total number of defects as a function of the day of the week that the item is produced. Production runs are done 10 hours a day, 7 days a week. The dependent variable is . A) day of week B) production run C) percentage of defects D) number of defects E) number of production runs Answer: D Diff: 1 Response: See section 12.2 Introduction to Simple Regression Analysis Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable. 45) A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The intercept of this model is the . A) batch size B) unit variable cost C) fixed cost D) total cost E) total variable cost Answer: C Diff: 2 Response: See section 12.2 Introduction to Simple Regression Analysis Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable. 46) A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The slope of the accountant's model is . A) batch size B) unit variable cost C) fixed cost D) total cost E) total variable cost Answer: B Diff: 1 Response: See section 12.2 Introduction to Simple Regression Analysis Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

47) In the regression equation, ŷ = 49.56 + 0.97x, the slope is . A) 0.97 B) 49.56 C) 1.00 D) 0.00 E) -0.97 Answer: A Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. 48) In the regression equation, ŷ = 54.78 + 1.45x, the intercept is . A) 1.45 B) -1.45 C) 54.78 D) -54.78 E) 0.00 Answer: C Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. 49) For a certain data set the regression equation is ŷ = 29 - 5x. The correlation coefficient between y and x in this data set . A) must be 0 B) is negative C) must be 1 D) is positive E) must be >1 Answer: B Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

50) For a certain data set the regression equation is ŷ = 37 + 13x. The correlation coefficient between y and x in this data set . A) must be 0 B) is negative C) must be 1 D) is positive E) must be 3 Answer: D Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. 51) The coefficient of correlation in a simple regression analysis is = - 0.6. The coefficient of determination for this regression would be . A) 0.6 B) - 0.6 or + 0.6 C) 0.13 D) - 0.36 E) 0.36 Answer: E Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. 52) The following data is to be used to construct a regression model: X y

3 5

5 4

7 5

4 4

8 7

10 10

9 8

The value of the intercept is . A) 16.49 B) 1.19 C) 1.43 D) 0.75 E) 1.30 Answer: B Diff: 2 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

53) The following data is to be used to construct a regression model: x y

3 5

5 4

7 5

4 4

8 7

10 10

9 8

The value of the slope is . A) 16.49 B) 1.19 C) 1.43 D) 0.75 E) 1.30 Answer: D Diff: 2 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. 54) The following data is to be used to construct a regression model: x y

3 5

5 4

7 5

4 4

8 7

10 10

9 8

The regression equation is . A) ŷ = 16.49 + 1.43x B) ŷ = 1.19 + 0.91x C) ŷ = 1.19 + 0.75x D) ŷ = 0.75 + 0.18x E) ŷ = 0.91 + 4.06x Answer: C Diff: 3 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

55) Consider the following scatter plot and regression line. At x = 50, the residual (error term) is .

A) positive B) zero C) negative D) imaginary E) unknown Answer: A Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

56) For the following scatter plot and regression line, at x = 34 the residual is

A) positive B) zero C) negative D) imaginary E) unknown Answer: C Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. 57) One of the assumptions made in simple regression is that . A) the error terms are normally distributed B) the error terms have unequal variances C) the model is nonlinear D) the error terms are dependent E) the error terms are all equal Answer: A Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

58) One of the assumptions made in simple regression is that . A) the error terms are exponentially distributed B) the error terms have unequal variances C) the model is linear D) the error terms are dependent E) the model is nonlinear Answer: C Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. 59) The assumptions underlying simple regression analysis include . A) the error terms are exponentially distributed B) the error terms have unequal variances C) the model is nonlinear D) the error terms are dependent E) the error terms are independent Answer: E Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. 60) The assumption of constant error variance in regression analysis is called . A) heteroscedasticity B) homoscedasticity C) residuals D) linearity E) nonnormality Answer: B Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

61) The following residuals plot indicates

A) a nonlinear relation B) a nonconstant error variance C) the simple regression assumptions are met D) the sample is biased E) the sample is random Answer: B Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

62) The following residuals plot indicates

A) a nonlinear relation B) a nonconstant error variance C) the simple regression assumptions are met D) the sample is biased E) a random sample Answer: A Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. 63) The total of the squared residuals is called the . A) coefficient of determination B) sum of squares of error C) standard error of the estimate D) R-squared E) coefficient of correlation Answer: B Diff: 1 Response: See section 12.5 Standard Error of the Estimate Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

2 62

64) A standard deviation of the error of the regression model is called the . A) coefficient of determination B) sum of squares of error C) standard error of the estimate D) R-squared E) coefficient of correlation Answer: C Diff: 1 Response: See section 12.5 Standard Error of the Estimate Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model. 65) A simple regression model developed for 12 pairs of data resulted in a sum of squares of error, SSE = 246. The standard error of the estimate is . A) 24.6 B) 4.96 C) 20.5 D) 4.53 E) 12.3 Answer: B Diff: 2 Response: See section 12.5 Standard Error of the Estimate Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model. 66) A simple regression model developed for ten pairs of data resulted in a sum of squares of error, SSE = 125. The standard error of the estimate is . A) 12.5 B) 3.5 C) 15.6 D) 3.95 E) 25 Answer: D Diff: 2 Response: See section 12.5 Standard Error of the Estimate Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

2 63

67) In regression analysis, R-squared is also called the . A) residual B) coefficient of determination C) coefficient of correlation D) standard error of the estimate E) sum of squares of regression Answer: B Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 68) The numerical value of the coefficient of determination must be . A) between -1 and +1 B) between -1 and 0 C) between 0 and 1 D) equal to SSE/(n-2) E) between -100 and +100 Answer: C Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 69) The proportion of variability of the dependent variable accounted for or explained by the independent variable is called the . A) sum of squares error B) coefficient of correlation C) coefficient of determination D) covariance E) regression sum of squares Answer: C Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

2 64

70) If x and y in a regression model are totally unrelated, . A) the correlation coefficient would be -1 B) the coefficient of determination would be 0 C) the coefficient of determination would be 1 D) the SSE would be 0 E) the MSE would be 0s Answer: B Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 71) In a regression analysis if SST = 200 and SSR = 200, r2 = . A) 0.25 B) 0.75 C) 0.00 D) 1.00 E) -1.00 Answer: D Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 72) If the coefficient of determination was 0.49, then the correlation coefficient would be . A) 0.7 B) -0.7 C) 0.49 D) 0.7 or -0.7 E) -0.49 Answer: D Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

2 65

73) In a regression analysis if SST = 150 and SSR = 100, r2 = . A) 0.82 B) 1.22 C) 1.50 D) 0.67 E) -1.00 Answer: D Diff: 1 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. 74) A researcher has developed a regression model from fourteen pairs of data points. He wants to test if the slope is significantly different from zero. He uses a two tailed test and α = 0.01. The critical table t value is . A) 2.650 B) 3.012 C) 3.055 D) 2.718 E) 2.168 Answer: C Diff: 1 Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model Learning Objective: 12.7: Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model. 75) A researcher has developed a regression model from fifteen pairs of data points. He wants to test if the slope is significantly different from zero. He uses a twotailed test and α = 0.10. The critical table t value is . A) 1.771 B) 1.350 C) 1.761 D) 2.145 E) 2.068 Answer: A Diff: 1 Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model Learning Objective: 12.7: Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

2 66

76) In the regression equation, ŷ = 2.164 + 1.3657x, and n = 6, the mean of x is 8.667, SSxx = 89.333 and Se = 3.44. A 95% confidence interval for the average of y when x = 8 is . A) (9.13, 17.05) B) (2.75, 23.43) C) (10.31, 15.86) D) (3.56, 22.62) E) (12.09, 14.09) Answer: A Diff: 3 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable. 77) In the regression equation, ŷ = 2.164+1.3657x and n = 6, the mean of x is 8.667, SSxx = 89.333 and Se = 3.44. A 95% prediction interval for y when x = 8 is . A) (9.13, 17.05) B) (2.75, 23.43) C) (10.31, 15.86) D) (3.56, 22.62) E) (12.09, 14.09) Answer: B Diff: 3 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable. 78) In the regression equation, ŷ = 5.23+2.74x and n = 24, the mean of x is 12.56, SSxx = 55.87 and Se = 10.71. A 90% prediction interval for y when x = 11 is . A) (2.74, 5.23) B) (35.37, 70.74) C) (16.21, 54.53) D) (12.56, 55.87) E) (30.00, 40.74) Answer: C Diff: 3 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

2 67

79) In the regression equation, ŷ = 5.23+2.74x and n = 24, the mean of x is 12.56, SSxx = 55.87 and Se = 10.71. A 90% confidence interval for y when x = 11 is . A) (2.74, 5.23) B) (35.37, 70.74) C) (16.21, 54.53) D) (12.56, 55.87) E) (30.00, 40.74) Answer: E Diff: 3 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable. 80) When determining interval estimates for specific x0, the closer x0 is to the , the narrower become(s). A) standard deviation; the confidence interval B) mean of y; both the prediction and confidence intervals C) mean of x; both the prediction and confidence intervals D) standard deviation; mean of y E) mean of x; mean of y Answer: C Diff: 1 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable. 81) A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: ŷ = 2,000 + 0.42x. If a car is driven 15,000 miles, the predicted cost is . A) 2,090 B) 17,000 C) 8,400 D) 8,300 E) 6,300 Answer: D Diff: 1 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary.

2 68

82) A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: ŷ = 2,000 + 0.42x. If a car is driven 30,000 miles, the predicted cost is . A) 10,400 B) 14,600 C) 2,000 D) 32,000 E) 10,250 Answer: B Diff: 1 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary. 83) A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: ŷ = 2,000 + 0.42x. If a car is driven 20,000 miles, the predicted cost is . A) 10,400 B) 20,000 C) 2,840 D) 6,200 E) 6,750 Answer: A Diff: 1 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary. 84) A manager wants to predict the cost (y) of travel for salespeople based on the number of days (x) spent on each sales trip. The following model has been developed: ŷ = $400 + 120x. If a trip took 4 days, the predicted cost of the trip is . A) 480 B) 880 C) 524 D) 2080 E) 1080 Answer: B Diff: 1 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary.

2 69

85) A manager wants to predict the cost (y) of travel for salespeople based on the number of days (x) spent on each sales trip. The following model has been developed: ŷ = $400 + 120x. If a trip took 3 days, the predicted cost of the trip is . A) 760 B) 360 C) 523 D) 1560 E) 1080 Answer: A Diff: 1 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary. 86) The equation of the trend line for the data based on sales (in $1000) of a local restaurant over the years 2015-2021 is Sales = -265575 + 132.571*year. Using the trend line, the forecast sales for the year 2023 is . A) $2616.13 B) $2,616,133 C) $2,350,991 D) $2483.56 E) $2,483,562 Answer: B Diff: 1 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary. 87) The equation of the trend line for the data based on sales (in $1000) of a local restaurant over the years 2015-2021 is Sales = -265575 + 132.571*year. The equation of the trend line when using 1 to 7 for 2015-2021 is . A) -265575 + 132.571x B) 132,571x C) 1422.994 + 132.571x D) -263571 + 1422.994x E) 2014 + 1422.994x Answer: C Diff: 2 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary.

2 70

88) The equation of the trend line for the data based on sales (in $1000) of a local restaurant over the years 2015-2021 is Sales = -265575 + 132.571*year. The equation of the trend line when using 5 to 10 for 2015-2021 is . A) 892.71 + 132.571x B) 132,571x C) 1422.994 + 132.571x D) -263571 + 892.71x E) 2010 + 892.71x Answer: A Diff: 2 Response: See section 12.9 Using regression to develop a forecasting trend line Learning Objective: 12.9: Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary. 89) Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 3.996 0.358

Source Regression Residual Total

df 1 11 12

Standard Error 1.161268 0.102397

t Statistic p-value 3.441065 0.004885 3.496205 0.004413

SS MS F 9.858769 9.858769 12.22345 8.872 0.806545 18.73077

Se = 0.898 2 r = 0.526341

Louis's regression model is . A) ŷ = -0.358 + 3.996x B) ŷ = 0.358 + 3.996x C) ŷ = -3.996 + 0.358x D) ŷ = 3.996 - 0.358x E) ŷ = 3.996 + 0.358x Answer: E Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

1) Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 3.996 0.358

Source Regression Residual Total

df 1 11 12

Standard Error 1.161268 0.102397

t Statistic p-value 3.441065 0.004885 3.496205 0.004413

SS MS F 9.858769 9.858769 12.22345 8.872 0.806545 18.73077

Se = 0.898 r2 = 0.526341

The correlation coefficient between Louis's variables is . A) -0.73 B) 0.73 C) 0.28 D) -0.28 E) 0.00 Answer: B Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

2) Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 3.996 0.358

Source Regression Residual Total

df 1 11 12

Standard Error 1.161268 0.102397

t Statistic p-value 3.441065 0.004885 3.496205 0.004413

SS MS F 9.858769 9.858769 12.22345 8.872 0.806545 18.73077

Se = 0.898 r2 = 0.526341

Louis's sample size (n) is . A) 13 B) 14 C) 12 D) 24 E) 1 Answer: A Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

3) Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 3.996 0.358

Source Regression Residual Total

df 1 11 12

Standard Error 1.161268 0.102397

t Statistic p-value 3.441065 0.004885 3.496205 0.004413

SS MS F 9.858769 9.858769 12.22345 8.872 0.806545 18.73077

Se = 0.898 r2 = 0.526341

Using α = 0.05, Louis should . A) increase the sample size B) suspend judgment C) not reject H0: β1 = 0 D) reject H0: β1 = 0 E) not reject H0: β0 = 0 Answer: D Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

4) Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 3.996 0.358

Source Regression Residual Total

df 1 11 12

Standard Error 1.161268 0.102397

t Statistic p-value 3.441065 0.004885 3.496205 0.004413

SS MS F 9.858769 9.858769 12.22345 8.872 0.806545 18.73077

Se = 0.898 r2 = 0.526341

For a lot size of 10,000 handsets, Louis' model predicts total cost will be . A) $4,031.80 B) $757.60 C) $3,960.20 D) $354.01 E) $1873.077 Answer: B Diff: 2 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

5) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 39.14942 1.792312

Source Regression Residual Total

df 1 9 10

Standard Error 22.30182 0.407507

t Statistic p-value 1.755436 0.109712 4.398234 0.001339

SS MS F 16850.99 16850.99 19.34446 7839.915 871.1017 24690.91

Se = 29.51443 r2 = 0.682478

Abby's regression model is . A) ŷ = 39.15 + 2.79x B) ŷ = 39.15 - 1.79x C) ŷ = 1.79 + 39.15x D) ŷ = -1.79 + 39.15x E) ŷ = 39.15 + 1.79x Answer: E Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

6) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 39.14942 1.792312

Source Regression Residual Total

df 1 9 10

Standard Error 22.30182 0.407507

t Statistic p-value 1.755436 0.109712 4.398234 0.001339

SS MS F 16850.99 16850.99 19.34446 7839.915 871.1017 24690.91

Se = 29.51443 r2 = 0.682478

The correlation coefficient between the two variables in this regression is . A) 0.682478 B) -0.83 C) 0.83 D) -0.68 E) 1.0008 Answer: C Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

7) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 39.14942 1.792312

Source Regression Residual Total

df 1 9 10

Standard Error 22.30182 0.407507

t Statistic p-value 1.755436 0.109712 4.398234 0.001339

SS MS F 16850.99 16850.99 19.34446 7839.915 871.1017 24690.91

Se = 29.51443 r2 = 0.682478

Abby's sample size (n) is . A) 8 B) 10 C) 11 D) 20 E) 12 Answer: C Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

8) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 39.14942 1.792312

Source Regression Residual Total

df 1 9 10

Standard Error 22.30182 0.407507

t Statistic p-value 1.755436 0.109712 4.398234 0.001339

SS MS F 16850.99 16850.99 19.34446 7839.915 871.1017 24690.91

Se = 29.51443 r2 = 0.682478

Using α = 0.05, Abby should . A) reject H0: β1 = 0 B) not reject H0: β1 = 0 C) increase the sample size D) suspend judgment E) reject H0: β0 = 0 Answer: A Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

9) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

Intercept x

Coefficients 39.14942 1.792312

Source Regression Residual Total

df 1 9 10

Standard Error 22.30182 0.407507

t Statistic p-value 1.755436 0.109712 4.398234 0.001339

SS MS F 16850.99 16850.99 19.34446 7839.915 871.1017 24690.91

Se = 29.51443 r2 = 0.682478

For a household with $50,000 annual income, Abby's model predicts monthly grocery expenditures of . A) $150 B) $50 C) $1,959 D) $129 E) $1288 Answer: D Diff: 2 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

10) Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Intercept x

Coefficients -0.14156 0.105195

Source Regression Residual Total

df 1 4 5

Standard Error 0.292143 0.013231

t Statistic p-value -0.48455 0.653331 7.950352 0.001356

SS MS F 3.550325 3.550325 63.20809 0.224675 0.056169 3.775

Se = 0.237 2 r = 0.940483

Annie's regression model can be written as: . A) ŷ = 7.950352 - 0.48455x B) ŷ = -0.48455 + 7.950352x C) ŷ = -0.14156 + 0.105195x D) ŷ = 0.105195 - 0.14156x E) ŷ = 0.105195 + 0.14156x Answer: C Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

11) Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Intercept x

Coefficients -0.14156 0.105195

Source Regression Residual Total

df 1 4 5

Standard Error 0.292143 0.013231

t Statistic p-value -0.48455 0.653331 7.950352 0.001356

SS MS F 3.550325 3.550325 63.20809 0.224675 0.056169 3.775

Se = 0.237 2 r = 0.940483

The numerical value of the correlation coefficient between the historic city tour sales and the size of city population is . A) 0.969785 B) 0.940483 C) 0.224675 D) -0.14156 E) 1.000000 Answer: A Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

4 11

12) Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Intercept x

Coefficients -0.14156 0.105195

Source Regression Residual Total

df 1 4 5

Standard Error 0.292143 0.013231

t Statistic p-value -0.48455 0.653331 7.950352 0.001356

SS MS F 3.550325 3.550325 63.20809 0.224675 0.056169 3.775

Se = 0.237 2 r = 0.940483

Annie's sample size is . A) 2 B) 4 C) 6 D) 8 E) 10 Answer: C Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

4 12

13) Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Intercept x

Coefficients -0.14156 0.105195

Source Regression Residual Total

df 1 4 5

Standard Error 0.292143 0.013231

t Statistic p-value -0.48455 0.653331 7.950352 0.001356

SS MS F 3.550325 3.550325 63.20809 0.224675 0.056169 3.775

Se = 0.237 2 r = 0.940483

Using α = 0.05, Annie should . A) increase the sample size B) not reject H0: β1 = 0 C) reject H0: β1 = 0 D) suspend judgment E) rejectH0: β0 = 0 Answer: C Diff: 1 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it.

4 13

14) Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Intercept x

Coefficients -0.14156 0.105195

Source Regression Residual Total

df 1 4 5

Standard Error 0.292143 0.013231

t Statistic p-value -0.48455 0.653331 7.950352 0.001356

SS MS F 3.550325 3.550325 63.20809 0.224675 0.056169 3.775

Se = 0.237 2 r = 0.940483

For a city with a population of 500,000, Annie's model predicts annual sales of . A) $70,780 B) $5,259 C) $170,780 D) $52,597 E) $152,597 Answer: D Diff: 2 Response: See section 12.10 Interpreting the output Learning Objective: 12.10: Use a computer to develop a regression analysis, and interpret the output that is associated with it. 104) If a scatter plot of variables X and Y shows a trend that can be summarized to a large degree by a straight line with slope 0.8 and y-intercept 0.2 (i.e., Y = 0.2 + 0.8X), then the correlation coefficient between X and Y is . A) 0.8, and there is a causal relation between X and Y (either X causes Y or Y causes X) B) 0.2, and there is a causal relation between X and Y (either X causes Y or Y causes X) C) 0.8, but there is no causal relation between X and Y D) 0.2, but there is no causal relation between X and Y E) 0.8, and there may be a causal relation between X and Y , but not necessarily Answer: E Diff: 2 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables. Bloom's level: Knowledge

4 14

105) Suppose you compute the correlation coefficient between two variables, X and Y , and obtain a value of 0.55. Then you realize that all the values for both variables have been corrupted in a way that their actual sign has been changed (positive values were turned into negative values and vice versa; only the signs have been changed). Then the actual, corrected value of the correlation coefficient . A) is -0.55 B) remains unchanged C) changes but there is not enough information to determine the correct value D) is 0 E) is 0.50 Answer: B Diff: 2 Response: See section 12.1 Correlation Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables. Bloom's level: Knowledge 106) In the regression equation, ŷ = 54.78 + 1.45x, the x-intercept is . A) 1.45 B) -1.45 C) 54.78 D) -54.78 E) -37.8 Answer: E Diff: 2 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. Bloom's level: Knowledge 107) A regression line minimizes the sum of the squared error values. This means that the regression line minimizes the sum of from each point in the scatter point to the regression line. A) the squares of the distances B) the squares of the horizontal distances (differences in the x-coordinates) C) the squares of the vertical distances (differences in the x-coordinates) D) the squares of the horizontal distances (differences in the y-coordinates) E) the squares of the vertical distances (differences in the y-coordinates) Answer: E Diff: 1 Response: See section 12.3 Determining the Equation of the Regression Line Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line. Bloom's level: Knowledge

4 15

108) Which of the following assertions is true about the regression line? A) The regression line is also called the least cubes line and is found minimizing the sum of the cubes of the residuals. B) It is found by minimizing the sum of the residuals squared, but–even though it would be unnecessarily complicated–it could also be found minimizing the sum of the residuals cubed. C) Depending on the data, some regression lines could have only positive residuals. D) Depending on the data, some regression lines could have all residuals equal to zero. E) Depending on the data, some regression lines could have only negative residuals. Answer: D Diff: 1 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. Bloom's level: Application 109) Suppose for a given data set the regression equation is: ŷ = 54.78 + 1.45x, and the point (0.00, 24.78) is in the data set. The residual for this point is . A) 24.78 B) -24.78 C) 0.00 D) 30.00 E) -30.00 Answer: E Diff: 2 Response: See section 12.4 Residual Analysis Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model. Bloom's level: Application 110) A simple regression model resulted in a sum of squares of error of 125 (i.e., SSE = 125), and the standard error is 3.95. This model is for pairs of data. A) 8 B) 9 C) 10 D) 11 E) 12 Answer: C Diff: 2 Response: See section 12.5 Standard Error of the Estimate Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model. Bloom's level: Application

4 16

111) A simple regression model for 10 pair of data resulted in a standard error of 3.95 (i.e., Se = 3.95), and the sum of squares of error (SSE) is . A) 187.23 B) 171.63 C) 156.03 D) 140.42 E) 124.82 Answer: E Diff: 2 Response: See section 12.5 Standard Error of the Estimate Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model. Bloom's level: Application 112) Suppose that in a regression analysis, SSE = 35, and SSyy = 70. Then the corresponding coefficient of determination r2 = . A) 0.25 B) 0.50 C) either 0.25 or -0.25, but there is not enough information to determine its sign D) either 0.50 or -0.50, but there is not enough information to determine its sign E) -0.50 Answer: B Diff: 2 Response: See section 12.6 Coefficient of Determination Learning Objective: 12.6: Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation. Bloom's level: Application 113) A researcher has developed the regression equation ŷ = 2.164 + 1.3657x, where n = 6, the mean of x is 8.667, Sxx = 89.333, and Se = 3.44. The researcher wants to test if the slope is significantly positive, and he chooses a significance level of 0.05. The observed t value is . A) 3.752 B) 3.852 C) 3.972 D) 3.985 E) 3.995 Answer: A Diff: 2 Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model Learning Objective: 12.7: Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model. Bloom's level: Application

4 17

114) A researcher has developed the regression equation ŷ = 2.164 + 1.3657x, where n = 6, the mean of x is 8.667, SSxx = 89.333, and Se = 3.44. The researcher wants to test if the slope is significantly positive, and he chooses a significance level of 0.05. The critical t value is . A) 2.776 B) 2.132 C) 2.015 D) 1.943 E) 1.782 Answer: B Diff: 2 Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model Learning Objective: 12.7: Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model. Bloom's level: Application 115) A researcher has developed the regression equation ŷ = 2.164 + 1.3657x, where n = 6, the mean of x is 8.667, SSxx = 89.333, and Se = 3.44. The 90% confidence interval for y when x = 1 is . A) (-2.14, 9.2) B) (-2.54, 10.6) C) (-3.04, 9.92) D) (-3.13, 10.19) E) (-3.24, 10.4) Answer: D Diff: 3 Response: See section 12.8 Estimation Learning Objective: 12.8: Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable. Bloom's level: Application

4 18

Business Statistics, 11e (Black) Chapter 13 Multiple Regression Analysis 1) Regression analysis with two dependent variables and two or more independent variables is called multiple regression. Answer: FALSE Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 2) The model y = β 0 + β 1x1 + β 2x2 + ε is a second-order regression model. Answer: FALSE Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 3) The model y = β 0 + β 1x1 + β 2x2 + β 3x3 + ε is a first-order regression model. Answer: TRUE Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 4) In the multiple regression model y = β 0 + β 1x1 + β 2x2 + β 3x3 + ε, the β coefficients of the x variables are called partial regression coefficients. Answer: TRUE Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 5) In the model y = β 0 + β 1x1 + β 2x2 + β 3x3 + ε, y is the independent variable. Answer: FALSE Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns.

6) In a multiple regression model, the partial regression coefficient of an independent variable represents the increase in the y variable when that independent variable is increased by one unit if the values of all other independent variables are held constant. Answer: TRUE Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 7) In the model y = β 0 + β 1x1 + β 2x2 + β 3x3 + ε, ε is a constant. Answer: FALSE Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 8) A slope in a multiple regression model is known as a partial slope because it ignores the effects of other explanatory variables. Answer: FALSE Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 9) Multiple t-tests are used to determine whether the independent variables in the regression model are significant. Answer: TRUE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. 10) The F test is used to determine whether the overall regression model is significant. Answer: TRUE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

11) The F value that is used to test for the overall significance of a multiple regression model is calculated by dividing the mean square regression (MSreg) by the mean square error (MSerr). Answer: TRUE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. 12) The F value that is used to test for the overall significance of a multiple regression model is calculated by dividing the sum of mean squares regression (SSreg) by the sum of squares error (SSerr). Answer: FALSE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. 13) The mean square error (MSerr) is calculated by dividing the sum of squares error (SSerr) by the number of observations in the data set (N). Answer: FALSE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. 14) The mean square error (MSerr) is calculated by dividing the sum of squares error (SSerr) by the number of degrees of freedom in the error (dferr). Answer: TRUE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. 15) In a multiple regression analysis with N observations and k independent variables, the degrees of freedom for the residual error is given by (N - k - 1). Answer: TRUE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

16) In a multiple regression analysis with N observations and k independent variables, the degrees of freedom for the residual error is given by (N - k). Answer: FALSE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. 17) If we reject H0: β1 = β2 = 0 using the F-test, then we should conclude that both slopes are different from zero. Answer: FALSE Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. 18) The standard error of the estimate of a multiple regression model is essentially the standard deviation of the residuals for the regression model. Answer: TRUE Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 19) The standard error of the estimate of a multiple regression model is computed by taking the square root of the SSE divided by the degrees of freedom of error for the model. Answer: TRUE Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 20) In a multiple regression model, the proportion of the variation of the dependent variable, y, accounted for the independent variables in the regression model is given by the coefficient of multiple correlation. Answer: FALSE Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

21) The value of R2 always goes up when a nontrivial explanatory variable is added to a regression model. Answer: TRUE Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 22) The value of adjusted R2 always goes up when a nontrivial explanatory variable is added to a regression model. Answer: FALSE Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 23) Minitab and Excel output for a multiple regression model show the F test for the overall model, but do not provide the t tests for the regression coefficients. Answer: FALSE Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 24) Minitab and Excel output for a multiple regression model show the t tests for the regression coefficients but do not provide a t test for the regression constant. Answer: FALSE Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 25) A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). The response variable in this model is . A) batch size B) production shift C) production plant D) total cost E) variable cost Answer: D Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns.

26) A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, "shift" is . A) a response variable B) an independent variable C) a quantitative variable D) a dependent variable E) a constant Answer: B Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 27) A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, "batch size" is . A) a response variable B) an indicator variable C) a dependent variable D) a qualitative variable E) an independent variable Answer: E Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 28) A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The response variable in this model is . A) family size B) expenditures on groceries C) household income D) suburban E) household neighborhood Answer: B Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 6

29) A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The "neighborhood" variable in this model is . A) an independent variable B) a response variable C) a quantitative variable D) a dependent variable E) a constant Answer: A Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 30) A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The "income" variable in this model is . A) an indicator variable B) a response variable C) a qualitative variable D) a dependent variable E) an independent variable Answer: E Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 31) A human resources analyst is developing a regression model to predict electricity plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The response variable in this model is . A) plant manager compensation B) plant capacity C) number of employees D) plant technology E) nuclear Answer: A Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 7

32) A human resources analyst is developing a regression model to predict electricity plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The "plant technology" variable in this model is . A) a response variable B) a dependent variable C) a quantitative variable D) an independent variable E) a constant Answer: D Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 33) A human resources analyst is developing a regression model to predict electricity plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The "number of employees at the plant" variable in this model is . A) a qualitative variable B) a dependent variable C) a response variable D) an indicator variable E) an independent variable Answer: E Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 34) A real estate appraiser is developing a regression model to predict the market value of singlefamily residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The response variable in this model is . A) heated area B) number of bedrooms C) market value D) central heating E) residential houses Answer: C Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 8

35) A real estate appraiser is developing a regression model to predict the market value of singlefamily residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The "central heating" variable in this model is . A) a response variable B) an independent variable C) a quantitative variable D) a dependent variable E) a constant Answer: B Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. 36) The multiple regression formulas used to estimate the regression coefficients are designed to . A) minimize the total sum of squares (SST) B) minimize the sum of squares of error (SSE) C) maximize the standard error of the estimate D) maximize the p-value for the calculated F value E) minimize the mean error Answer: B Diff: 2 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns.

37) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 616.6849 -3.33833 1.780075 df 2 15 17

Standard Error 154.5534 2.333548 0.335605

t Statistic p-value 3.990108 0.000947 -1.43058 0.170675 5.30407 5.83E-05

SS MS F p-value 121783 60891.48 14.76117 0.000286 61876.68 4125.112 183659.6

The regression equation for this analysis is . A) ŷ = 616.6849 + 3.33833 x1 + 1.780075 x2 B) ŷ = 154.5535 - 1.43058 x1 + 5.30407 x2 C) ŷ = 616.6849 - 3.33833 x1 - 1.780075 x2 D) ŷ = 154.5535 + 2.333548 x1 + 0.335605 x2 E) ŷ = 616.6849 - 3.33833 x1 + 1.780075 x2 Answer: E Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns.

38) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 616.6849 -3.33833 1.780075 df 2 15 17

Standard Error 154.5534 2.333548 0.335605

t Statistic p-value 3.990108 0.000947 -1.43058 0.170675 5.30407 5.83E-05

SS MS F p-value 121783 60891.48 14.76117 0.000286 61876.68 4125.112 183659.6

The sample size for this analysis is . A) 19 B) 17 C) 34 D) 15 E) 18 Answer: E Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns.

1 38

39) A multiple regression analysis produced the following tables.

For x1 = 360 and x2 = 220, the predicted value of y is . A) 1314.70 B) 1959.71 C) 1077.58 D) 2635.19 E) 2265.57 Answer: A Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns.

1 39

40) A multiple regression analysis produced the following tables.

The regression equation for this analysis is . A) ŷ = 1959.71 + 0.46 x1 + 2.16 x2 B) ŷ = 1959.71 - 0.46 x1 + 2.16 x2 C) ŷ = 1959.71 - 0.46 x1 - 2.16 x2 D) ŷ = 1959.71 + 0.46 x1 - 2.16 x2 E) ŷ = - 0.46 x1 - 2.16 x2 Answer: C Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns.

1 40

41) A multiple regression analysis produced the following tables.

The sample size for this analysis is . A) 12 B) 15 C) 17 D) 18 E) 24 Answer: D Diff: 1 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns.

1 41

42) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 616.6849 -3.33833 1.780075 df 2 15 17

Standard Error 154.5534 2.333548 0.335605

t Statistic p-value 3.990108 0.000947 -1.43058 0.170675 5.30407 5.83E-05

SS MS F p-value 121783 60891.48 14.76117 0.000286 61876.68 4125.112 183659.6

Using α = 0.01 to test the null hypothesis H0: β 1 = β 2 = 0, the critical F value is . A) 8.68 B) 6.36 C) 8.40 D) 6.11 E) 3.36 Answer: B Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

1 42

43) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 616.6849 -3.33833 1.780075 df 2 15 17

Standard Error 154.5534 2.333548 0.335605

t Statistic p-value 3.990108 0.000947 -1.43058 0.170675 5.30407 5.83E-05

SS MS F p-value 121783 60891.48 14.76117 0.000286 61876.68 4125.112 183659.6

Using α = 0.05 to test the null hypothesis H0: β1 = 0, the critical t value is _ . A) ± 1.753 B) ± 2.110 C) ± 2.131 D) ± 1.740 E) ± 2.500 Answer: C Diff: 2 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

1 43

44) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 616.6849 -3.33833 1.780075 df 2 15 17

Standard Error 154.5534 2.333548 0.335605

t Statistic p-value 3.990108 0.000947 -1.43058 0.170675 5.30407 5.83E-05

SS MS F p-value 121783 60891.48 14.76117 0.000286 61876.68 4125.112 183659.6

These results indicate that . A) none of the predictor variables are significant at the 5% level B) each predictor variable is significant at the 5% level C) x1 is significant at the 5% level D) x2 is significant at the 5% level E) the intercept is not significant at 5% level Answer: D Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

1 44

45) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 752.0833 11.87375 1.908183 df 2 12 14

Standard Error 336.3158 5.32047 0.662742

t Statistic p-value 2.236241 0.042132 2.231711 0.042493 2.879226 0.01213

SS MS F p-value 203693.3 101846.7 6.745406 0.010884 181184.1 15098.67 384877.4

Using α = 0.05 to test the null hypothesis H0: β1 = β2 = 0, the critical F value is . A) 3.74 B) 3.89 C) 4.75 D) 4.60 E) 2.74 Answer: B Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

1 45

46) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 752.0833 11.87375 1.908183 df 2 12 14

Standard Error 336.3158 5.32047 0.662742

t Statistic p-value 2.236241 0.042132 2.231711 0.042493 2.879226 0.01213

SS MS F p-value 203693.3 101846.7 6.745406 0.010884 181184.1 15098.67 384877.4

Using α = 0.10 to test the null hypothesis H0: β2 = 0, the critical t value is _ . A) ±1.345 B) ±1.356 C) ±1.761 D) ±2.782 E) ±1.782 Answer: E Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

1 46

47) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 752.0833 11.87375 1.908183 df 2 12 14

Standard Error 336.3158 5.32047 0.662742

t Statistic p-value 2.236241 0.042132 2.231711 0.042493 2.879226 0.01213

SS MS F p-value 203693.3 101846.7 6.745406 0.010884 181184.1 15098.67 384877.4

These results indicate that . A) none of the predictor variables are significant at the 5% level B) each predictor variable is significant at the 5% level C) x1 is the only predictor variable significant at the 5% level D) x2 is the only predictor variable significant at the 5% level E) the intercept is not significant at the 5% level Answer: B Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

48) A multiple regression analysis produced the following tables.

Using α = 0.01 to test the model, these results indicate that . A) at least one of the regression variables is a significant predictor of y B) none of the regression variables are significant predictors of y C) y cannot be sufficiently predicted using these data D) y is a good predictor of the regression variables in the model E) the y intercept in this model is the best predictor variable Answer: A Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

2 48

49) A multiple regression analysis produced the following tables.

Using α = 0.05 to test the null hypothesis H0: β1 = 0, the correct decision is . A) fail to reject the null hypothesis B) reject the null hypothesis C) fail to reject the alternative hypothesis D) reject the alternative hypothesis E) there is not enough information provided to make a decision Answer: A Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

2 49

50) A multiple regression analysis produced the following tables.

Using α = 0.05 to test the null hypothesis H0: β2 = 0, the correct decision is . A) fail to reject the null hypothesis B) reject the null hypothesis C) fail to reject the alternative hypothesis D) reject the alternative hypothesis E) there is not enough information provided to make a decision Answer: B Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients.

2 50

51) A multiple regression analysis produced the following tables.

These results indicate that . A) none of the predictor variables are significant at the 10% level B) each predictor variable is significant at the 10% level C) x1 is significant at the 10% level D) x2 is significant at the 10% level E) the intercept is not significant at 10% level Answer: B Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. 52) In regression analysis, outliers may be identified by examining the . A) coefficient of determination B) coefficient of correlation C) p-values for the partial coefficients D) residuals E) R-squared value Answer: D Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 2 51

53) The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source Regression Error Total

SS 700

1000

The number of degrees of freedom for this regression is . A) 1 B) 4 C) 34 D) 30 E) 35 Answer: B Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 54) The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source Regression Error Total

SS 700

1000

The number of degrees of freedom for error is . A) 1 B) 4 C) 34 D) 30 E) 35 Answer: D Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

2 52

55) The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source Regression Error Total

SS 700

1000

The MSR value is . A) 700.00 B) 350.00 C) 233.33 D) 175.00 E) 275.00 Answer: D Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 56) The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source Regression Error Total

SS 700

1000

The MSE value is . A) 8.57 B) 8.82 C) 10.00 D) 75.00 E) 20.00 Answer: C Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

2 53

57) The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source Regression Error Total

SS 700

1000

The observed F value is . A) 17.50 B) 2.33 C) 0.70 D) 0.43 E) 0.50 Answer: A Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 58) The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source Regression Error Total

SS 700

1000

The value of the standard error of the estimate se is . A) 13.23 B) 3.16 C) 17.32 D) 26.46 E) 10.00 Answer: B Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

2 54

59) The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables Source Regression Error Total

SS 700

1000

The R2 value is _. A) 0.80 B) 0.70 C) 0.66 D) 0.76 E) 0.30 Answer: B Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 60) The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables Source Regression Error Total

SS 700

1000

The adjusted R2 value is . A) 0.80 B) 0.70 C) 0.66 D) 0.76 E) 0.30 Answer: C Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

2 55

61) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The sample size for the analysis is . A) 30 B) 26 C) 3 D) 29 E) 31 Answer: A Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 62) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The number of independent variables in the analysis is . A) 30 B) 26 C) 1 D) 3 E) 2 Answer: D Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

2 56

63) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The MSR value is . A) 1500 B) 50 C) 2300 D) 500 E) 31 Answer: D Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 64) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The SSE value is . A) 30 B) 1500 C) 500 D) 800 E) 2300 Answer: D Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

65) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The MSE value is closest to . A) 31 B) 500 C) 16 D) 2300 E) 8.7 Answer: A Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 66) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The observed F value is . A) 16.25 B) 30.77 C) 500 D) 0.049 E) 0.039 Answer: A Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

67) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The value of the standard error of the estimate se is . A) 30.77 B) 5.55 C) 4.03 D) 3.20 E) 0.73 Answer: B Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 68) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The R2 value is _. A) 0.65 B) 0.53 C) 0.35 D) 0.43 E) 1.37 Answer: A Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model.

69) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 26

SS 1500

2300

The adjusted R2 value is . A) 0.65 B) 0.39 C) 0.61 D) 0.53 E) 0.78 Answer: C Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. 70) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 624.5369 8.569122 4.736515 df 2 11 13

Standard Error 78.49712 1.652255 0.699194

t Statistic p-value 7.956176 6.88E-06 5.186319 0.000301 6.774248 3.06E-05

SS MS F 1660914 830457.1 58.31956 156637.5 14239.77 1817552

p-value 1.4E-06

These results indicate that . A) none of the predictor variables are significant at the 5% level B) each predictor variable is significant at the 5% level C) x1 is the only predictor variable significant at the 5% level D) x2 is the only predictor variable significant at the 5% level E) the intercept is not significant at 5% level Answer: B Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs.

71) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 624.5369 8.569122 4.736515 df 2 11 13

Standard Error 78.49712 1.652255 0.699194

t Statistic p-value 7.956176 6.88E-06 5.186319 0.000301 6.774248 3.06E-05

SS MS F 1660914 830457.1 58.31956 156637.5 14239.77 1817552

p-value 1.4E-06

For x1= 30 and x2 = 100, the predicted value of y is . A) 753.77 B) 1,173.00 C) 1,355.26 D) 615.13 E) 6153.13 Answer: C Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 72) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 624.5369 8.569122 4.736515 df 2 11 13

Standard Error 78.49712 1.652255 0.699194

t Statistic p-value 7.956176 6.88E-06 5.186319 0.000301 6.774248 3.06E-05

SS MS F 1660914 830457.1 58.31956 156637.5 14239.77 1817552

p-value 1.4E-06

The coefficient of multiple determination is . A) 0.0592 B) 0.9138 C) 0.1149 D) 0.9559 E) 1.0000 Answer: B Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 34

73) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 624.5369 8.569122 4.736515 df 2 11 13

Standard Error 78.49712 1.652255 0.699194

t Statistic p-value 7.956176 6.88E-06 5.186319 0.000301 6.774248 3.06E-05

SS MS F 1660914 830457.1 58.31956 156637.5 14239.77 1817552

p-value 1.4E-06

The adjusted R2 is . A) 0.9138 B) 0.9408 C) 0.8981 D) 0.8851 E) 0.8891 Answer: C Diff: 2 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 74) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients -139.609 24.24619 32.10171 df 2 13 15

Standard Error 2548.989 22.25267 17.44559

t Statistic p-value -0.05477 0.957154 1.089586 0.295682 1.840105 0.08869

SS MS F p-value 302689 151344.5 1.705942 0.219838 1153309 88716.07 1455998

The regression equation for this analysis is . A) ŷ = 302689 + 1153309 x1 + 1455998 x2 B) ŷ = -139.609 + 24.24619 x1 + 32.10171 x2 C) ŷ = 2548.989 + 22.25267 x1 + 17.44559 x2 D) ŷ = -0.05477 + 1.089586 x1 + 1.840105 x2 E) ŷ = 0.05477 + 1.089586 x1 + 1.840105 x2 Answer: B Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 35

75) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients -139.609 24.24619 32.10171 df 2 13 15

Standard Error 2548.989 22.25267 17.44559

t Statistic p-value -0.05477 0.957154 1.089586 0.295682 1.840105 0.08869

SS MS F p-value 302689 151344.5 1.705942 0.219838 1153309 88716.07 1455998

The sample size for this analysis is . A) 17 B) 13 C) 16 D) 11 E) 15 Answer: C Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 76) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients -139.609 24.24619 32.10171 df 2 13 15

Standard Error 2548.989 22.25267 17.44559

t Statistic p-value -0.05477 0.957154 1.089586 0.295682 1.840105 0.08869

SS MS F p-value 302689 151344.5 1.705942 0.219838 1153309 88716.07 1455998

Using α = 0.01 to test the null hypothesis H0: β 1 = β 2 = 0, the critical F value is . A) 5.99 B) 5.70 C) 1.96 D) 4.84 E) 6.70 Answer: E Diff: 2 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 36

77) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients -139.609 24.24619 32.10171 df 2 13 15

Standard Error 2548.989 22.25267 17.44559

t Statistic p-value -0.05477 0.957154 1.089586 0.295682 1.840105 0.08869

SS MS F p-value 302689 151344.5 1.705942 0.219838 1153309 88716.07 1455998

Using α = 0.01 to test the null hypothesis H0: β2 = 0, the critical t value is _ . A) ± 1.174 B) ± 2.093 C) ± 2.131 D) ± 4.012 E) ± 3.012 Answer: E Diff: 2 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 78) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients -139.609 24.24619 32.10171 df 2 13 15

Standard Error 2548.989 22.25267 17.44559

t Statistic p-value -0.05477 0.957154 1.089586 0.295682 1.840105 0.08869

SS MS F p-value 302689 151344.5 1.705942 0.219838 1153309 88716.07 1455998

These results indicate that . A) none of the predictor variables are significant at the 5% level B) each predictor variable is significant at the 5% level C) x1 is the only predictor variable significant at the 5% level D) x2 is the only predictor variable significant at the 5% level E) all variables are significant at 5% level Answer: A Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 37

79) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients -139.609 24.24619 32.10171 df 2 13 15

Standard Error 2548.989 22.25267 17.44559

t Statistic p-value -0.05477 0.957154 1.089586 0.295682 1.840105 0.08869

SS MS F p-value 302689 151344.5 1.705942 0.219838 1153309 88716.07 1455998

For x1= 40 and x2 = 90, the predicted value of y is . A) 753.77 B) 1,173.00 C) 1,355.26 D) 3,719.39 E) 1,565.75 Answer: D Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 80) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients -139.609 24.24619 32.10171 df 2 13 15

Standard Error 2548.989 22.25267 17.44559

t Statistic p-value -0.05477 0.957154 1.089586 0.295682 1.840105 0.08869

SS MS F p-value 302689 151344.5 1.705942 0.219838 1153309 88716.07 1455998

The coefficient of multiple determination is . A) 0.2079 B) 0. 0860 C) 0.5440 D) 0.7921 E) 0.5000 Answer: A Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. 38

81) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients -139.609 24.24619 32.10171 df 2 13 15

Standard Error 2548.989 22.25267 17.44559

t Statistic p-value -0.05477 0.957154 1.089586 0.295682 1.840105 0.08869

SS MS F p-value 302689 151344.5 1.705942 0.219838 1153309 88716.07 1455998

The adjusted R2 is . A) 0.2079 B) 0.0860 C) 0.5440 D) 0.7921 E) 1.0000 Answer: B Diff: 2 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs.

82) A multiple regression analysis produced the following output from Minitab. Regression Analysis: Y versus x1 and x2 Predictor Coef SE Coef T Constant -0.0626 0.2034 -0.31 x1 1.1003 0.5441 2.02 x2 -0.8960 0.5548 -1.61 S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8% Analysis of Variance Source DF Regression 2 Residual Error 18 Residual Error 20

SS 4.7013 0.5796 5.2809

MS 2.3506 0.0322

P 0.762 0.058 0.124

F 73.00

P 0.000

These results indicate that . A) none of the predictor variables are significant at the 5% level B) each predictor variable is significant at the 5% level C) x1 is the only predictor variable significant at the 5% level D) x2 is the only predictor variable significant at the 5% level E) at least one of the variables is significant at 5% level Answer: E Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs.

83) A multiple regression analysis produced the following output from Minitab. Regression Analysis: Y versus x1 and x2 Predictor Coef SE Coef T Constant -0.0626 0.2034 -0.31 x1 1.1003 0.5441 2.02 x2 -0.8960 0.5548 -1.61 S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8% Analysis of Variance Source DF Regression 2 Residual Error 18 Residual Error 20

SS 4.7013 0.5796 5.2809

MS 2.3506 0.0322

P 0.762 0.058 0.124

F 73.00

P 0.000

The overall proportion of variation of y accounted by x1 and x2 is _. A) 0.179 B) 0.89 C) 0.878 D) 0.203 E) 0.5441 Answer: B Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs.

4 83

84) A multiple regression analysis produced the following output from Excel.

The overall proportion of variation of y accounted by x1 and x2 is _. A) 0.9787 B) 0.9579 C) 0.9523 D) 67.671 E) 0.0489 Answer: B Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs.

4 84

85) A multiple regression analysis produced the following output from Excel.

The coefficient of multiple determination is . A) 0.9787 B) 0.9579 C) 0.9523 D) 67.671 E) 0.0489 Answer: B Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs.

4 85

86) A multiple regression analysis produced the following output from Excel.

The correlation coefficient is . A) 0.9787 B) 0.9579 C) 0.9523 D) 67.671 E) 0.0489 Answer: A Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs.

4 86

87) In the regression equation ŷ = 1959.71 - 0.46 x1 + 2.16 x2, suppose that you are considering the point (x1, x2) = (1.5, 0.5), and furthermore, suppose that the variable x1 increases by a factor of 2 (i.e., it doubles). What must be the change in the variable x2 so that y remains unchanged? A) 2.16 B) -2.16 C) 0.319 D) -0.319 E) 0.638 Answer: C Diff: 2 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. Bloom's level: Application 88) Suppose that the regression equation ŷ = 16.99 + 0.32 x1 + 0.41 x2 + 5.31 x3 predicts an adult's height (y) given the individual's mother's height (x1), his or her father's height (x2), and whether the individual is male (x3 = 1) or female (x3 = 0). All heights are measured in inches. In this equation, the coefficient of means that . A) x2; if two individuals have fathers whose heights differ by 1 inch, then the individuals' heights will differ by 0.41 inches. B) x2; if two individuals have mothers whose heights differ by 1 inch, then the individuals' heights will differ by 0.41 inches. C) x3; a brother is expected to be 5.31 inches taller than his sister D) x1; if two individuals have mothers whose heights differ by 0.32 inch, then the individuals' heights will differ by 1 inch. E) x1; if two individuals have mothers whose heights differ by 0.5 inch, then the individuals' heights will differ by 0.32 inch. Answer: C Diff: 3 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. Bloom's level: Application

4 87

89) Suppose that the regression equation ŷ = 16.99 + 0.32 x1 + 0.41 x2 + 5.31 x3 predicts an adult's height (y) given the individual's mother's height (x1), his or her father's height (x2), and whether the individual is male (x3 = 1) or female (x3 = 0). All heights are measured in inches. Assume also that this equation is stable through time, the average adult female height is currently 63.8 inches and the average adult male height is 69.7 inches. Approximately what would be the average female height in two generations? You can assume that each individual has parents of average height. A) There is not enough information to determine the average female height in two generations. B) 66 inches. C) 67.25 inches D) 67.33 inches E) 67.82 inches Answer: D Diff: 3 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. Bloom's level: Application 90) Suppose that the regression equation ŷ = c1 + 0.32 x1 + 0.41 x2 + 5.31 x3 predicts an adult's height (y) given the individual's mother's height (x1), his or her father's height (x2), and whether the individual is male (x3 = 1) or female (x3 = 0). All heights are measured in inches. Assume also that this equation is stable through time, the average adult female height is currently 63.8 inches and the average adult male height is 69.7 inches. If the average female height is stable through time (daughters are on average exactly as tall their mothers), then c1 = . A) There is not enough information to determine the average female height in two generations. B) 16.515 C) 15.751 D) 14.807 E) 13.155 Answer: D Diff: 3 Response: See section 13.1 The Multiple Regression Model Learning Objective: 13.1: Explain how, by extending the simple regression model to a multiple regression model with two independent variables, it is possible to determine the multiple regression equation for any number of unknowns. Bloom's level: Application

4 88

91) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 752.0833 11.87375 1.908183 df 2 12 14

Standard Error 336.3158 5.32047 0.662742

t Statistic p-value 2.236241 0.042132 2.231711 0.042493 2.879226 0.01213

SS MS F p-value 203693.3 101846.7 6.745406 0.010884 181184.1 15098.67 384877.4

Using α = 0.10 to test the null hypothesis H0: β1 = β2 = 0, the critical F value is . A) 2.57 B) 2.81 C) 3.23 D) 3.89 E) 3.95 Answer: B Diff: 1 Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients Learning Objective: 13.2: Examine significance tests of both the overall regression model and the regression coefficients. Bloom's level: Application 92) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 22

SS 8157.7

MS 4068.5 135.1

F 27.57

p 0.000

11018.4

The adjusted R2 value is closest to . A) 0.65 B) 0.67 C) 0.68 D) 0.70 E) 0.73 Answer: D Diff: 2 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. Bloom's level: Application

4 89

93) The following ANOVA table is from a multiple regression analysis. Source Regression Error Total

df 3 25

SS 1728

2571

The R2 value is _. A) 0.65 B) 0.67 C) 0.69 D) 0.71 E) 0.73 Answer: B Diff: 1 Response: See section 13.3 Residuals, Standard Error of the Estimate, and R2 Learning Objective: 13.3: Calculate the residual, standard error of the estimate, coefficient of multiple determination, and adjusted coefficient of multiple determination of a regression model. Bloom's level: Application

4 90

94) A multiple regression analysis produced the following tables. Predictor Intercept x1 x2 Source Regression Residual Total

Coefficients 512.2359 7.1525 2.0208 df 2 11 13

Standard Error 78.49712 1.652255 0.699194

t Statistic p-value 7.956176 6.88E-06 5.186319 0.000301 6.774248 3.06E-05

SS MS F 1660914 830457.1 58.31956 156637.5 14239.7 1817552

p-value 1.4E-06

If x1= 25 and x2 = 85, then the predicted value of y is . A) 803.891 B) 807.255 C) 812.025 D) 825.517 E) 862.816 Answer: E Diff: 1 Response: See section 13.4 Interpreting Multiple Regression Computer Output Learning Objective: 13.4: Use a computer to find and interpret multiple regression outputs. Bloom's level: Application

4 91

Business Statistics, 11e (Black) Chapter 14 Building Multiple Regression Models 1) Regression models in which the highest power of any predictor variable is 1 and in which there are no cross product terms are referred to as first-order models. Answer: TRUE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 2) The regression model y = β0 + β1 x1 + β2 x2 + β3 x1x2 + ε is a first order model. Answer: FALSE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 3) The regression model y = β0 + β1 x1 + β2 x2 + β3 x3 + ε is a third order model. Answer: FALSE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 4) The regression model y = β0 + β1 x1 + β2 x21 + ε is called a quadratic model. Answer: TRUE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 5) A linear regression model cannot be used to explore the possibility that a quadratic relationship may exist between two variables. Answer: FALSE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

6) A linear regression model can be used to explore the possibility that a quadratic relationship may exist between two variables by suitably transforming the independent variable. Answer: TRUE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 7) Recoding data cannot improve the fit of a regression model. Answer: FALSE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 8) A logarithmic transformation may be applied to both positive and negative numbers. Answer: FALSE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 9) If a square-transformation is applied to a series of positive numbers, all greater than 1, the numerical values of the numbers in the transformed series will be smaller than the corresponding numbers in the original series. Answer: FALSE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 10) If the effect of an independent variable (e.g., square footage) on a dependent variable (e.g., price) is affected by different ranges of values for a second independent variable (e.g., age ), the two independent variables are said to interact. Answer: TRUE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

11) The interaction between two independent variables can be examined by including a new variable, which is the sum of the two independent variables, in the regression model. Answer: FALSE Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 12) Qualitative data can be incorporated into linear regression models using indicator variables. Answer: TRUE Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 13) A qualitative variable which represents categories such as geographical territories or job classifications may be included in a regression model by using indicator or dummy variables. Answer: TRUE Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 14) If a qualitative variable has c categories, then c dummy variables must be included in the regression model, one for each category. Answer: FALSE Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 15) If a qualitative variable has c categories, then only (c - 1) dummy variables must be included in the regression model. Answer: TRUE Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.

16) If a data set contains k independent variables, the "all possible regression" search procedure will determine 2k different models. Answer: FALSE Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. 17) If a data set contains k independent variables, the "all possible regression" search procedure will determine 2k - 1 different models. Answer: TRUE Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. 18) Stepwise regression is one of the ways to prevent the problem of multicollinearity. Answer: TRUE Diff: 2 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. 19) If two or more independent variables are highly correlated, the regression analysis is unlikely to suffer from the problem of multicollinearity. Answer: FALSE Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. 20) If each pair of independent variables is weakly correlated, there is no problem of multicollinearity. Answer: FALSE Diff: 2 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it.

21) If the variance inflation factor is bigger than 10, the regression analysis might suffer from the problem of multicollinearity. Answer: TRUE Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. 22) We may use logistic regression when the dependent variable is a dummy variable, coded 0 or 1. Answer: TRUE Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results. 23) The logistic regression model constrains the estimated probabilities to lie between 0 and 100. Answer: FALSE Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results. 24) When structuring a logistic regression model, only one independent or predictor variable can be used. Answer: FALSE Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results. 25) To test the overall effectiveness of a logistic regression, a chi-squared statistic is used. Answer: TRUE Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

26) Multiple linear regression models can handle certain nonlinear relationships by . A) biasing the sample B) recoding or transforming variables C) adjusting the resultant ANOVA table D) adjusting the observed t and F values E) performing nonlinear regression Answer: B Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 27) The following scatter plot indicates that

A) a log x transform may be useful B) a y2 transform may be useful C) a x2 transform may be useful D) no transform is needed E) a 1/x transform may be useful Answer: C Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

7) The following scatter plot indicates that

A) a log x transform may be useful B) a log y transform may be useful C) a x2 transform may be useful D) no transform is needed E) a 1/y transform may be useful Answer: A Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

8) The following scatter plot indicates that

A) a log x transform may be useful B) a log y transform may be useful C) an x2 transform may be useful D) no transform is needed E) a (-x) transform may be useful Answer: C Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

9) The following scatter plot indicates that

A) a x2 transform may be useful B) a log y transform may be useful C) a x4 transform may be useful D) no transform is needed E) a x3 transform may be useful Answer: B Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

10) A multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 1411.876 35.18215

Standard Error 762.1533 96.8433

t Statistic 1.852483 0.363289

p-value 0.074919 0.719218

7.721648

3.007943

2.567086

0.016115

df 2 25 27

SS 58567032 12765573 71332605

MS F 29283516 57.34861 510622.9

The regression equation for this analysis is . A) ŷ = 762.1533 + 96.8433 x1 + 3.007943 x12 B) ŷ = 1411.876 + 762.1533 x1 + 1.852483 x12 C) ŷ = 1411.876 + 35.18215 x1 + 7.721648 x12 D) ŷ = 762.1533 + 1.852483 x1 + 0.074919 x12 E) ŷ = 762.1533 - 1.852483 x1 + 0.074919 x12 Answer: C Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

11) A multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 1411.876 35.18215

Standard Error 762.1533 96.8433

t Statistic 1.852483 0.363289

p-value 0.074919 0.719218

7.721648

3.007943

2.567086

0.016115

df 2 25 27

SS 58567032 12765573 71332605

MS F 29283516 57.34861 510622.9

The sample size for this analysis is . A) 28 B) 25 C) 30 D) 27 E) 2 Answer: A Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 11

12) A multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 1411.876 35.18215

Standard Error 762.1533 96.8433

t Statistic 1.852483 0.363289

p-value 0.074919 0.719218

7.721648

3.007943

2.567086

0.016115

df 2 25 27

SS 58567032 12765573 71332605

MS F 29283516 57.34861 510622.9

Using α = 0.05 to test the null hypothesis H0: β1 = β2 = 0, the critical F value is . A) 4.24 B) 3.39 C) 5.57 D) 3.35 E) 2.35 Answer: B Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 12

13) A multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 1411.876 35.18215

Standard Error 762.1533 96.8433

t Statistic 1.852483 0.363289

p-value 0.074919 0.719218

7.721648

3.007943

2.567086

0.016115

df 2 25 27

SS 58567032 12765573 71332605

MS F 29283516 57.34861 510622.9

Using α = 0.10 to test the null hypothesis H0: β1 = 0, the critical t value is _ . A) ± 1.316 B) ± 1.314 C) ± 1.703 D) ± 1.780 E) ± 1.708 Answer: E Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 13

14) A multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 1411.876 35.18215

Standard Error 762.1533 96.8433

t Statistic 1.852483 0.363289

p-value 0.074919 0.719218

7.721648

3.007943

2.567086

0.016115

df 2 25 27

SS 58567032 12765573 71332605

MS F 29283516 57.34861 510622.9

Using α = 0.10 to test the null hypothesis H0: β2 = 0, the critical t value is _ . A) ± 1.316 B) ± 1.314 C) ± 1.703 D) ± 1.780 E) ± 1.708 Answer: E Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 14

15) A multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 1411.876 35.18215

Standard Error 762.1533 96.8433

t Statistic 1.852483 0.363289

p-value 0.074919 0.719218

7.721648

3.007943

2.567086

0.016115

df 2 25 27

SS 58567032 12765573 71332605

MS F 29283516 57.34861 510622.9

For x1= 10, the predicted value of y is . A) 8.88. B) 2,031.38 C) 2,53.86 D) 262.19 E) 2,535.86 Answer: E Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 15

16) A multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 1411.876 35.18215

Standard Error 762.1533 96.8433

t Statistic 1.852483 0.363289

p-value 0.074919 0.719218

7.721648

3.007943

2.567086

0.016115

df 2 25 27

SS 58567032 12765573 71332605

MS F 29283516 57.34861 510622.9

For x1= 20, the predicted value of y is . A) 5,204.18. B) 2,031.38 C) 2,538.86 D) 6262.19 E) 6,535.86 Answer: A Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 16

17) A local parent group was concerned with the increasing cost of school for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 707.9144 2.903307

Standard Error 435.1183 81.62802

t Statistic 1.626947 0.035568

p-value 0.114567 0.971871

11.91297

3.806211

3.129878

0.003967

df 2 27 29

SS 32055153 9140128 41195281

MS 16027577 338523.3

F 47.34557

p-value 1.49E-09

The regression equation for this analysis is . A) ŷ = 707.9144 + 2.903307 x1 + 11.91297 x12 B) ŷ = 707.9144 + 435.1183 x1 + 1.626947 x12 C) ŷ = 435.1183 + 81.62802 x1 + 3.806211 x12 D) ŷ = 1.626947 + 0.035568 x1 + 3.129878 x12 E) ŷ = 1.626947 + 0.035568 x1 - 3.129878 x12 Answer: A Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 17

18) A local parent group was concerned with the increasing cost of school for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 707.9144 2.903307

Standard Error 435.1183 81.62802

t Statistic 1.626947 0.035568

p-value 0.114567 0.971871

11.91297

3.806211

3.129878

0.003967

df 2 27 29

SS 32055153 9140128 41195281

MS 16027577 338523.3

F 47.34557

p-value 1.49E-09

The sample size for this analysis is . A) 27 B) 29 C) 30 D) 25 E) 28 Answer: C Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 18

19) A local parent group was concerned with the increasing cost of school for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 707.9144 2.903307

Standard Error 435.1183 81.62802

t Statistic 1.626947 0.035568

p-value 0.114567 0.971871

11.91297

3.806211

3.129878

0.003967

df 2 27 29

SS 32055153 9140128 41195281

MS 16027577 338523.3

F 47.34557

p-value 1.49E-09

Using α = 0.01 to test the null hypothesis H0: β1 = β2 = 0, the critical F value is . A) 5.42 B) 5.49 C) 7.60 D) 3.35 E) 2.49 Answer: B Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

1 19

20) A local parent group was concerned with the increasing cost of school for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 707.9144 2.903307

Standard Error 435.1183 81.62802

t Statistic 1.626947 0.035568

p-value 0.114567 0.971871

11.91297

3.806211

3.129878

0.003967

df 2 27 29

SS 32055153 9140128 41195281

MS 16027577 338523.3

F 47.34557

p-value 1.49E-09

Using α = 0.05 to test the null hypothesis H0: β1 = 0, the critical t value is _ . A) ± 1.311 B) ± 1.699 C) ± 1.703 D) ± 2.502 E) ± 2.052 Answer: E Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

21) A local parent group was concerned with the increasing cost of school for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 707.9144 2.903307

Standard Error 435.1183 81.62802

t Statistic 1.626947 0.035568

p-value 0.114567 0.971871

11.91297

3.806211

3.129878

0.003967

df 2 27 29

SS 32055153 9140128 41195281

MS 16027577 338523.3

F 47.34557

p-value 1.49E-09

Using α = 0.05 to test the null hypothesis H0: β2 = 0, the critical t value is _ . A) ± 1.311 B) ± 1.699 C) ± 1.703 D) ± 2.052 E) ± 2.502 Answer: D Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

2 21

22) A local parent group was concerned with the increasing cost of school for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 707.9144 2.903307

Standard Error 435.1183 81.62802

t Statistic 1.626947 0.035568

p-value 0.114567 0.971871

11.91297

3.806211

3.129878

0.003967

df 2 27 29

SS 32055153 9140128 41195281

MS 16027577 338523.3

F 47.34557

p-value 1.49E-09

These results indicate that . A) none of the predictor variables is significant at the 5% level B) each predictor variable is significant at the 5% level C) x1 is the only predictor variable significant at the 5% level D) x12 is the only predictor variable significant at the 5% level E) each predictor variable is insignificant at the 5% level Answer: D Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

2 22

23) A local parent group was concerned with the increasing cost of school for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 707.9144 2.903307

Standard Error 435.1183 81.62802

t Statistic 1.626947 0.035568

p-value 0.114567 0.971871

11.91297

3.806211

3.129878

0.003967

df 2 27 29

SS 32055153 9140128 41195281

MS 16027577 338523.3

F 47.34557

p-value 1.49E-09

For a child in grade 5 (x1 = 5), the predicted value of y is . A) 707.91 B) 1,020.26 C) 781.99 D) 840.06 E) 1078.32 Answer: B Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

2 23

24) A local parent group was concerned with the increasing cost of school for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.

Intercept x1 x12

Regression Residual Total

Coefficients 707.9144 2.903307

Standard Error 435.1183 81.62802

t Statistic 1.626947 0.035568

p-value 0.114567 0.971871

11.91297

3.806211

3.129878

0.003967

df 2 27 29

SS 32055153 9140128 41195281

MS 16027577 338523.3

F 47.34557

p-value 1.49E-09

For a child in grade 10 (x1 = 10) the predicted value of y is . A) 707.91 B) 1,117.38 C) 856.08 D) 2,189.54 E) 1,928.24 Answer: E Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

2 24

46) After a transformation of the y-variable values into log y, and performing a regression analysis produced the following tables.

Intercept x

Coefficients 2.005349 0.027126

Regression Residual Total

df 1 26 27

Standard Error 0.097351 0.009518

SS 0.196642 0.629517 0.826159

MS 0.196642 0.024212

t Statistic 20.59923 2.849843

p-value 4.81E-18 0.008275

F 8.121607

p-value 0.008447

For x1 = 10, the predicted value of y is . A) 155.79 B) 1.25 C) 2.42 D) 189.06 E) 18.90 Answer: D Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. 47) In multiple regression analysis, qualitative variables are sometimes referred to as . A) dummy variables B) quantitative variables C) dependent variables D) performance variables E) cardinal variables Answer: A Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.

2 25

48) If a qualitative variable has 4 categories, how many dummy variables must be created and used in the regression analysis? A) 3 B) 4 C) 5 D) 6 E) 7 Answer: A Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 49) If a qualitative variable has "c" categories, how many dummy variables must be created and used in the regression analysis? A) c - 1 B) c C) c + 1 D) c - 2 E) 4 + c Answer: A Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 50) Yvonne Yang, VP of Finance at Discrete Components, Inc. (DCI), wants a regression model which predicts the average collection period on credit sales. Her data set includes two qualitative variables: sales discount rates (0%, 2%, 4%, and 6%), and total assets of credit customers (small, medium, and large). The number of dummy variables needed for "sales discount rate" in Yvonne's regression model is . A) 1 B) 2 C) 3 D) 4 E) 7 Answer: C Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.

2 26

51) Yvonne Yang, VP of Finance at Discrete Components, Inc. (DCI), wants a regression model which predicts the average collection period on credit sales. Her data set includes two qualitative variables: sales discount rates (0%, 2%, 4%, and 6%), and total assets of credit customers (small, medium, and large). The number of dummy variables needed for "total assets of credit customer" in Yvonne's regression model is . A) 1 B) 2 C) 3 D) 4 E) 7 Answer: B Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 52) Hope Hernandez is the new regional Vice President for a large gasoline station chain. She wants a regression model to predict sales in the convenience stores. Her data set includes two qualitative variables: the gasoline station location (inner city, freeway, and suburbs), and curb appeal of the convenience store (low, medium, and high). The number of dummy variables needed for "curb appeal" in Hope's regression model is . A) 1 B) 2 C) 3 D) 4 E) 5 Answer: B Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 53) Hope Hernandez is the new regional Vice President for a large gasoline station chain. She wants a regression model to predict sales in the convenience stores. Her data set includes two qualitative variables: the gasoline station location (inner city, freeway, and suburbs), and curb appeal of the convenience store (low, medium, and high). The number of dummy variables needed for Hope's regression model is . A) 2 B) 4 C) 6 D) 8 E) 9 Answer: B Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 2 27

54) Alan Bissell, a market analyst for City Sound Online Mart, is analyzing sales from heavy metal song downloads. Alan's dependent variable is annual heavy metal song download sales (in $1,000,000's), and his independent variables are website visitors (in 1,000's) and type of download format requested (0 = MP3, 1 = other). Regression analysis of the data yielded the following tables. Coefficients Intercept 1.7 x1(website visitors) 0.04 x2(download format) -1.5666667

Standard Error 0.384212 0.014029 0.20518

t Statistic 4.424638 2.851146 -7.63558

p-value 0.00166 0.019054 3.21E-05

Alan's model is . A) ŷ = 1.7 + 0.384212 x1 + 4.424638 x2 + 0.00166 x3 B) ŷ = 1.7 + 0.04 x1 + 1.5666667 x2 C) ŷ = 0.384212 + 0.014029 x1 + 0.20518 x2 D) ŷ = 4.424638 + 2.851146 x1 - 7.63558 x2 E) ŷ = 1.7 + 0.04 x1 - 1.5666667 x2 Answer: E Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.

2 28

29) Alan Bissell, a market analyst for City Sound Online Mart, is analyzing sales from heavy metal song downloads. Alan's dependent variable is annual heavy metal song download sales (in $1,000,000's), and his independent variables are website visitors (in 1,000's) and type of download format requested (0 = MP3, 1 = other). Regression analysis of the data yielded the following tables. Coefficients Intercept 1.7 x1(website visitors) 0.04 x2(download format) -1.5666667

Standard Error 0.384212 0.014029 0.20518

t Statistic 4.424638 2.851146 -7.63558

p-value 0.00166 0.019054 3.21E-05

For MP3 sales with 10,000 website visitors, Alan's model predicts annual sales of heavy metal song downloads of . A) $2,100,000 B) $524,507 C) $533,333 D) $729,683 E) $21,000,000 Answer: A Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.

2 29

30) Alan Bissell, a market analyst for City Sound Online Mart, is analyzing sales from heavy metal song downloads. Alan's dependent variable is annual heavy metal song download sales (in $1,000,000's), and his independent variables are website visitors (in 1,000's) and type of download format requested (0 = MP3, 1 = other). Regression analysis of the data yielded the following tables. Coefficients Intercept 1.7 x1(website visitors) 0.04 x2(download format) -1.5666667

Standard Error 0.384212 0.014029 0.20518

t Statistic 4.424638 2.851146 -7.63558

p-value 0.00166 0.019054 3.21E-05

For "other" download formats with 10,000 website visitors, Alan's model predicts annual sales of heavy metal song downloads of . A) $2,100,000 B) $524,507 C) $533,333 D) $729,683 E) $210,000 Answer: C Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.

31) Alan Bissell, a market analyst for City Sound Online Mart, is analyzing sales from heavy metal song downloads. Alan's dependent variable is annual heavy metal song download sales (in $1,000,000's), and his independent variables are website visitors (in 1,000's) and type of download format requested (0 = MP3, 1 = other). Regression analysis of the data yielded the following tables. Coefficients Intercept 1.7 x1(website visitors) 0.04 x2(download format) -1.5666667

Standard Error 0.384212 0.014029 0.20518

t Statistic 4.424638 2.851146 -7.63558

p-value 0.00166 0.019054 3.21E-05

For the same number of website visitors, what is difference between the predicted sales for MP3 versus "other" heavy metal song downloads? A) $1,566,666 higher sales for "other" formats B) the same sales for both formats C) $1,566,666 lower sales for the "other" format D) $1,700,000 higher sales for the MP3 format E) $1,700,000 lower sales for the "other" format Answer: C Diff: 2 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 58) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table.

Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

Abby's model is . A) ŷ = 19.68247 + 10.01176 x1 + 1.965934 x2 B) ŷ = 1.965934 + 9.940612 x1 + 6.416667 x2 C) ŷ = 10.01176 + 0.174564 x1 + 7.655776 x2 D) ŷ = 19.68247 - 1.735272 x1 + 49.12456 x2 E) ŷ = 19.68247 + 1.735272 x1 + 49.12456 x2 Answer: E Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 3 31

59) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table.

Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

For a rural household with $90,000 annual income, Abby's model predicts weekly grocery expenditure of . A) $156.19 B) $224.98 C) $444.62 D) $141.36 E) $175.86 Answer: B Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.

3 32

60) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table.

Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

For a suburban household with $90,000 annual income, Abby's model predicts weekly grocery expenditure of . A) $156.19 B) $224.98 C) $444.62 D) $141.36 E) $175.86 Answer: E Diff: 1 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 61) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table.

Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

For two households, one suburban and one rural, Abby's model predicts . A) equal weekly expenditures for groceries B) the suburban household's weekly expenditures for groceries will be $49 more C) the rural household's weekly expenditures for groceries will be $49 more D) the suburban household's weekly expenditures for groceries will be $8 more E) the rural household's weekly expenditures for groceries will be $49 less Answer: C Diff: 2 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. 3 33

62) Which of the following iterative search procedures for model-building in a multiple regression analysis reevaluates the contribution of variables previously include in the model after entering a new independent variable? A) Backward elimination B) Stepwise regression C) Forward selection D) All possible regressions E) Backward selection Answer: B Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. 63) Which of the following iterative search procedures for model-building in a multiple regression analysis starts with all independent variables in the model and then drops nonsignificant independent variables is a step-by-step manner? A) Backward elimination B) Stepwise regression C) Forward selection D) All possible regressions E) Backward selection Answer: A Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. 64) Which of the following iterative search procedures for model-building in a multiple regression analysis adds variables to model as it proceeds, but does not reevaluate the contribution of previously entered variables? A) Backward elimination B) Stepwise regression C) Forward selection D) All possible regressions E) Forward elimination Answer: C Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious.

3 34

65) An "all possible regressions" search of a data set containing 7 independent variables will produce regressions. A) 13 B) 127 C) 48 D) 64 E) 97 Answer: B Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. 66) An "all possible regressions" search of a data set containing 5 independent variables will produce regressions. A) 31 B) 10 C) 25 D) 32 E) 24 Answer: A Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. 67) An "all possible regressions" search of a data set containing 8 independent variables will produce regressions. A) 8 B) 15 C) 256 D) 64 E) 255 Answer: E Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious.

3 35

68) An "all possible regressions" search of a data set containing "k" independent variables will produce regressions. A) 2k -1 B) 2k + 1 C) k2 - 1 D) 2k - 1 E) 2k Answer: D Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. 69) Inspection of the following table of t values for variables in a multiple regression analysis reveals that the first independent variable entered by the forward selection procedure will be . y y x1 x2 x3 x4 x5

1 -0.1661 0.231849 0.423522 -0.33227 0.199796

1 -0.51728 1 -0.22264 -0.00734 1 0.028957 -0.49869 0.260586 1 -0.20467 0.078916 0.207477 0.023839

A) x2 B) x3 C) x4 D) x5 E) x1 Answer: B Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious.

3 36

37) Inspection of the following table of t values for variables in a multiple regression analysis reveals that the first independent variable entered by the forward selection procedure will be . y y x1 x2 x3 x4 x5

1 -0.44008 0.566053 0.064919 -0.35711 0.426363

1 -0.51728 1 -0.22264 -0.00734 1 0.028957 -0.49869 0.260586 1 -0.20467 0.078916 0.207477 0.023839

A) x1 B) x2 C) x3 D) x4 E) x5 Answer: B Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious.

3 37

38) Inspection of the following table of t values for variables in a multiple regression analysis reveals that the first independent variable that will be entered into the regression model by the forward selection procedure will be . y y x1 x2 x3 x4 x5

1 -0.0857 1 -0.20246 0.868358 1 -0.22631 -0.10604 -0.14853 1 -0.28175 -0.0685 0.41468 -0.14151 0.271105 0.150796 0.129388 -0.15243

1 0.00821

A) x1 B) x2 C) x3 D) x4 E) x5 Answer: D Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious.

3 38

39) Inspection of the following table of t values for variables in a multiple regression analysis reveals that the first independent variable that will be entered into the regression model by the forward selection procedure will be . y y x1 x2 x3 x4 x5

1 0.854168 -0.11828 -0.12003 0.525901 -0.18105

1 -0.00383 1 -0.08499 -0.14523 1 0.118169 -0.14876 0.050042 1 -0.07371 0.995886 -0.14151 -0.16934

A) x1 B) x2 C) x3 D) x4 E) x5 Answer: A Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious.

3 39

73) Carlos Cavazos, Director of Human Resources, is exploring employee absenteeism at the Plano Piano Plant. A multiple regression analysis was performed using the following variables. The results are presented below. Variable Y x1 x2 x3 x4 x5

Description number of days absent last fiscal year commuting distance (in miles) employee's age (in years) single-parent household (0 = no, 1 = yes) length of employment at PPP (in years) shift (0 = day, 1 = night)

Intercept x1 x2 x3 x4 x5

Coefficients 6.594146 -0.18019 0.268156 -2.31068 -0.50579 2.329513

Regression Residual Total

df 5 67 72

R = 0.498191 se = 3.553858

Standard Error 3.273005 0.141949 0.260643 0.962056 0.270872 0.940321

t Statistic 2.014707 -1.26939 1.028828 -2.40182 -1.86725 2.47736

p-value 0.047671 0.208391 0.307005 0.018896 0.065937 0.015584

SS MS F p-value 279.358 55.8716 4.423755 0.001532 846.2036 12.6299 1125.562 R2 = 0.248194 n = 73

Adj R2 = 0.192089

Which of the following conclusions can be drawn from the above results? A) All the independent variables in the regression are significant at 5% level. B) Commuting distance is a highly significant (<1%) variable in explaining absenteeism. C) Age of the employees tends to have a very significant (<1%) effect on absenteeism. D) This model explains a little over 49% of the variability in absenteeism data. E) A single-parent household employee is expected to be absent fewer days, all other variables held constant, compared to one who is not a single-parent household. Answer: E Diff: 1 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious.

74) Large correlations between two or more independent variables in a multiple regression model could result in the problem of . A) multicollinearity B) autocorrelation C) partial correlation D) rank correlation E) non-normality Answer: A Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. 75) An appropriate method to identify multicollinearity in a regression model is to . A) examine a residual plot B) examine the ANOVA table C) examine a correlation matrix D) examine the partial regression coefficients E) examine the R2 of the regression model Answer: C Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. 76) An acceptable method of managing multicollinearity in a regression model is the . A) use the forward selection procedure B) use the backward elimination procedure C) use the forward elimination procedure D) use the stepwise regression procedure E) use all possible regressions Answer: D Diff: 2 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it.

77) A useful technique in controlling multicollinearity involves the use of . A) variance inflation factors B) a backward elimination procedure C) a forward elimination procedure D) a forward selection procedure E) all possible regressions Answer: A Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. 78) Inspection of the following table of correlation coefficients for variables in a multiple regression analysis reveals potential multicollinearity with variables . y y x1 x2 x3 x4 x5

1 -0.0857 1 -0.20246 0.868358 1 -0.22631 -0.10604 -0.14853 1 -0.28175 -0.0685 0.41468 -0.14151 0.271105 0.150796 0.129388 -0.15243

1 0.00821

A) x1 and x2 B) x1 and x4 C) x4 and x5 D) x4 and x3 E) x5 and y Answer: A Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it.

79) Inspection of the following table of correlation coefficients for variables in a multiple regression analysis reveals potential multicollinearity with variables . y y x1 x2 x3 x4 x5

1 -0.08301 0.236745 0.155149 0.022234 0.4808

1 -0.51728 1 -0.22264 -0.00734 1 -0.58079 0.884216 0.131956 1 -0.20467 0.078916 0.207477 0.103831

A) x1 and x5 B) x2 and x3 C) x4 and x2 D) x4 and x3 E) x4 and y Answer: C Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. 80) Inspection of the following table of correlation coefficients for variables in a multiple regression analysis reveals potential multicollinearity with variables . y y x1 x2 x3 x4 x5

1 0.854168 -0.11828 -0.12003 0.525901 -0.18105

1 -0.00383 1 -0.08499 -0.14523 1 0.118169 -0.14876 0.050042 1 -0.07371 0.995886 -0.14151 -0.16934

A) x1 and x2 B) x1 and x5 C) x3 and x4 D) x2 and x5 E) x3 and x5 Answer: D Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. 43

81) Suppose a company is interested in understanding the effect of age and sex on the likelihood a customer will purchase a new product. The data analyst intends to run a logistic regression on her data. Which of the following variable(s) will the analyst need to code as 0 or 1 prior to performing the logistic regression analysis? A) age and gender B) age and purchase status C) age D) purchase status E) sex and purchase status Answer: D Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results. 82) Suppose a community based political group is interested in determining if there is a relationship between the years that a candidate lives in a community, the number of volunteer hours the candidate gives to the community, and the outcome of the candidate in the local city council election. Which of the following statements is not true about the experimental design? A) The election outcome (win/ lose) is the response variable. B) The number of hours the candidate gives in volunteering is the dependent variable. C) A correlation analysis should evaluate possible multicollinearity between the years a candidate lives in a community and the number of volunteer hours. D) The number of years a candidate lives in a community is an independent variable. E) The only variable that must be recoded 0 or 1 is the response variable. Answer: B Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

5) A research project was conducted to study the effect of smoking and weight upon resting pulse rate. The response variable is coded as 1 when the pulse rate is low and 0 when it high. Smoking is also coding as 1 when smoking and 0 when not smoking. Shown below is the Minitab output from a logistic regression. Response Information Variable Rating Pulse

Value 1 0 Total

Count 70 (Event) 22 92

Logistic Regression Table Predictor Constant Weight Smokes

Coef -1.98717 0.0250226 -1.19297

SE Coef 1.67930 0.0122551 0.552980

Z -1.18 2.04 -2.16

P 0.237 0.041 0.031

Odds Ratio

95% CI Lower Upper

1.03 0.30

1.00 0.10

1.05 0.90

Log-Likelihood = -46.820 Test that all slopes are zero: G = 7.574, DF = 2, P-Value = 0.023

The log of the odds ratio or logit equation is . A) ln(S) = -1.19297 + 0.0250226 Weight - 1.98717 Smokes B) S = -1.98717 + 0.025226 Weight - 1.19297 Smokes C) Rating Pulse = -1.98717 + 0.025226 Weight - 1.19297 Smokes D) ln(S) = -1.98717 + 0.025226 Weight - 1.19297 Smokes E) ln(p) = -1.98717 + 0.025226 Weight - 1.19297 Smokes Answer: D Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

6) A research project was conducted to study the effect of smoking and weight upon resting pulse rate. The response variable is coded as 1 when the pulse rate is low and 0 when it high. Smoking is also coding as 1 when smoking and 0 when not smoking. Shown below is the Minitab output from a logistic regression. Response Information Variable Rating Pulse

Value 1 0 Total

Count 70 (Event) 22 92

Logistic Regression Table Predictor Constant Weight Smokes

Coef -1.98717 0.0250226 -1.19297

SE Coef 1.67930 0.0122551 0.552980

Z -1.18 2.04 -2.16

P 0.237 0.041 0.031

Odds Ratio

95% CI Lower Upper

1.03 0.30

1.00 0.10

1.05 0.90

Log-Likelihood = -46.820 Test that all slopes are zero: G = 7.574, DF = 2, P-Value = 0.023

The predicted probability that a 150 pounds person who smokes has a low pulse rate is closest to . A) 0.6395 B) 0.8540 C) 0.2145 D) 0.5 E) 0.9871 Answer: A Diff: 3 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

7) A research project was conducted to study the effect of smoking and weight upon resting pulse rate. The response variable is coded as 1 when the pulse rate is low and 0 when it high. Smoking is also coding as 1 when smoking and 0 when not smoking. Shown below is the Minitab output from a logistic regression. Response Information Variable Rating Pulse

Value 1 0 Total

Count 70 (Event) 22 92

Logistic Regression Table Predictor Constant Weight Smokes

Coef -1.98717 0.0250226 -1.19297

SE Coef 1.67930 0.0122551 0.552980

Z -1.18 2.04 -2.16

P 0.237 0.041 0.031

Odds Ratio

95% CI Lower Upper

1.03 0.30

1.00 0.10

1.05 0.90

Log-Likelihood = -46.820 Test that all slopes are zero: G = 7.574, DF = 2, P-Value = 0.023

The predicted probability that a 150 pounds person who does not smoke has a low pulse rate is closest to . A) 0.6395 B) 0.8540 C) 0.2145 D) 0.5 E) 0.9871 Answer: B Diff: 3 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

8) A research project was conducted to study the effect of smoking and weight upon resting pulse rate. The response variable is coded as 1 when the pulse rate is low and 0 when it high. Smoking is also coding as 1 when smoking and 0 when not smoking. Shown below is the Minitab output from a logistic regression. Response Information Variable Rating Pulse

Value 1 0 Total

Count 70 (Event) 22 92

Logistic Regression Table Predictor Constant Weight Smokes

Coef -1.98717 0.0250226 -1.19297

SE Coef 1.67930 0.0122551 0.552980

Z -1.18 2.04 -2.16

P 0.237 0.041 0.031

Odds Ratio

95% CI Lower Upper

1.03 0.30

1.00 0.10

1.05 0.90

Log-Likelihood = -46.820 Test that all slopes are zero: G = 7.574, DF = 2, P-Value = 0.023

The predicted probability that a 150 pounds person who does not smoke has a high pulse rate is closest to . A) 0.6395 B) 0.8540 C) 0.2145 D) 0.1460 E) 0.9871 Answer: D Diff: 3 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

9) A research project was conducted to study the effect of smoking and weight upon resting pulse rate. The response variable is coded as 1 when the pulse rate is low and 0 when it high. Smoking is also coding as 1 when smoking and 0 when not smoking. Shown below is the Minitab output from a logistic regression. Response Information Variable Rating Pulse

Value 1 0 Total

Count 70 (Event) 22 92

Logistic Regression Table Predictor Constant Weight Smokes

Coef -1.98717 0.0250226 -1.19297

SE Coef 1.67930 0.0122551 0.552980

Z -1.18 2.04 -2.16

P 0.237 0.041 0.031

Odds Ratio

95% CI Lower Upper

1.03 0.30

1.00 0.10

1.05 0.90

Log-Likelihood = -46.820 Test that all slopes are zero: G = 7.574, DF = 2, P-Value = 0.023

Based on the results, the null hypothesis that all of the predictor variables are insignificant with coefficients of 0, should . A) not be rejected as the p-value is 7.574 B) be rejected as the p-value is -46.82 C) be rejected as the p-value is 0.023 D) not be rejected as the p-value is 0.023 E) not be rejected as there is insufficient information Answer: C Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

10) A research project was conducted to study the effect of smoking and weight upon resting pulse rate. The response variable is coded as 1 when the pulse rate is low and 0 when it high. Smoking is also coding as 1 when smoking and 0 when not smoking. Shown below is the Minitab output from a logistic regression. Response Information Variable Rating Pulse

Value 1 0 Total

Count 70 (Event) 22 92

Logistic Regression Table Predictor Constant Weight Smokes

Coef -1.98717 0.0250226 -1.19297

SE Coef 1.67930 0.0122551 0.552980

Z -1.18 2.04 -2.16

P 0.237 0.041 0.031

Odds Ratio

95% CI Lower Upper

1.03 0.30

1.00 0.10

1.05 0.90

Log-Likelihood = -46.820 Test that all slopes are zero: G = 7.574, DF = 2, P-Value = 0.023

Which of the individual predictors are found to be significant predictors of resting pulse rates at an alpha of 0.05? A) weight B) constant and weight C) constant D) smokes and constant E) smokes and weight Answer: E Diff: 1 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

11) A multiple regression analysis produced the following tables. Coefficients Intercept 1411.876 x1 35.18215 2 x1 -7.721648

Regression Residual Total

df 2 25 27

Standard Error 7.621533 96.8433

t Statistic p-value

3.007943 SS 58567032 12765573 71332605

MS 29283516 510622.9

F 57.34861

The minimum value of the predicted value of the dependent variable is reached when x1 = . A) 2.15815 B) 3.18512 C) 3.37785 D) 3.40125 E) a value not listed here Answer: E Diff: 2 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. Bloom's level: Application

5 11

12) A multiple regression analysis produced the following tables. Coefficients Intercept 1411.876 x1 35.18215 2 x1 -7.721648

Regression Residual Total

df 2 25 27

Standard Error 7.621533 96.8433

t Statistic p-value

3.007943 SS 58567032 12765573 71332605

MS 29283516 510622.9

F 57.34861

If the predicted value of the dependent variable is 1000, then x1 = _. A) 4.50666 B) 8.158666 C) 9.928668 D) 10.15866 E) 11.928666 Answer: C Diff: 3 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. Bloom's level: Application

5 12

13) A multiple regression analysis produced the following tables. Coefficients Intercept 1411.876 x1 35.18215 2 x1 -7.721648

Regression Residual Total

df 2 25 27

Standard Error 7.621533 96.8433

t Statistic p-value

3.007943 SS 58567032 12765573 71332605

MS 29283516 510622.9

F 57.34861

Using α = 0.01 to test the null hypothesis H0: β1 = β2 = 0, the critical F value is . A) 8.09 B) 6.89 C) 6.09 D) 5.78 E) 5.09 Answer: D Diff: 1 Response: See section 14.1 Nonlinear Models: Mathematical Transformation Learning Objective: 14.1: Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables. Bloom's level: Application

5 13

14) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

For a rural household with annual income, Abby's model predicts weekly grocery expenditure of $235. A) $90,753.44 B) $92,257.51 C) $94,734.77 D) $95,773.44 E) $96,737.71 Answer: D Diff: 2 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. Bloom's level: Application

5 14

15) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

For a suburban household with annual income, Abby's model predicts weekly grocery expenditure of $235. A) $120,082.9 B) $122,082.9 C) $124,082.9 D) $126,082.9 E) $128,082.9 Answer: C Diff: 2 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. Bloom's level: Application

5 15

16) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

For what annual income will a suburban household and a rural household have the same predicted weekly grocery spending? A) $120,082.9 B) $122,082.9 C) $124,082.9 D) The predicted weekly grocery spending of a rural household will always be larger for a given annual income. E) The predicted weekly grocery spending of a suburban household will always be larger for a given annual income. Answer: D Diff: 2 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. Bloom's level: Application

5 16

17) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

However, Abby has reasons to believe that actually suburban households have a higher propensity to spend in groceries than rural households, as they tend to make more impulse purchasing decisions. In this new model, the actual income coefficient for the suburban families is 1.735272α, for some α > 1. If new data confirms that suburban houses with an annual income of $95,773.44 have the same weekly grocery spending as rural households, then α = . A) 1.055587 B) 1.165587 C) 1.245587 D) 1.275587 E) 1.295587 Answer: E Diff: 3 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. Bloom's level: Application

5 17

18) Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Intercept x1 (income) x2 (neighborhood)

Coefficients 19.68247 1.735272 49.12456

Standard Error 10.01176 0.174564 7.655776

t Statistic 1.965934 9.940612 6.416667

p-value 0.077667 1.68E-06 7.67E-05

The marginal propensity to consume (MPC) is defined in economics as the proportion of an additional dollar of income that a household (or individual) consumes. Assume that grocery spending is the main expenditure of households. Then according to the regression analysis above, the MPC . A) of rural households is equal to that of urban households, and this MPC decreases as households increase their annual income B) of rural households is larger than that of urban households, and both MPCs decrease as households increase their annual income C) of rural households is equal to that of urban households, and this MPC is constant as households increase their annual income D) of rural households is larger than that of urban households, and both MPCs are constant as households increase their annual income E) of rural households is larger than that of urban households, and both MPCs increase as households increase their annual income Answer: C Diff: 3 Response: See section 14.2 Indicator (Dummy) Variables Learning Objective: 14.2: Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis. Bloom's level: Application

5 18

97) If there are 6 independent variables, then there are possible regressions with 2 predictors and possible regressions with 3 predictors. A) 12; 18 B) 15; 18 C) 12; 20 D) 18; 20 E) 15; 20 Answer: E Diff: 2 Response: See section 14.3 Model-Building: Search Procedures Learning Objective: 14.3: Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious. Bloom's level: Application 98) A researcher wants to address multicollinearity using the guideline that a variance inflation factor (VIF) greater than 9 will indicate a severe multicollinearity problem. This means that the highest coefficient of determination allowed will be . A) 0.900 B) 0.912 C) 0.928 D) 0.889 E) 0.939 Answer: D Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. Bloom's level: Application 99) If a coefficient of determination for a given model is 0.85, then its variance inflation factor is . A) 8.5 B) 7.7 C) 6.2 D) 4.9 E) 6.7 Answer: E Diff: 1 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. Bloom's level: Application

5 19

100) A regression analysis conducted to predict an independent variable by other independent variables . A) is never done; predicted variables are dependent by definition B) done to control multicollinearity C) done to avoid multicollinearity D) done to detect multicollinearity E) done implicitly when the correlation matrix is computed Answer: D Diff: 2 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. Bloom's level: Application 101) Which of the following problems is not caused by multicollinearity? A) It is difficult or even impossible to interpret the estimates of the regression coefficients. B) Inordinately small t values for the regression coefficients may result. C) The standard deviations of the regression coefficients are underestimated. D) The algebraic sign of estimated regression coefficients may be the opposite of what would be expected for a predictor variable. E) The variance of a given regression coefficient is larger than it would otherwise have been if the variable had been completely uncorrelated with all the other variables in the model. Answer: C Diff: 2 Response: See section 14.4 Multicollinearity Learning Objective: 14.4: Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it. Bloom's level: Application

1) A research project was conducted to study the effect of a chemical on undesired insects. The researcher uses 6 dose levels, and at each level exposes 250 insects to the chemical and proceeds to count the number of insects that die. The researcher uses a binary logistic regression model to estimate the probability of death as a function of dose. Shown below is Minitab output from a logistic regression. Coefficients Term Constant Dose

Coef -2.644 0.6740

SE Coef 95% CI 0.156 (-2.950, -2.338) 0.0391 (0.5973, 0.7506)

Z-Value -16.94 17.23

P-Value 0.000 0.000

VIF 1.00

Odd Ratios for Continuous Predictors Odds Ratio 95% CI Dose 1.9621 (1.8173, 2.1184)

The predicted probability that an insect will die when exposed to the second dose is . A) 0.1246 B) 0.1446 C) 0.1857 D) 0.2148 E) 0.2945 Answer: D Diff: 3 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

2) A research project was conducted to study the effect of a chemical on undesired insects. The researcher uses 6 dose levels, and at each level exposes 250 insects to the chemical and proceeds to count the number of insects that die. The researcher uses a binary logistic regression model to estimate the probability of death as a function of dose. Shown below is Minitab output from a logistic regression. Coefficients Term Constant Dose

Coef -2.644 0.6740

SE Coef 95% CI 0.156 (-2.950, -2.338) 0.0391 (0.5973, 0.7506)

Z-Value -16.94 17.23

P-Value 0.000 0.000

VIF 1.00

Odd Ratios for Continuous Predictors Odds Ratio 95% CI Dose 1.9621 (1.8173, 2.1184)

The predicted probability that an insect will die when exposed to the first dose is . A) 0.0814 B) 0.1046 C) 0.1153 D) 0.1224 E) 0.1395 Answer: D Diff: 3 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

3) A research project was conducted to study the effect of a chemical on undesired insects. The researcher uses 6 dose levels, and at each level exposes 250 insects to the chemical and proceeds to count the number of insects that die. The researcher uses a binary logistic regression model to estimate the probability of death as a function of dose. Shown below is Minitab output from a logistic regression. Coefficients Term Constant Dose

Coef -2.644 0.6740

SE Coef 95% CI 0.156 (-2.950, -2.338) 0.0391 (0.5973, 0.7506)

Z-Value -16.94 17.23

P-Value 0.000 0.000

VIF 1.00

Odd Ratios for Continuous Predictors Odds Ratio 95% CI Dose 1.9621 (1.8173, 2.1184)

The estimated odds of death at a given level divided by the estimated odds of death at the following level equals . A) 1.9621 B) 0.6470 C) 0.5097 D) 0.3271 E) 0.3008 Answer: C Diff: 2 Response: See section 14.5 The Logistic Regression Model. Learning Objective: 14.5: Understand when to use logistic regression and be able to interpret its results.

Business Statistics, 11e (Black) Chapter 15 Time-Series Forecasting and Index Numbers 1) Time-series data are data gathered on a desired characteristic at a particular point in time. Answer: FALSE Diff: 1 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. 2) The long-term general direction of data is referred to as series. Answer: FALSE Diff: 1 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. 3) A stationary time-series data has only trend, but no cyclical or seasonal effects. Answer: FALSE Diff: 1 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. 4) Forecast error is the difference between the value of the response variable and those of the explanatory variables. Answer: FALSE Diff: 1 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. 5) For large datasets, the mean error (ME) and mean absolute deviation (MAD) always have the same numerical value. Answer: FALSE Diff: 1 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use.

6) Naïve forecasting models have no useful applications because they do not take into account data trend, cyclical effects or seasonality. Answer: FALSE Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 7) When a trucking firm uses the number of shipments for January of the previous year as the forecast for January next year, it is using a naïve forecasting model. Answer: TRUE Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 8) Two popular general categories of smoothing techniques are averaging models and exponential models. Answer: TRUE Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 9) Two popular general categories of smoothing techniques are exponential models and logarithmic models. Answer: FALSE Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 10) An exponential smoothing technique in which the smoothing constant alpha is equal to one is equivalent to a regression forecasting model. Answer: FALSE Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 11) Linear regression models cannot be used to analyze quadratic trends in time-series data. Answer: FALSE Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method. 2

12) Although seasonal effects can confound a trend analysis, a regression model is robust to these effects and the researcher does not need to adjust for seasonality prior to using a regression model to analyze trends. Answer: FALSE Diff: 2 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method. 13) If the trend equation is quadratic in time t = 1….T, the forecast value for the next time period, T+1, depends on time T. Answer: TRUE Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method. 14) If the trend equation is linear in time, the slope indicates the increase, or decrease when negative, in the forecasted value of the response value Y for the next time period. Answer: TRUE Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method. 15) One of the main techniques for isolating the effects of seasonality is reconstitution. Answer: FALSE Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. 16) One of the main techniques for isolating the effects of seasonality is decomposition. Answer: TRUE Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method.

17) The first step of isolating seasonal effects is to remove the trend and cycles effects. Answer: TRUE Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. 18) Once the seasonal effects have been isolated, these effects can be removed from the original data through desensitizing. Answer: FALSE Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. 19) When the error terms of a regression forecasting model are correlated the problem of autocorrelation occurs. Answer: TRUE Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. 20) If autocorrelation occurs in regression analysis, then the confidence intervals and tests using the t and F distributions are no longer strictly applicable. Answer: TRUE Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. 21) One of the ways to overcome the autocorrelation problem in a regression forecasting model is to increase the level of significance for the F test. Answer: FALSE Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

22) One of the ways to overcome the autocorrelation problem in a regression forecasting model is to transform the variables by taking the first differences. Answer: TRUE Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. 23) Autoregression is a multiple regression technique in which the independent variables are time-lagged versions of the dependent variable. Answer: TRUE Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. 24) Autocorrelation in a regression forecasting model can be detected by the F test. Answer: FALSE Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. 25) In statistics, the Winters' Three Parameter statistic is a test statistic used to detect the presence of autocorrelation in the residuals from a regression analysis. Answer: FALSE Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. 26) A small value of the Durbin-Watson statistic indicates that successive error terms are positively correlated. Answer: TRUE Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

27) Unweighted price indexes can only compare across the entire successive time period for which there is data. Answer: FALSE Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 28) Index numbers are used to compare various time frame measures to a base time period measure. Answer: TRUE Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 29) A simple index number is the ratio of the base period divided by the period of interest, multiplied by 100. Answer: FALSE Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each.

30) A time series with forecast values and error terms is presented in the following table. The mean error (ME) for this forecast is . Month July Aug Sept Oct Nov

Actual 5 11 13 6 5

Forecast

Error

5 6.8 8.66 7.862

6.0 6.20 -2.66 -2.86

A) 1.67 B) 1.34 C) 6.68 D) 3.67 E) 2.87 Answer: A Diff: 1 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. 31) A time series with forecast values and error terms is presented in the following table. The mean absolute deviation (MAD) for this forecast is . Month July Aug Sept Oct Nov

Actual 5 11 13 6 5

Forecast

Error

5 6.8 8.66 7.862

6.0 6.20 -2.66 -2.86

A) 3.54 B) 7.41 C) 4.43 D) 17.72 E) 4.34 Answer: C Diff: 1 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use.

32) A time series with forecast values and error terms is presented in the following table. The mean squared error (MSE) for this forecast is . Month July Aug Sept Oct Nov

Actual 5 11 13 6 5

Forecast

Error

5 6.8 8.66 7.862

6.0 6.20 -2.66 -2.86

A) 13.33 B) 17.94 C) 89.71 D) 22.43 E) 32.34 Answer: D Diff: 2 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. 33) A time series analysis was performed to determine the number of new online customers that joined the "Jelly of the Month Club". The actual number of new customers, the forecast values and the error terms are presented in the following table. The mean error (ME) for this forecast is . Month July Aug Sept Oct Nov

Actual 4 6 3 9 8

Forecast

Error

5 6 8 9

-1 3 -1 1

A) -0.50 B) 0.50 C) 1.50 D) 7.00 E) 3.00 Answer: B Diff: 1 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use.

34) A time series analysis was performed to determine the number of new online customers that joined the "Jelly of the Month Club". The actual number of new customers, the forecast values and the error terms are presented in the following table. The mean absolute deviation (MAD) for this forecast is . Month July Aug Sept Oct Nov

Actual 4 6 3 9 8

Forecast

Error

5 6 8 9

-1 3 -1 1

A) -0.50 B) 0.50 C) 1.50 D) 7.00 E) 3.00 Answer: C Diff: 2 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. 35) A time series analysis was performed to determine the number of new online customers that joined the "Jelly of the Month Club". The actual number of new customers, the forecast values and the error terms are presented in the following table. The mean squared error (MSE) for this forecast is . Month July Aug Sept Oct Nov

Actual 4 6 3 9 8

Forecast

Error

5 6 8 9

-1 3 -1 1

A) -0.50 B) 0.50 C) 1.50 D) 7.00 E) 3.00 Answer: E Diff: 2 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. 9

36) In exponential smoothing models, the value of the smoothing constant may be any number between . A) -1 and 1 B) -5 and 5 C) 0 and 1 D) 0 and 10 E) 0 and 100 Answer: C Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 37) Use of a smoothing constant value greater than 0.5 in an exponential smoothing model gives more weight to . A) the actual value for the current period B) the actual value for the previous period C) the forecast for the current period D) the forecast for the previous period E) the forecast for the next period Answer: A Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 38) Use of a smoothing constant value less than 0.5 in an exponential smoothing model gives more weight to . A) the actual value for the current period B) the actual value for the previous period C) the forecast for the current period D) the forecast for the previous period E) the forecast for the next period Answer: C Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing.

39) When forecasting with exponential smoothing, data from previous periods is . A) given equal importance B) given exponentially increasing importance C) ignored D) given exponentially decreasing importance E) linearly decreasing importance Answer: D Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 40) Using a three-month moving average, the forecast value for October made at the end of September in the following time series would be . July Aug Sept Oct

5 11 13 6

A) 11.60 B) 10.00 C) 9.07 D) 8.06 E) 9.67 Answer: E Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing.

41) Using a three-month moving average, the forecast value for November in the following time series is . July Aug Sept Oct

5 11 13 6

A) 11.60 B) 10.00 C) 9.67 D) 8.60 E) 6.00 Answer: B Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 42) Using a three-month moving average (with weights of 6, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for October made at the end of September in the following time series would be . July Aug Sept Oct

5 11 13 6

A) 11.60 B) 10.00 C) 9.67 D) 8.60 E) 6.11 Answer: A Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing.

43) Using a three-month moving average (with weights of 6, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for November in the following time series is . July Aug Sept Oct

5 11 13 6

A) 11.60 B) 10.00 C) 9.67 D) 8.06 E) 8.60 Answer: E Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 44) The city golf course is interested in starting a junior golf program. The golf pro has collected data on the number of youths under 13 that have played golf during the last 4 months. Using a three-month moving average, the forecast value for October made at the end of September in the following time series would be . July Aug Sept Oct

28 27 17 19

A) 24 B) 21 C) 21.56 D) 19.22 E) 22 Answer: A Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing.

45) The city golf course is interested in starting a junior golf program. The golf pro has collected data on the number of youths under 13 that have played golf during the last 4 months. Using a three-month moving average, the forecast value for November in the following time series would be . July Aug Sept Oct

28 27 17 19

A) 24 B) 21 C) 21.56 D) 19.22 E) 22 Answer: B Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 46) The city golf course is interested in starting a junior golf program. The golf pro has collected data on the number of youths under 13 that have played golf during the last 4 months. Using a three-month moving average (with weights of 5, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for October made at the end of September in the following time series would be . July Aug Sept Oct

28 27 17 19

A) 24 B) 21 C) 21.56 D) 19.22 E) 22 Answer: C Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing.

47) The city golf course is interested in starting a junior golf program. The golf pro has collected data on the number of youths under 13 that have played golf during the last 4 months. Using a three-month moving average (with weights of 5, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for November in the following time series would be . July Aug Sept Oct

28 27 17 19

A) 24 B) 21 C) 21.56 D) 19.22 E) 22 Answer: D Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 48) What is the forecast for the Period 7 using a 3-period moving average technique, given the following time-series data for six past periods? Period Value

1 136

2 126

3 146

4 148

5 156

6 164

A) 164.67 B) 156.00 C) 148.00 D) 126.57 E) 158.67 Answer: B Diff: 1 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing.

49) The forecast value for August was 22 and the actual value turned out to be 19. Using exponential smoothing with α = 0.30, the forecast value for September would be . A) 21.1 B) 19.9 C) 18.1 D) 22.9 E) 21.0 Answer: A Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. 50) The forecast value for September was 21.1 and the actual value turned out to be 18. Using exponential smoothing with α = 0.30, the forecast value for October would be . A) 18.09 B) 18.93 C) 20.17 D) 21.00 E) 17.07 Answer: C Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing.

51) The following graph of time-series data suggests a

trend.

A) linear B) quadratic C) cosine D) tangential E) flat Answer: B Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method.

8) The following graph of a time-series data suggests a

trend.

A) linear B) tangential C) cosine D) quadratic E) flat Answer: D Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method.

9) The following graph of a time-series data suggests a

trend.

A) linear B) quadratic C) cosine D) tangential E) flat Answer: A Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method.

54) The following graph of time-series data suggests a

trend.

A) quadratic B) cosine C) linear D) tangential E) flat Answer: C Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method.

1) Fitting a linear trend to 36 monthly data points (January 2011 = 1, February 2011 = 2, March 2011 = 3, etc.) produced the following tables.

Intercept x

Regression Residual Total

Coefficients 222.379 9.009066

Standard Error t Statistic p-value 67.35824 3.301438 0.002221 3.17471 2.83776 0.00751

df SS MS F p-value 1 315319.3 315319.3 8.052885 0.007607 34 1331306 39156.07 35 1646626

The projected trend value for January 2014 is . A) 231.39 B) 555.71 C) 339.50 D) 447.76 E) 355.71 Answer: B Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method.

2) Fitting a linear trend to 36 monthly data points (January 2011 = 1, February 2011 = 2, March 2011 = 3, etc.) produced the following tables.

Intercept x

Regression Residual Total

Coefficients 222.379 9.009066

Standard Error t Statistic p-value 67.35824 3.301438 0.002221 3.17471 2.83776 0.00751

df SS MS F p-value 1 315319.3 315319.3 8.052885 0.007607 34 1331306 39156.07 35 1646626

The projected trend value for January 2014 is . A) 544.29 B) 868.61 C) 652.39 D) 760.50 E) 876.90 Answer: A Diff: 1 Response: See section 15.3 Trend Analysis Learning Objective: 15.3: Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method. 57) Which of the following is not a component of time series data? A) Trend B) Seasonal fluctuations C) Cyclical fluctuations D) Normal fluctuations E) Irregular fluctuations Answer: D Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method.

58) Calculating the "ratios of actuals to moving average" is a common step in time series decomposition. The results (the quotients) of this step estimate the . A) trend and cyclical components B) seasonal and irregular components C) cyclical and irregular components D) trend and seasonal components E) irregular components Answer: B Diff: 2 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. 59) The high and low values of the "ratios of actuals to moving average" are ignored when finalizing the seasonal index for a period (month or quarter) in time series decomposition. The rationale for this is to . A) reduce the sample size B) eliminate autocorrelation C) minimize serial correlation D) eliminate the irregular component E) eliminate the trend Answer: D Diff: 2 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method.

60) The ratios of "actuals to moving averages" (seasonal indexes) for a time series are presented in the following table as percentages. 2008 Q1 Q2 Q3 Q4

97.76 86.61

2009 112.22 100.65 99.08 95.00

2010 110.78 108.68 97.68 94.64

2011 111.22 103.78 97.61 92.92

2012 111.87 101.95

The final (completely adjusted) estimate of the seasonal index for Q1 is . A) 109.733 B) 109.921 C) 113.853 D) 113.492 E) 111.545 Answer: E Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. 61) The ratios of "actuals to moving averages" (seasonal indexes) for a time series are presented in the following table as percentages. 2008 Q1 Q2 Q3 97.76 Q4 86.61

2009 112.22 100.65 99.08 95.00

2010 110.78 108.68 97.68 94.64

2011 111.22 103.78 97.61 92.92

2012 111.87 101.95

The final (completely adjusted) estimate of the seasonal index for Q4 is . A) 86.61 B) 90.90 C) 93.78 D) 92.29 E) 93.00 Answer: C Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method.

62) Given several years of quarterly data and finding the four quarter moving average from Q3 of the second year through Q2 of the third year would be placed on the decomposition table between which two quarters? A) Second year Q3 and Q4 B) Second year Q4 and third year Q2 C) Third year Q1 and Q2 D) Third year Q2 and Q3 E) Second year Q4 and third year Q1 Answer: E Diff: 2 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. 63) The effect of a four-quarter moving average can be described as the seasonal effects of the data. A) emphasizing B) dampening C) removing D) incorporating E) normalizing Answer: B Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. 64) A seasonal index for quarterly data is found as the ratio of to multiplied by 100. A) actuals; medians B) moving average; 8 C) actuals; moving averages D) actuals; 4 E) 100; actuals Answer: C Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method.

and is then

65) If the seasonal index values for four consecutive quarters are 86.3, 105.6, 99.2, and 100, respectively, then which quarter has the most activity compared with the base quarter? A) Q1 B) Q2 C) cannot be determined D) Q3 E) Q4 Answer: B Diff: 1 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. 66) In an autoregressive forecasting model, the independent variable(s) is (are) . A) time-lagged values of the dependent variable B) first-order differences of the dependent variable C) second-order, or higher, differences of the dependent variable D) first-order quotients of the dependent variable E) time-lagged values of the independent variable Answer: A Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

7) Analysis of data for an autoregressive forecasting model produced the following tables.

Intercept yt-1 yt-2

Regression Residual Total

Coefficients 3.85094 0.70434 -0.62669 df 2 43 45

Standard Error 3.745787 0.082849 0.035709

SS 135753.5 27192.79 162946.3

MS 67876.76 632.3904

t Statistic 0.84426 -1.66023 14.65044

p-value 0.34299 0.13822 6.69E-19

F 107.3336

p-value 1.91E-17

The forecasting model is . A) ŷt = 3.745787 + 0.082849yt-1 + 0.035709yt-2 B) ŷt = 3.85094 + 0.70434yt-1 - 0.62669yt-2 C) ŷt = 0.84426 - 1.66023yt-1 + 14.65023yt-2 D) ŷt = 0.34299 + 0.13822yt-1 + 9.69yt-2 E) ŷt = 0.34299 + 0.13822yt-1 - 6.69yt-2 Answer: B Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

8) Analysis of data for an autoregressive forecasting model produced the following tables.

Intercept yt-1 yt-2

Regression Residual Total

Coefficients 3.85094 0.70434 -0.62669 df 2 43 45

Standard Error 3.745787 0.082849 0.035709

SS 135753.5 27192.79 162946.3

MS 67876.76 632.3904

t Statistic 0.84426 -1.66023 14.65044

p-value 0.34299 0.103822 6.69E-19

F 107.3336

p-value 1.91E-17

The results indicate that . A) the first predictor, yt-1, is significant at the 10% level B) the second predictor, yt-2, is significant at the 1% level C) all predictor variables are significant at the 5% level D) none of the predictor variables are significant at the 5% level E) the overall regression model is not significant at 5% level Answer: B Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

9) Analysis of data for an autoregressive forecasting model produced the following tables.

Intercept yt-1 yt-2

Regression Residual Total

Coefficients 3.85094 0.70434 -0.62669 df 2 43 45

Standard Error 3.745787 0.082849 0.035709

SS 135753.5 27192.79 162946.3

MS 67876.76 632.3904

t Statistic 0.84426 -1.66023 14.65044

p-value 0.34299 0.103822 6.69E-19

F 107.3336

p-value 1.91E-17

The actual values of this time series, y, were 228, 54, and 191 for May, June, and July, respectively. The forecast value predicted by the model for July is . A) -101.00 B) 104.54 C) 218.71 D) 21.56 E) -77.81 Answer: A Diff: 2 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

10) Analysis of data for an autoregressive forecasting model produced the following tables.

Intercept yt-1 yt-2

Regression Residual Total

Coefficients 3.85094 0.70434 -0.62669 df 2 43 45

Standard Error 3.745787 0.082849 0.035709

SS 135753.5 27192.79 162946.3

MS 67876.76 632.3904

t Statistic 0.84426 -1.66023 14.65044

p-value 0.34299 0.103822 6.69E-19

F 107.3336

p-value 1.91E-17

The actual values of this time series, y, were 228, 54, and 191 for May, June, and July, respectively. The predicted (forecast) value for August is . A) -101.00 B) 104.54 C) 218.71 D) 21.56 E) -77.81 Answer: B Diff: 2 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

11) Jim Royo, Manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's). He selects the dollar value of residential building permits (in $10,000) as the predictor variable. An analysis of the data yielded the following tables.

Intercept x

Regression Residual Total

Coefficients 222.1456 6.152885 df 1 20 21

Standard Error 74.765 1.895423

SS 259643.9 492791.3 752435.2

MS 259643.9 24639.56

t Statistic 2.971252 3.24618

p-value 0.007284 0.003866

F 10.53768

p-value 0.004046

Using α = 0.05 the critical value of the Durbin-Watson statistic, dL, is . A) 1.24 B) 1.22 C) 1.13 D) 1.15 E) 1.85 Answer: A Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

3 11

12) Jim Royo, Manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's). He selects the dollar value of residential building permits (in $10,000) as the predictor variable. An analysis of the data yielded the following tables.

Intercept x

Coefficients 222.1456 6.152885

Regression Residual Total

df 1 20 21

Standard Error 74.765 1.895423

SS 259643.9 492791.3 752435.2

MS 259643.9 24639.56

t Statistic 2.971252 3.24618

p-value 0.007284 0.003866

F 10.53768

p-value 0.004046

Using α = 0.05 the critical value of the Durbin-Watson statistic, dU, is . A) 1.54 B) 1.42 C) 1.43 D) 1.44 E) 1.85 Answer: C Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

3 12

13) Jim Royo, Manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's). He selects the dollar value of residential building permits (in $10,000) as the predictor variable. An analysis of the data yielded the following tables.

Intercept x

Regression Residual Total

Coefficients 222.1456 6.152885 df 1 20 21

Standard Error 74.765 1.895423

SS 259643.9 492791.3 752435.2

MS 259643.9 24639.56

t Statistic 2.971252 3.24618

p-value 0.007284 0.003866

F 10.53768

p-value 0.004046

Jim's calculated value for the Durbin-Watson statistic is 1.93. Using α = 0.05, the appropriate decision is . A) do not reject H0: ρ = 0 B) reject H0: ρ ≠ 0 C) do not reject: ρ ≠ 0 D) the test is inconclusive E) reject H0: ρ = 0 Answer: A Diff: 2 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression.

3 13

14) Jim Royo, Manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's). He selects the dollar value of residential building permits (in $10,000) as the predictor variable. An analysis of the data yielded the following tables.

Intercept x

Regression Residual Total

Coefficients 222.1456 6.152885 df 1 20 21

Standard Error 74.765 1.895423

SS 259643.9 492791.3 752435.2

MS 259643.9 24639.56

t Statistic 2.971252 3.24618

p-value 0.007284 0.003866

F 10.53768

p-value 0.004046

Jim's calculated value for the Durbin-Watson statistic is 1.14. Using α = 0.05, the appropriate decision is . A) do not reject H0: ρ = 0 B) reject H0: ρ = 0 C) do not reject H0: ρ ≠ 0 D) the test is inconclusive E) reject H0: ρ ≠ 0 Answer: B Diff: 2 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. 75) The motivation for using an index number is to . A) transform the data to a standard normal distribution B) transform the data for a linear model C) eliminate bias from the sample D) reduce data to an easier-to-use, more convenient form E) reduce the variance in the data Answer: D Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each.

3 14

76) Often, index numbers are expressed as . A) percentages B) frequencies C) cycles D) regression coefficients E) correlation coefficients Answer: A Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 77) Index numbers facilitate comparison of . A) means B) data over time C) variances D) samples E) deviations Answer: B Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 78) Typically, the denominator used to calculate an index number is a measurement for the period. A) base B) current C) spanning D) intermediate E) peak Answer: A Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each.

3 15

79) Weighted aggregate price indexes are also known as . A) unbalanced indexes B) balanced indexes C) value indexes D) multiplicative indexes E) overall indexes Answer: C Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 80) When constructing a weighted aggregate price index, the weights usually are . A) prices of substitute items B) prices of complementary items C) quantities of the respective items D) squared quantities of the respective items E) quality of individual items Answer: C Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 81) Using 2010 as the base year, the 2012 value of a simple price index for the following price data is . Year Price

2008 29.88

2009 32.69

2010 42.04

2011 46.18

2012 47.98

2013 48.32

A) 77.60 B) 114.13 C) 160.58 D) 99.30 E) 100.00 Answer: B Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each.

3 16

82) Using 2000 as the base year, the 1990 value of the Paasche Price Index is (Quantities are averages for the student body.) Year Tuition ($/3 hrs) Books ($ each) Calculator ($ each)

1900 2000 2010 Price Quantity Price Quantity Price Quantity 81 15.00 164 13.00 200 12.00 32 1.00 43 1.50 85 2.00 45 0.50 27 0.75 25 1.00

A) 80.72 B) 162.28 C) 240.06 D) 50.45 E) 30.35 Answer: D Diff: 2 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 83) Using 2011 as the base year, the 2010 value of the Laspeyres Price Index is Year Wheat (¢'s/bushel) Sugar (¢'s/pound) Lard (¢'s/pound)

2010 2011 2012 Price Quantity Price Quantity Price Quantity 370 100 372 110 255 120 13 50 12 40 12 30 14 70 13 60 13 40

A) 69.92 B) 144.06 C) 100.21 D) 79.72 E) 99.72 Answer: E Diff: 2 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each.

3 17

84) Using 2011 as the base year, the 2010 value of the Paasche' Price Index is Year Wheat (¢'s/bushel) Sugar (¢'s/pound) Lard (¢'s/pound)

2010 2011 2012 Price Quantity Price Quantity Price Quantity 370 100 372 110 255 120 13 50 12 40 12 30 14 70 13 60 13 40

A) 99.79 B) 192.51 C) 100.29 D) 59.19 E) 39.99 Answer: A Diff: 2 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 85) A weighted aggregate price index where the weight for each item is computed by using the quantities of the base period is known as the . A) Paasche Index B) Simple Index C) Laspeyres Index D) Consumer Price index E) Producer Price index Answer: C Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 86) A weighted aggregate price index where the weight for each item is computed by using the quantities of the year of interest is known as the . A) Paasche Index B) Simple Index C) Laspeyres Index D) Consumer Price index E) Producer Price index Answer: A Diff: 1 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. 3 18

87) A time series with forecast values is presented in the following table: Month Jan Mar May Jul Sep

Actual a 1.2a 1.15a 1.25 a 1.3a

Forecast 1.1a 1.2 a 1.21a 1.25a

On this table, a is some nondisclosed value. The mean square error (MSE) is % of a. A) 4.15 B) 0.415 C) 4.15a D) 0.415a E) 0.00332 Answer: B Diff: 2 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. Bloom's level: Application 88) A time series with forecast values is presented in the following table: Month Jan Mar May Jul Sep

Actual a 1.2a 1.15a 1.25 a 1.3a

Forecast 1.1a 1.2 a 1.21a 1.25a

On this table, a is some nondisclosed value. The mean absolute deviation (MAD) is % of a. A) 6a B) 0.06a C) 6 D) 0.06 E) 4.8 Answer: C Diff: 2 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. Bloom's level: Application 3 19

89) A time series with forecast values is presented in the following table: Month Jan Mar May Jul Sep

Actual a 1.2a 1.15a 1.25 a 1.3a

Forecast 1.1a 1.2 a 1.21a 1.25a

If the mean absolute deviation (MAD) is 257, then a = . A) 4283.33 B) 428.33 C) 15.42 D) 42.833 E) 1.542 Answer: A Diff: 2 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. Bloom's level: Application 90) A time series with forecast values is presented in the following table: Month Jan Mar May Jul Sep Nov

Actual 1 1.2 1.15 1.25 1.3 1.275

Forecast * * * * x

If the mean absolute deviation (MAD) until September is 0.06, and the overall MAD is 0.05, then x = . A) 1.270 B) 1.270 or 1.280 C) 1.265 or 1.285 D) 1.260 or 1.290 E) 1.285 Answer: C Diff: 2 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. Bloom's level: Application 40

91) A time series with forecast values is presented in the following table: Month Jan Mar May Jul Sep Nov

Actual 1 1.2 1.15 1.25 1.3 1.275

Forecast * * * * x

If the mean square error (MSE) until September is 0.01125, and the overall MSE is 0.010125, then x = . A) 1.15 B) 1.25 C) 1.3 D) 1.25 or 1.3 E) 1.2 or 1.35 Answer: E Diff: 2 Response: See section 15.1 Introduction to Forecasting Learning Objective: 15.1: Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use. Bloom's level: Application 92) The forecast value for July was 210 and the actual value turned out to be 195. The researcher is using exponential smoothing and determines that the forecast value for August is 206.25. Then he is using α = . A) 0.35 B) 0.32 C) 0.30 D) 0.25 E) 0.22 Answer: D Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. Bloom's level: Application

93) The actual value of a variable for July was 195. The researcher is using exponential smoothing with α = 0.30 and determines that the forecast value for August is 205.5. Then the forecast value for July was . A) 200 B) 202 C) 205 D) 207 E) 210 Answer: E Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. Bloom's level: Application 94) If a researcher is using exponential smoothing and determines that the forecast for the next period (Ft + 1) is the average of the actual value for the previous period (Xt ) and the forecast value for the previous period (Ft ), then α = . A) 0.35 B) 0.40 C) 0.50 D) 0.55 E) there is not enough information to determine the value of α Answer: C Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. Bloom's level: Application 95) If a researcher is using exponential smoothing and determines that the forecast for the next period (Ft + 1) is the weighted average of the actual value for the previous period (Xt) and the forecast value for the previous period (Ft), with weights of 1 and 3 respectively, then α = . A) 0.25 B) 0.33 C) 0.67 D) 0.75 E) 0.90 Answer: A Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. Bloom's level: Application

96) If a researcher is using exponential smoothing and determines that the forecast for the next period (Ft + 1) is the weighted average of the actual value for the previous period (Xt) and the forecast value for the previous period (Ft), with weights of p and q respectively, then α = . A) p/q B) q/p C) 1 - q/(p + q) D) 1 - p/(p + q) E) 1/(p + q) Answer: C Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. Bloom's level: Application 97) If a researcher is using exponential smoothing and determines that the forecast for the next period (Ft + 1) coincides with the weighted average of the actual value for the previous period (Xt ) and the forecast value for the previous period (Ft ), with weights of p and q respectively. If p = 2, then q = . A) 2/α + 2 B) 2/(α + 2) C) 2/(α - 2) D) 1/(α - 2) E) 2/α - 2 Answer: E Diff: 2 Response: See section 15.2 Smoothing Techniques Learning Objective: 15.2: Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing. Bloom's level: Application 98) If the Yeart Quarterq actual value is 9,885 and the corresponding Yeart Quarterq seasonal index is 97.75, then the Yeart Quarterq deseasonalized value is . A) 222.41 B) 9,662.59 C) 9,775.00 D) 10,083.18 E) 10,112.53 Answer: E Diff: 2 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. Bloom's level: Application 43

99) If the Yeart Quarterq actual value is 9,885 and the Yeart Quarterq deseasonalized value is 10,112.53, then the Yeart Quarterq seasonal index is . A) 0.022 B) 0.9775 C) 2.22 D) 97.75 E) 102.30 Answer: D Diff: 2 Response: See section 15.4 Seasonal Effects Learning Objective: 15.4: Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method. Bloom's level: Application 100) Suppose that for a time-series model with one predictor, you compute a Durbin-Watson statistic of 0.625. Assume that n = 30 and α = 0.05. Then dL = . A) 1.32 B) 1.35 C) 1.38 D) 1.41 E) 1.43 Answer: B Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. Bloom's level: Application 101) Suppose that for a time-series model with one predictor, you compute a Durbin-Watson statistic D = 0.625. Assume that n = 30 and α = 0.05. Then your decision is . A) fail to reject the null hypothesis D = 0 B) reject the null hypothesis D = 0 C) fail to reject the null hypothesis ρ = 0 D) reject the null hypothesis ρ = 0 E) fail to reject the null hypothesis ρ > 0 Answer: D Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. Bloom's level: Application

102) Suppose that for a time-series model with one predictor, you compute a Durbin-Watson statistic D = 1.409. Assume that n = 30 and α = 0.01. Then your decision is to . A) fail to reject the null hypothesis D = 0 B) reject the null hypothesis D = 0 C) fail to reject the null hypothesis ρ = 0 D) reject the null hypothesis ρ = 0 E) fail to reject the null hypothesis ρ > 0 Answer: C Diff: 1 Response: See section 15.5 Autocorrelation and Autoregression Learning Objective: 15.5: Test for autocorrelation using the Durbin-Watson test, overcoming autocorrelation by adding independent variables and transforming variables, and taking advantage of autocorrelation with autoregression. Bloom's level: Application 103) The table below shows the prices in $ and quantities (thousands) for five specialized electronic components for 2000 and 2016.

Ring A Ring B Capacitor Sigma unit CPU

P_2000 Q_2000 P_2016 Q_2016 1.58 35 2.16 37 2.25 48 3.25 46 0.36 52 0.81 50 1.27 48 1.59 52 4.15 28 5.5 28

The Paasche price index for 2016 using 2000 as base year is . A) 139.87 B) 137.25 C) 140.33 D) 133.25 E) 131.87 Answer: A Diff: 2 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. Bloom's level: Application

6) The table below shows the prices in $ and quantities (thousands) for five specialized electronic components for 2000 and 2016.

Ring A Ring B Capacitor Sigma unit CPU

P_2000 Q_2000 P_2016 Q_2016 1.58 35 2.16 37 2.25 48 3.25 46 0.36 52 0.81 50 1.27 48 1.59 52 4.15 28 5.5 28

The Laspeyres price index for 2016 using 2000 as base year is . A) 136.25 B) 137.33 C) 138.75 D) 139.87 E) 140.33 Answer: E Diff: 2 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. Bloom's level: Application

7) The table below shows the prices in $ and quantities (thousands) for five specialized electronic components for 2000 and 2016.

Ring A Ring B Capacitor Sigma unit CPU

P_2000 Q_2000 P_2016 Q_2016 1.58 35 2.16 37 2.25 48 3.25 46 0.36 52 0.81 50 1.27 48 1.59 52 4.15 28 P 28

If the Paasche price index for 2016 using 2000 as base year is 137.75, then P = . A) 4.75 B) 4.88 C) 5.23 D) 5.67 E) 5.72 Answer: C Diff: 3 Response: See section 15.6 Index Numbers Learning Objective: 15.6: Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 16 Analysis of Categorical Data 1) In a chi-square goodness-of-fit test, theoretical frequencies are also called expected frequencies. Answer: TRUE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 2) In a chi-square goodness-of-fit test, actual frequencies are also called calculated frequencies. Answer: FALSE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 3) The number of degrees of freedom in a chi-square goodness-of-fit test is the number of categories minus the number of parameters estimated. Answer: FALSE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 4) The number of degrees of freedom in a chi-square goodness-of-fit test is the number of categories minus the number of parameters estimated minus one. Answer: TRUE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 5) A chi-square goodness-of-fit test is being used to test the goodness-of-fit of a uniform distribution for a dataset with "k" categories. This test has (k-3) degrees of freedom. Answer: FALSE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

6) The null hypothesis in a chi-square goodness-of-fit test is that the observed distribution is the same as the expected distribution. Answer: TRUE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 7) The decision rule in a chi-square goodness-of-fit test is to reject the null hypothesis if the computed chi-square value is greater than the table chi-square value. Answer: TRUE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 8) When using the chi-square goodness-of-fit test, the type of distribution (uniform, Poisson, normal) being used does not influence the hypothesis test. Answer: FALSE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 9) A chi-square goodness-of-fit test to determine if the observed frequencies in seven categories are uniformly distributed has six degrees of freedom. Answer: TRUE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 10) A chi-square goodness-of-fit test to determine if the observed frequencies in ten categories are Poisson distributed has nine degrees of freedom. Answer: FALSE Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 11) A two-way table used for a test of independence is sometimes called a contingency table. Answer: TRUE Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 2

12) A researcher is interested in using a chi-square test of independence to determine if age is independent of minutes spent reading. Age is divided into four categories while minutes spent reading is classified as high, medium, low. The number of degrees of freedom for this test is 12. Answer: FALSE Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 13) In a chi-square test of independence the contingency table has 4 rows and 3 columns. The number of degrees of freedom for this test is 7. Answer: FALSE Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 14) In a chi-square test of independence the contingency table has 4 rows and 3 columns. The number of degrees of freedom for this test is 6. Answer: TRUE Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 15) The null hypothesis for a chi-square test of independence is that the two variables are not related. Answer: TRUE Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 16) A goodness of fit test is to be performed to see if consumers prefer any of three package designs (A, B, and C) more than the other two. A sample of 60 consumers is used. What is the expected frequency for category A? A) 1/3 B) 20 C) 60 D) 10 E) 30 Answer: B Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 3

17) A goodness of fit test is to be performed to see if Web Surfers prefer any of four Web sites (A, B, C and D) more than the other three. A sample of 60 consumers is used. What is the expected frequency for Web site A? A) 1/4 B) 20 C) 15 D) 10 E) 30 Answer: C Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 18) A variable contains five categories. It is expected that data are uniformly distributed across these five categories. To test this, a sample of observed data is gathered on this variable resulting in frequencies of 27, 30, 29, 21, and 24. Using α = .01, the degrees of freedom for this test are . A) 5 B) 4 C) 3 D) 2 E) 1 Answer: B Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 19) A variable contains five categories. It is expected that data are uniformly distributed across these five categories. To test this, a sample of observed data is gathered on this variable resulting in frequencies of 27, 30, 29, 21, and 24. Using α = .01, the critical value of chi-square is . A) 7.78 B) 15.09 C) 9.24 D) 13.28 E) 15.48 Answer: D Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

20) A variable contains five categories. It is expected that data are uniformly distributed across these five categories. To test this, a sample of observed data is gathered on this variable resulting in frequencies of 27, 30, 29, 21, and 24. Using α = .01, the observed value of chi-square is . A) 12.09 B) 9.82 C) 13.28 D) 17.81 E) 2.09 Answer: E Diff: 2 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 21) A variable contains five categories. It is expected that data are uniformly distributed across these five categories. To test this, a sample of observed data is gathered on this variable resulting in frequencies of 27, 30, 29, 21, and 24. Using α = .01, the appropriate decision is . A) reject the null hypothesis that the observed distribution is uniform B) reject the null hypothesis that the observed distribution is not uniform C) do not reject the null hypothesis that the observed distribution is uniform D) do not reject the null hypothesis that the observed distribution is not uniform E) do nothing Answer: C Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 22) A suburban realtor is studying commuter time in the Houston metropolitan area. She has been told the average weekday travel time from a southern suburb at 8:00am is 45 minutes. For five months she counts the number of days that it takes her more than 45 minutes to arrive in downtown when she leaves her house at 8:00am. She expects the data are uniformly distributed across the five months. Her sample of observed data yields the following frequencies 9 days, 15 days, 8 days, 11 days, 12 days. Using α = .01, the critical chi-square value is . A) 13.277 B) 15.086 C) 7.779 D) 11.070 E) 2.727 Answer: A Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

23) A suburban realtor is studying commuter time in the Houston metropolitan area. She has been told the average weekday travel time from a southern suburb at 8:00am is 45 minutes. For five months she counts the number of days that it takes her more than 45 minutes to arrive in downtown when she leaves her house at 8:00am. She expects that data are uniformly distributed across the five months. Her sample of observed data yields the following frequencies 9 days, 15 days, 8 days, 11 days, 12 days. Using α = .01, the observed chi-square value is . A) 1.18 B) 9.10 C) 21.75 D) 4.51 E) 2.73 Answer: E Diff: 2 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 24) A suburban realtor is studying commuter time in the Houston metropolitan area. She has been told the average weekday travel time from a southern suburb at 8:00am is 45 minutes. For five months she counts the number of days that it takes her more than 45 minutes to arrive in downtown when she leaves her house at 8:00am. She expects the data are uniformly distributed across the five months. Her sample of observed data yields the following frequencies 9 days, 15 days, 8 days, 11 days, 12 days. Using α = .01, the appropriate decision is . A) do not reject the null hypothesis that the observed distribution is uniform B) do not reject the null hypothesis that the observed distribution is not uniform C) reject the null hypothesis that the observed distribution is uniform D) reject the null hypothesis that the observed distribution is not uniform E) do nothing Answer: A Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

25) A suburban realtor is studying commuter time in the Houston metropolitan area. She has been told the average weekday travel time from a southern suburb at 8:00am is 45 minutes. For five months she counts the number of days that it takes her more than 45 minutes to arrive in downtown when she leaves her house at 8:00am. She expects the data are uniformly distributed across the five months. Her sample of observed data yields the following frequencies 9 days, 15 days, 8 days, 11 days, 12 days. Using α = .10, the appropriate decision is . A) do not reject the null hypothesis that the observed distribution is uniform B) do not reject the null hypothesis that the observed distribution is not uniform C) reject the null hypothesis that the observed distribution is uniform D) reject the null hypothesis that the observed distribution is not uniform E) do nothing Answer: A Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 26) A market researcher is studying the use of coupons by consumers of varying ages. She classifies consumers into four age categories and counts the number of grocery store customers who use at least one coupon during check out. It is expected that data are uniformly distributed across the four age categories. The observed data results in frequencies of 22, 35, 32, and 21. Using α = .05, the critical chisquare value is . A) 13.277 B) 15.086 C) 7.8147 D) 11.070 E) 15.546 Answer: C Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

27) A market researcher is studying the use of coupons by consumers of varying ages. She classifies consumers into four age categories and counts the number of grocery store customers who use at least one coupon during check out. It is expected that data are uniformly distributed across the four age categories. The observed data results in frequencies of 22, 35, 32, and 21. Using α = .05, the observed chi-square value is . A) 5.418 B) 9.10 C) 20.27 D) 4.51 E) 7.86 Answer: A Diff: 2 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 28) A market researcher is studying the use of coupons by consumers of varying ages. She classifies consumers into four age categories and counts the number of grocery store customers who use at least one coupon during check out. It is expected that data are uniformly distributed across the four age categories. The observed data results in frequencies of 22, 35, 32, and 21. Using α = .05, the appropriate decision is _. A) do not reject the null hypothesis that the observed distribution is uniform B) do not reject the null hypothesis that the observed distribution is not uniform C) reject the null hypothesis that the observed distribution is uniform D) reject the null hypothesis that the observed distribution is not uniform E) do nothing Answer: A Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 29) A market researcher is studying the use of coupons by consumers of varying ages. She classifies consumers into four age categories and counts the number of grocery store customers who use at least one coupon during check out. It is expected that data are uniformly distributed across the four age categories. The observed data results in frequencies of 22, 35, 32, and 21. Using α = .10, the appropriate decision is _. A) do not reject the null hypothesis that the observed distribution is uniform B) do not reject the null hypothesis that the observed distribution is not uniform C) reject the null hypothesis that the observed distribution is uniform D) reject the null hypothesis that the observed distribution is not uniform E) do nothing Answer: A Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 8

30) A chi-square goodness of fit test is to be performed to see if data fit the Poisson distribution. There are 6 categories, and lambda must be estimated. How many degrees of freedom should be used? A) 6 B) 5 C) 4 D) 3 E) 2 Answer: C Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 31) A chi-square goodness of fit test is to be performed to see if data fit the Poisson distribution. There are 8 categories, and lambda must be estimated. How many degrees of freedom should be used? A) 8 B) 7 C) 6 D) 5 E) 4 Answer: C Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 32) A chi-square goodness of fit test is to be performed to see if data fit the Poisson distribution. There are 8 categories, and lambda must be estimated. Alpha is chosen to be 0.10. The critical (table) value of chi-square is . A) 10.645 B) 12.017 C) 3.828 D) 16.812 E) 17.345 Answer: A Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

33) A researcher believes that arrivals at a walk-in hair salon are Poisson distributed. The following data represent a distribution of frequency of arrivals in a one-hour time period. Number of customer arrivals Frequency

0 47

1 56

2 39

3 22

4 18

≥5 10

Using α = 0.10, the critical chi-square value for this goodness-of-fit test is . A) 1.064 B) 13.277 C) 9.236 D) 8.799 E) 7.779 Answer: E Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 34) A researcher believes that arrivals at a walk-in hair salon are Poisson distributed. The following data represent a distribution of frequency of arrivals in a one hour time period. Number of customer arrivals Frequency

0 47

1 56

2 39

3 22

4 18

≥5 10

Using α = 0.10, the observed chi-square value for this goodness-of-fit test is . A) 2.28 B) 14.82 C) 17.43 D) 1.68 E) 2.67 Answer: B Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

35) A researcher believes that a variable is Poisson distributed across six categories. To test this, the following random sample of observations is collected: Category Observed

0 7

1 18

2 25

3 17

4 12

>5 5

Using α = 0.10, the critical value of chi-square for the data is . A) 9.236 B) 1.064 C) 13.277 D) 12.89 Answer: B Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 36) A researcher believes that a variable is Poisson distributed across six categories. To test this, the following random sample of observations is collected: Category Observed

0 7

1 18

2 25

3 17

4 12

>5 5

Using α = 0.10, the value of the observed chi-square for the data is . A) 19.37 B) 2.29 C) 1.74 D) 3.28 E) 4.48 Answer: C Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

37) Sami Schmitt believes that the number of cars arriving at his Scrub and Shine Car Wash follows a Poisson distribution. He collected a random sample and constructed the following frequency distribution to test his hypothesis. Cars per 15-minute interval Observed frequency

0 5

1 15

2 17

3 12

4 10

>5 8

The number of degrees of freedom for this goodness-of-fit test is . A) 5 B) 4 C) 3 D) 2 E) 1 Answer: B Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 38) Sami Schmitt believes that the number of cars arriving at his Scrub and Shine Car Wash follows a Poisson distribution. He collected a random sample and constructed the following frequency distribution to test his hypothesis. Cars per 15-minute interval Observed frequency

0 5

1 15

2 17

3 12

4 10

>5 8

Using α = 0.05, the critical value of chi-square for this goodness-of-fit test is . A) 9.49 B) 7.81 C) 7.78 D) 11.07 E) 12.77 Answer: A Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

39) Sami Schmitt believes that the number of cars arriving at his Scrub and Shine Car Wash follows a Poisson distribution. He collected a random sample and constructed the following frequency distribution to test his hypothesis. Cars per 15-minute interval Observed frequency

0 5

1 15

2 17

3 12

4 10

>5 8

The observed value of chi-square for this goodness-of-fit test is closest to . A) 0.73 B) 6.72 C) 3.15 D) 7.81 E) 9.87 Answer: A Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 40) Sami Schmitt believes that the number of cars arriving at his Scrub and Shine Car Wash follows a Poisson distribution. He collected a random sample and constructed the following frequency distribution to test his hypothesis. Cars per 15-minute interval Observed frequency

0 5

1 15

2 17

3 12

4 10

>5 8

Using α = 0.05, the appropriate decision for this goodness-of-fit test is . A) reject the null hypothesis that the observed distribution is Poisson B) reject the null hypothesis that the observed distribution is not Poisson C) do not reject the null hypothesis that the observed distribution is not Poisson D) do not reject the null hypothesis that the observed distribution is Poisson E) do nothing Answer: D Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

41) The manager of a grocery store believes that the number of people waiting in the express check out lane follows a Poisson distribution. While watching the lane at random times throughout the day and week, she collects the following information. People waiting in express check out lane Observed frequency

0 35

1 41

2 22

3 14

4 3

>5 2

Using α = 0.10, the critical value of chi-square for this goodness of fit test is . A) 9.49 B) 7.81 C) 7.78 D) 10.28 E) 11.41 Answer: C Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 42) The manager of a grocery store believes that the number of people waiting in the express check out lane follows a Poisson distribution. While watching the lane at random times throughout the day and week, she collects the following information. People waiting in express check out lane Observed frequency

0 35

1 41

2 22

3 14

4 3

>5 2

The observed value of chi-square for this goodness-of-fit test is closest to . A) 0.99 B) 2.33 C) 4.54 D) 9.77 E) 13.05 Answer: B Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

43) The manager of a grocery store believes that the number of people waiting in the express check out lane follows a Poisson distribution. While watching the lane at random times throughout the day and week, she collects the following information. People waiting in express check out lane Observed frequency

0 35

1 41

2 22

3 14

4 3

>5 2

Using α = 0.10, the appropriate decision for this goodness of fit test is . A) do not reject the null hypothesis that the observed distribution is Poisson B) reject the null hypothesis that the observed distribution is not the Poisson C) do nothing D) do not reject the null hypothesis that the observed distribution is not Poisson E) reject the null hypothesis that the observed distribution is Poisson Answer: A Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 44) In a college dining hall, students with a full meal plan can eat up to four meals each day. The kitchen would like to know the distribution of meal consumption by students and think it would follow a Poisson distribution. Reviewing dining hall data over the past week, the following information is compiled. Meals Observed frequency

0 287

1 581

2 447

3 290

>4 185

Using α = 0.05, the critical value of chi-square for this goodness of fit test is . A) 9.49 B) 7.81 C) 7.78 D) 10.28 E) 11.41 Answer: B Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

45) In a college dining hall, students with a full meal plan can eat up to four meals each day. The kitchen would like to know the distribution of meal consumption by students and think it would follow a Poisson distribution. Reviewing dining hall data over the past week, the following information is compiled. Meals Observed frequency

0 287

1 581

2 447

3 290

>4 185

The observed value of chi-square for this goodness-of-fit test is closest to . A) 1.54 B) 4.88 C) 4.97 D) 8.49 E) 11.27 Answer: E Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 46) In a college dining hall, students with a full meal plan can eat up to four meals each day. The kitchen would like to know the distribution of meal consumption by students and think it would follow a Poisson distribution. Reviewing dining hall data over the past week, the following information is compiled. Meals Observed frequency

0 287

1 581

2 447

3 290

>4 185

Using α = 0.05, the appropriate decision for this goodness of fit test is . A) do not reject the null hypothesis that the observed distribution is Poisson B) reject the null hypothesis that the observed distribution is not the Poisson C) do nothing D) do not reject the null hypothesis that the observed distribution is not Poisson E) reject the null hypothesis that the observed distribution is Poisson Answer: E Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension.

47) In a college dining hall, students with a full meal plan can eat up to four meals each day. The kitchen is now thinking that the meal consumption may more closely resemble a uniform distribution. Reviewing dining hall data over the past week, the following information is compiled. Meals Observed frequency

0 287

1 581

2 447

3 290

>4 185

Using α = 0.05, the appropriate decision for this goodness of fit test is . A) do not reject the null hypothesis that the observed distribution is uniform B) reject the null hypothesis that the observed distribution is uniform C) do nothing D) do not reject the null hypothesis that the observed distribution is not uniform E) reject the null hypothesis that the observed distribution is not uniform Answer: B Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. 48) The chi-square test of independence uses the of two categorical variables to determine whether those variables are independent. A) number of categories B) frequencies C) means D) standard deviations E) total counts Answer: B Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 49) The type of data most appropriate for use in the chi-square test of independence is A) ratio B) interval C) continuous D) ordinal E) nominal Answer: E Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

50) A test of independence is to be performed. The contingency table has 4 rows and 5 columns. What would the degrees of freedom be? A) 20 B) 9 C) 7 D) 12 E) 19 Answer: D Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 51) A contingency table is to be used to test for independence. There are 3 rows and 3 columns in the table. How many degrees of freedom are there for this problem? A) 6 B) 5 C) 4 D) 3 E) 1 Answer: C Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 52) A travel agent believes that vacation destinations are independent of the region of the country that the vacationer resides. She has compiled a table with six vacation destinations and five regions throughout the United States. When applying a chi-square test of independence to this table, the number of degrees of freedom is . A) 9 B) 20 C) 30 D) 11 E) 12 Answer: B Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

53) A market researcher believes that industry type is independent of the operating margin. He compiles a table with seven industry classifications and classifies operating margin into five levels. When applying a chi-square test of independence to this table, the number of degrees of freedom is . A) 24 B) 35 C) 12 D) 10 E) 11 Answer: A Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 54) Contingency tables should not be used with expected cell frequencies . A) less than the number of rows B) less than the number of columns C) less than 5 D) less than 30 E) less than 50 Answer: C Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 55) Use the following set of observed frequencies to test the independence of the two variables. Variable one has values of "A" and "B"; variable two has values of "C", "D", and "E".

A B

C 12 20

D 10 24

E 8 26

Using α = 0.05, the critical chi-square value is . A) 9.488 B) 1.386 C) 8.991 D) 3.357 E) 5.991 Answer: E Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

56) Use the following set of observed frequencies to test the independence of the two variables. Variable one has values of "A" and "B"; variable two has values of "C", "D", and "E".

A B

C 12 20

D 10 24

E 8 26

Using α = 0.05, the observed chi-square value is _ . A) 0 B) 0.69 C) 1.54 D) 21.28 E) 8.29 Answer: C Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 57) Use the following set of observed frequencies to test the independence of the two variables. Variable one has values of "A" and "B"; variable two has values of "C", "D", and "E".

A B

C 12 20

D 10 24

E 8 26

Using α = 0.05, the estimates of the expected frequency in row 1 (A) column 1 (C) when the two variables are independent is . A) 9.6 B) 12 C) 16 D) 10 E) 20 Answer: A Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

58) Sam Hill, Director of Media Research, is analyzing subscribers to the TravelWorld magazine. He wonders whether subscriptions are influenced by the head of household's highest degree earned. His staff prepared the following contingency table from a random sample of 300 households.

Subscribers Subscribers

Yes No

Head of Household Classification Associate Bachelor Master/ PhD 10 90 60 60 60 20

Sam's null hypothesis is . A) "head of household classification" is related to "subscribes" B) "head of household classification" is not independent of "subscribes" C) "head of household classification" is independent of "subscribes" D) "head of household classification" influences "subscribes" E) "clerical is not related to managerial" Answer: C Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 59) Sam Hill, Director of Media Research, is analyzing subscribers to the TravelWorld magazine. He wonders whether subscriptions are influenced by the head of household's highest degree earned. His staff prepared the following contingency table from a random sample of 300 households.

Subscribers Subscribers

Yes No

Head of Household Classification Associate Bachelor Master/ PhD 10 90 60 60 60 20

Using α = .05, the critical value of chi-square is . A) 5.99 B) 3.84 C) 5.02 D) 7.37 E) 9.99 Answer: A Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

60) Sam Hill, Director of Media Research, is analyzing subscribers to the TravelWorld magazine. He wonders whether subscriptions are influenced by the head of household's highest degree earned. His staff prepared the following contingency table from a random sample of 300 households.

Subscribers Subscribers

Yes No

Head of Household Classification Associate Bachelor Master/ PhD 10 90 60 60 60 20

The observed value of chi-square is . A) 5.99 B) 28.30 C) 32.35 D) 60.65 E) 50.78 Answer: D Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 61) Sam Hill, Director of Media Research, is analyzing subscribers to the TravelWorld magazine. He wonders whether subscriptions are influenced by the head of household's highest degree earned. His staff prepared the following contingency table from a random sample of 300 households.

Subscribers Subscribers

Yes No

Head of Household Classification Associate Bachelor Master/ PhD 10 90 60 60 60 20

Using α = .05, the appropriate decision is _. A) reject the null hypothesis and conclude the two variables are independent B) do not reject the null hypothesis and conclude the two variables are independent C) reject the null hypothesis and conclude the two variables are not independent D) do not reject the null hypothesis and conclude the two variables are not independent E) do nothing Answer: C Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

62) Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of alternative toothpaste packages. She wonders whether acceptance is influenced by the age of the children in the household. Her staff prepared the following contingency table from a random sample of 100 households.

Preferred Package Preferred Package

Pump Tube

Children in Household Pre-teenagers teenagers 30 20 10 10

none 10 20

Catherine's null hypothesis is . A) "children in household" is not independent of "preferred package" B) "children in household" is independent of "preferred package" C) "children in household" is related to "preferred package" D) "children in household" influences "preferred package" E) "pump" is independent of "tube" Answer: B Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 63) Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of alternative toothpaste packages. She wonders whether acceptance is influenced by the age of the children in the household. Her staff prepared the following contingency table from a random sample of 100 households.

Preferred Package Preferred Package

Pump Tube

Children in Household Pre-teenagers teenagers 30 20 10 10

none 10 20

Using α = .05, the critical value of chi-square is . A) 5.02 B) 3.84 C) 7.37 D) 6.09 E) 5.99 Answer: E Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

64) Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of alternative toothpaste packages. She wonders whether acceptance is influenced by the age of the children in the household. Her staff prepared the following contingency table from a random sample of 100 households.

Preferred Package Preferred Package

Pump Tube

Children in Household Pre-teenagers teenagers 30 20 10 10

none 10 20

Using α = .05, the observed value of chi-square is . A) 5.28 B) 9.49 C) 13.19 D) 16.79 E) 18.79 Answer: C Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 65) Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of alternative toothpaste packages. She wonders whether acceptance is influenced by the age of the children in the household. Her staff prepared the following contingency table from a random sample of 100 households.

Preferred Package Preferred Package

Pump Tube

Children in Household Pre-teenagers teenagers 30 20 10 10

none 10 20

Using α = .05, the appropriate decision is _. A) reject the null hypothesis and conclude the two variables are not independent B) do not reject the null hypothesis and conclude the two variables are not independent C) reject the null hypothesis and conclude the two variables are independent D) do not reject the null hypothesis and conclude the two variables are independent E) do nothing Answer: A Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

66) Anita Cruz recently assumed responsibility for a large investment portfolio. She wonders whether the industry sector influences investment objective. Her staff prepared the following contingency table from a random sample of 200 common stocks. Industry Sector Investment Objective Growth Income

Electronics 100 20

Airlines 10 20

Healthcare 40 10

Anita's null hypothesis is . A) "investment objective" is related to "industry sector" B) "investment objective" influences "industry sector" C) "investment objective" is not independent of "industry sector" D) "investment objective" is independent of "industry sector" E) "growth" and "income" are independent Answer: D Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 67) Anita Cruz recently assumed responsibility for a large investment portfolio. She wonders whether the industry sector influences investment objective. Her staff prepared the following contingency table from a random sample of 200 common stocks. Industry Sector Investment Objective Growth Income

Electronics 100 20

Airlines 10 20

Healthcare 40 10

Using α = .01, critical chi-square value is _. A) 9.21 B) 7.88 C) 15.09 D) 16.81 E) 18.81 Answer: A Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

68) Anita Cruz recently assumed responsibility for a large investment portfolio. She wonders whether the industry sector influences investment objective. Her staff prepared the following contingency table from a random sample of 200 common stocks. Industry Sector Investment Objective Growth Income

Electronics 100 20

Airlines 10 20

Healthcare 40 10

Using α = .05, critical chi-square value is _. A) 9.21 B) 7.88 C) 15.09 D) 5.99 E) 7.89 Answer: D Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 69) Anita Cruz recently assumed responsibility for a large investment portfolio. She wonders whether the industry sector influences investment objective. Her staff prepared the following contingency table from a random sample of 200 common stocks. Industry Sector Investment Objective Growth Income

Electronics 100 20

Airlines 10 20

Healthcare 40 10

Using α = .01, observed chi-square value is . A) 24.93 B) 8.17 C) 32.89 D) 6.59 E) 4.89 Answer: C Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

70) Anita Cruz recently assumed responsibility for a large investment portfolio. She wonders whether the industry sector influences investment objective. Her staff prepared the following contingency table from a random sample of 200 common stocks. Industry Sector Investment Objective Growth Income

Electronics 100 20

Airlines 10 20

Healthcare 40 10

Using α = .01, appropriate decision is . A) reject the null hypothesis and conclude the two variables are not independent B) reject the null hypothesis and conclude the two variables are independent C) do not reject the null hypothesis and conclude the two variables are not independent D) do not reject the null hypothesis and conclude the two variables are independent E) do nothing Answer: A Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 71) A gasoline distributor wonders whether an individual's income level influences the grade of gasoline purchased. The following is a contingency table from a random sample of 300 individuals.

Personal Income Less than $50,000 $50,000 or More

Regular 90 60

Type of Gasoline Premium Extra Premium 10 20 60 60

The null hypothesis is . A) "income" is independent of "type of gasoline" B) "income" influences "type of gasoline" C) "income" is not independent of "type of gasoline" D) "income" is related to "type of gasoline" E) "regular" is independent of "premium" Answer: A Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

72) A gasoline distributor wonders whether an individual's income level influences the grade of gasoline purchased. The following is a contingency table from a random sample of 300 individuals. Personal Income Less than $50,000 $50,000 or More

Regular 90 60

Type of Gasoline Premium Extra Premium 10 20 60 60

Using α = .01, critical chi-square value is _. A) 15.09 B) 7.88 C) 9.21 D) 16.81 E) 17.89 Answer: C Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 73) A gasoline distributor wonders whether an individual's income level influences the grade of gasoline purchased. The following is a contingency table from a random sample of 300 individuals.

Personal Income Less than $50,000 $50,000 or More

Regular 90 60

Type of Gasoline Premium Extra Premium 10 20 60 60

Using α = .01, observed chi-square value is . A) 24.93 B) 4.44 C) 32.89 D) 51.79 E) 54.98 Answer: D Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

74) A gasoline distributor wonders whether an individual's income level influences the grade of gasoline purchased. The following is a contingency table from a random sample of 300 individuals. Personal Income Less than $50,000 $50,000 or More

Regular 80 70

Type of Gasoline Premium Extra Premium 30 30 40 50

Using α = .05, critical chi-square value is _. A) 15.09 B) 5.99 C) 9.21 D) 16.81 E) 23.87 Answer: B Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 75) A gasoline distributor wonders whether an individual's income level influences the grade of gasoline purchased. The following is a contingency table from a random sample of 300 individuals.

Personal Income Less than $50,000 $50,000 or More

Regular 80 70

Type of Gasoline Premium Extra Premium 30 30 40 50

Using α = .05, observed chi-square value is . A) 15.79 B) 4.44 C) 32.89 D) 51.79 E) 5.79 Answer: E Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

76) A gasoline distributor wonders whether an individual's income level influences the grade of gasoline purchased. The following is a contingency table from a random sample of 300 individuals. Personal Income Less than $50,000 $50,000 or More

Regular 80 70

Type of Gasoline Premium Extra Premium 30 30 40 50

Using α = .05, appropriate decision is . A) reject the null hypothesis and conclude the two variables are not independent B) reject the null hypothesis and conclude the two variables are independent C) do not reject the null hypothesis and conclude the two variables are not independent D) do not reject the null hypothesis and conclude the two variables are independent E) do nothing Answer: D Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. 77) A gasoline distributor wonders whether an individual's income level influences the grade of gasoline purchased. The following is a contingency table from a random sample of 300 individuals.

Personal Income Less than $50,000 $50,000 or More

Regular 90 60

Type of Gasoline Premium Extra Premium 10 20 60 60

The estimate of the expected number of individuals with income less than $30,000 who purchase regular gasoline when income and type of gasoline are independent is . A) 60 B) 90 C) 120 D) 80 E) 100 Answer: A Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis.

78) A recent national poll showed the reading habits of adults during the last 12 months: 3 or more books 2 books 1 book No books

5 27 38 30

A poll in a small Midwestern town shows the following percentages: 3 or more books 2 books 1 book No books

1 22 39 38

The null hypothesis is . A) the poll in the small Midwestern town is not based on a random sample B) the poll in the small Midwestern town is not accurate C) the sample size for the poll in the Midwestern town is not large enough D) the distributions are equal E) the poll in the small Midwestern town is based on a random sample Answer: D Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. Bloom's level: Application

79) A recent national poll showed the reading habits of adults during the last 12 months: 3 or more books 2 books 1 book No books

5 27 38 30

A poll in a small Midwestern town shows the following percentages: 3 or more books 2 books 1 book No books

1 22 39 38

The observed chi-squared statistic is . A) 5.14 B) 5.28 C) 6.14 D) 6.28 E) 6.54 Answer: D Diff: 2 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. Bloom's level: Application

3) A recent national poll showed the reading habits of adults during the last 12 months: 3 or more books 2 books 1 book No books

5 27 38 30

A poll in a small Midwestern town shows the following percentages: 3 or more books 2 books 1 book No books

1 22 39 38

Using α = .05, the critical value of the chi-squared goodness-of-fit statistic is . A) 9.49 B) 7.82 C) 5.99 D) 5.82 E) 5.49 Answer: B Diff: 1 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. Bloom's level: Application

4) A recent national poll showed the reading habits of adults during the last 12 months: 3 or more books 2 books 1 book No books

5 27 38 30

A poll in a small Midwestern town shows the following percentages: 3 or more books 2 books 1 book No books

1 22 39 38

Comparing the critical and observed values of the goodness-of fit chi-squared statistic and α = 0.05, the appropriate decision is . A) reject the null hypothesis that the two distributions are equal B) fail to reject the null hypothesis that the two distributions are unequal C) reject the null hypothesis that the two distributions are unequal D) fail to reject the null hypothesis that the two distributions are equal E) reject the null hypothesis that the sample size for the smaller poll is large enough Answer: D Diff: 3 Response: See section 16.1 Chi-Square Goodness-of-Fit Test Learning Objective: 16.1: Use the chi-square goodness-of-fit test to analyze probabilities of multinomial distribution trials along a single dimension. Bloom's level: Application

82) A market researcher is interested in determining whether the age of listeners influences their preferred musical styles. The following is a contingency table from a random sample of 385 individuals:

18-25 26-35 36-45 46+

Style_1 125 87 50 12

Style_2 13 12 22 25

Style_3 6 6 12 15

The researcher uses α = 0.05. The observed chi-square value is . A) 86.59 B) 88.64 C) 89.59 D) 89.64 E) 90.59 Answer: B Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. Bloom's level: Application 83) A market researcher is interested in determining whether the age of listeners influences their preferred musical styles. The following is a contingency table from a random sample of 385 individuals:

18-25 26-35 36-45 46+

Style_1 125 87 50 12

Style_2 13 12 22 25

Style_3 6 6 12 15

The researcher uses α = 0.05. The critical chi-square value is . A) 9.8 B) 10.9 C) 12.6 D) 15.5 E) 18.7 Answer: C Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. Bloom's level: Application

6) A market researcher is interested in determining whether the age of listeners influences their preferred musical styles. The following is a contingency table from a random sample of 385 individuals:

18-25 26-35 36-45 46+

Style_1 125 87 50 12

Style_2 13 12 22 25

Style_3 6 6 12 15

The researcher uses α = 0.05. The number of degrees of freedom is _. A) 12 B) 11 C) 10 D) 9 E) 6 Answer: E Diff: 1 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. Bloom's level: Application

7) A market researcher is interested in determining whether the age of listeners influences their preferred musical styles. The following is a contingency table from a random sample of 385 individuals:

18-25 26-35 36-45 46+

Style_1 125 87 50 12

Style_2 13 12 22 25

Style_3 6 6 12 15

The researcher uses α = 0.05. The appropriate decision is . A) reject the null hypothesis and conclude the two variables are not independent B) reject the null hypothesis and conclude the two variables are independent C) do not reject the null hypothesis and conclude the two variables are not independent D) do not reject the null hypothesis and conclude the two variables are independent E) do nothing Answer: A Diff: 3 Response: See section 16.2 Contingency Analysis: Chi-Square Test of Independence Learning Objective: 16.2: Use the chi-square test of independence to perform contingency analysis. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 17 Nonparametric Statistics 1) Statistical techniques based on assumptions about the population from which the sample data are selected are called parametric statistics. Answer: TRUE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 2) The methods of parametric statistics can be applied to nominal or ordinal data. Answer: FALSE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 3) Nonparametric statistical techniques are based on fewer assumptions about the population and the parameters compared to parametric statistical techniques. Answer: TRUE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 4) Nonparametric statistics are sometimes called distribution-dependent statistics. Answer: FALSE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 5) An advantage of nonparametric statistics is that the computations on nonparametric statistics are usually less complicated than those for parametric statistics, particularly for small samples. Answer: TRUE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random.

6) A disadvantage of nonparametric statistics is that the probability statements obtained from most nonparametric tests are not exact probabilities. Answer: FALSE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 7) The one-sample runs test is a nonparametric test of randomness in the sample data. Answer: TRUE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 8) In the one-sample runs test for randomness of the observations in a large sample (i.e., the number of observations with each of two possible characteristics is greater than 20) the sampling distribution of R, the number of runs, is approximately binomial. Answer: FALSE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 9) The sampling distribution of R, the number of runs, in the one-sample runs test for randomness of the observations in a large sample (i.e., the number of observations for each of two possible characteristics is greater than 20) is approximately normal, if H0 is true. Answer: TRUE Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 10) The appropriate test for comparing the means of two populations using ordinal-level data from two independent samples is the Mann-Whitney U test. Answer: TRUE Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations.

11) To compare the means of two populations which cannot be assumed to be normally distributed and only ordinal-level data is available from two independent samples, The MannWhitney U test should be used instead of the t-test for independent samples. Answer: TRUE Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 12) The Mann-Whitney U test is a generalization of the two-sample t-test. Answer: TRUE Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 13) The is used to test whether three or more independent of observations are drawn from the same or identical distributions. Answer: FALSE Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 14) The requires that the two samples under consideration have the same number of observations. Answer: FALSE Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 15) The nonparametric counterpart of the t test to compare the means of two independent populations is the Mann-Whitney U test. Answer: TRUE Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 16) The appropriate test for comparing the means of two populations using ordinal-level data from two related samples is the Wilcoxon test and not the Mann-Whitney U test. Answer: TRUE Diff: 1 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 3

17) The appropriate test for comparing the medians of two populations using ordinal-level data from two related samples is the Wilcoxon test. Answer: TRUE Diff: 1 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 18) The Mann-Whitney U test is implemented differently for small samples than for large samples. Answer: TRUE Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 19) No assumptions on the distribution of the difference are needed for the use of the Wilcoxon matched-pairs signed rank. Answer: FALSE Diff: 2 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 20) The nonparametric alternative to linear regression is the Kruskal-Wallis test. Answer: FALSE Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 21) The Kruskal-Wallis test is an extension of the Mann-Whitney U test to 3 or more groups. Answer: TRUE Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 22) The nonparametric alternative to analysis of variance for a randomized block design is the Friedman test. Answer: TRUE Diff: 1 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available. 4

23) The alternative hypothesis for the Friedman test is that at least one treatment is different from at least one other treatment. Answer: TRUE Diff: 1 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available. 24) Prior to computing a Friedman test, the data are ranked within each block from smallest to largest. Answer: TRUE Diff: 1 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available. 25) The degree of association of two variables cannot be estimated when only ordinal-level data are available. Answer: FALSE Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 26) When only ordinal-level data are available, Spearman's rank correlation rather than the Pearson product-moment correlation coefficient must be used to analyze the association between two variables. Answer: TRUE Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 27) Spearman rank correlation values, rs, range between +1 and 0. Answer: FALSE Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables.

28) Statistical techniques based on assumptions about the population from which the sample data are selected are called . A) population statistics B) parametric statistics C) nonparametric statistics D) chi-square statistics E) correlation statistics Answer: B Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 29) The methods of parametric statistics require _ . A) interval or ratio data B) nominal or ordinal data C) large samples D) small samples E) qualitative data Answer: A Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 30) Statistical techniques based on fewer assumptions about the population and the parameters are called . A) population statistics B) parametric statistics C) nonparametric statistics D) chi-square statistics E) correlation statistics Answer: C Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random.

31) Nonparametric statistics are sometimes called . A) nominal statistics B) interval statistics C) distribution-dependent statistics D) distribution-free statistics E) qualitative statistics Answer: D Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 32) The one-sample runs test is a . A) nonparametric test for statistical independence B) parametric test for statistical independence C) nonparametric test of randomness D) nonparametric test for correlation E) parametric test of sequences Answer: C Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 33) A production run of 500 items resulted in 29 defective items. A defective item is coded as 1 and a good item as 0. The following is an output from Minitab. Runs Test: Defects Runs test for Defects Runs above and below K = 0.058 The observed number of runs = 57 The expected number of runs = 55.636 29 observations above K, 471 below P-value = 0.574

The null hypothesis for a one-sample runs test is _ . A) successive items did not constitute a random sample. B) successive items constituted a random sample C) the proportion of defective items is 0.05 D) the proportion of defective items is 0.058 E) the distribution is binomial Answer: B Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 7

34) A production run of 500 items resulted in 29 defective items. A defective item is coded as 1 and a good item as 0. The following is an output from Minitab. Runs Test: Defects Runs test for Defects Runs above and below K = 0.058 The observed number of runs = 57 The expected number of runs = 55.636 29 observations above K, 471 below P-value = 0.574

Using α = 0.1, the conclusion is . A) successive items did not constitute a random sample B) reject the hypothesis that successive items constituted a random sample C) do not reject the hypothesis that successive items constituted a random sample D) the distribution is binomial Answer: C Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 35) The null hypothesis for a one-sample runs test is . A) "the observations in the sample are randomly generated" B) "the observations in the sample are not correlated" C) "the observations in the sample are statistically independent" D) "the observations in the sample are cross-linked" E) "the observations are systematically generated" Answer: A Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 36) The alternate hypothesis for a one-sample runs test is . A) "the observations in the sample are not cross-linked" B) "the observations in the sample are correlated" C) "the observations in the sample are not statistically independent" D) "the observations in the sample are not randomly generated" E) "the observations are not systematically generated" Answer: D Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 8

37) Charles Clayton monitors the daily performance of his investment portfolio by recording a "+" or a "-" sign to indicate whether the portfolio's value increased or decreased from the previous day. His record for the last eighteen business days is "- + + - - - + - - + + + - + + + + ". The number of runs in this sample is . A) uncertain B) four C) five D) nine E) one Answer: D Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 38) Charles Clayton monitors the daily performance of his investment portfolio by recording a "+" or a "-" sign to indicate whether the portfolio's value increased or decreased from the previous day. His record for the last eighteen business days is "- + + - - - + - - + + + + + + + + ". The number of runs in this sample is . A) seven B) six C) four D) three E) one Answer: A Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. 39) The nonparametric counterpart of the t test to compare the means of two independent populations is the . A) chi-square goodness of fit test B) chi-square test of independence C) Mann-Whitney U test D) Wilcoxon test E) Friedman test Answer: C Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations.

40) Which of the following tests should be used to compare the means of two populations if the samples are independent? A) Mann-Whitney test B) Wilcoxon test C) Runs test D) Spearman's test E) Kruskal-Wallis test Answer: A Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 41) A Mann-Whitney U test was performed to determine if there were differences in the cost of tuition at a Texas state university versus an Oklahoma state university. The total cost, including room, board, and books for 24 freshmen attending a Texas university were computed and compared to the total cost for 20 Oklahoma state university students. The U statistic was calculated to be 38.78 based on the sample sizes of 24 and 20. What is the z value for this test? A) 0.133 B) -4.74 C) 240 D) 42.43 E) 8.75 Answer: B Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 42) A Mann-Whitney U test was performed to determine if there were differences in the average compute time for Dallas residents versus Atlanta residents. The travel time for 22 commuters in Dallas was compared to the travel time for 28 commuters in Atlanta. The U statistic was calculated to be 58.0 based on the sample sizes of 22 and 28. What is the z value for this test? A) 51.17 B) 308 C) 0.117 D) -4.89 E) -2.44 Answer: D Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations.

43) Suppose a research uses the Mann-Whitney test to determine if there is a difference in the volume of text messages sent by a high school student living in a rural area versus an urban area during the month of December. Eight rural high school students and 9 urban high school students were included in the study. If, among all 17, the sum of the ranks W1 produced from the rural high school students is 72, the U1 statistic is . A) 29 B) 45 C) 90 D) 36 E) 43 Answer: D Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 44) Suppose a research uses the Mann-Whitney U test to determine if there is a difference in the volume of text messages sent by a high school student living in a rural area versus an urban area during the month of December. Eight rural high school students and 9 urban high school students were included in the study. If, among all 17, the sum of the ranks W2 produced from the urban high school is 88, the U2 test statistic is . A) 29 B) 45 C) 90 D) 43 E) 20 Answer: A Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 45) Suppose a research uses the Mann-Whitney test to determine if there is a difference in the volume of text messages sent by a high school student living in a rural area versus an urban area during the month of December. Eight rural high school students and 9 urban high school students were included in the study. If, among all 17, the U statistic is 29, n1=8 and n2 = 9, the conclusion at α=0.05 would be . A) reject the hypothesis that the number of text messages sent in December is identical B) do not reject the hypothesis that the number of text messages sent in December is identical C) reject the hypothesis that the average number of text messages is identical D) do not reject the hypothesis that the average number of text messages is identical E) accept the hypothesis that the number of text messages sent in December identical Answer: B Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. 11

46) The nonparametric counterpart of the t test to compare the means of two related samples is the . A) chi-square goodness of fit test B) chi-square test of independence C) Mann-Whitney U test D) Wilcoxon test E) Friedman test Answer: D Diff: 1 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 47) Which of the following tests might be used to compare the means of two populations if the samples are related? A) Mann-Whitney test B) Wilcoxon test C) Runs test D) Spearman's test E) Kruskal-Wallis test Answer: B Diff: 1 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 48) The Wilcoxon test was used on 18 pairs of data. The total of the ranks (T) were computed to be 111 (for + ranks) and 60 (for - ranks). The z value for this test is . A) -1.11 B) -0.05 C) -0.07 D) 0.033 E) 2.22 Answer: A Diff: 2 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples.

49) The Wilcoxon test was used on 16 pairs of data. The total of the ranks (T) were computed to be 76 (for + ranks) and 60 (for - ranks). The z value for this test is . A) -0.41 B) -0.02 C) 0.02 D) 16 E) -0.041 Answer: A Diff: 2 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 50) In the Wilcoxon test of the differences between two populations, the value z statistic was calculated to be 1.80. If the level of significance is 0.05, which of the following decisions is appropriate? A) Reject the null hypothesis B) Do not reject the null hypothesis C) Indeterminate without the sample size D) Indeterminate without all of the data E) Inconclusive Answer: B Diff: 1 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 51) In the Wilcoxon test of the differences between two populations, the value z statistic was calculated to be 1.80. If the level of significance is 0.10, which of the following decisions is appropriate? A) Reject the null hypothesis B) Do not reject the null hypothesis C) Indeterminate without the sample size D) Indeterminate without all of the data E) Inconclusive Answer: A Diff: 1 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples.

52) Many "Before and after" types of experiments should be analyzed using . A) chi-square goodness of fit test B) Kruskal-Wallis test C) Mann-Whitney U test D) Wilcoxon test E) Friedman test Answer: D Diff: 1 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 53) In a Wilcoxon matched-pairs signed rank test with 20 matched-pairs of observations, the observed value of the T statistic based on sample data is 76.33. The corresponding observed zvalue is . A) -1.79 B) -2.07 C) -1.70 D) -1.59 E) -1.07 Answer: E Diff: 2 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. 54) The nonparametric alternative to the one-way analysis of variance is the . A) Chi-square goodness of fit test B) Kruskal-Wallis test C) Mann-Whitney U test D) Wilcoxon test E) Friedman test Answer: B Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations.

55) Which of the following tests should be used to compare the means of three populations if the sample data is ordinal? A) One-way analysis of variance B) Kruskal-Wallis test C) Wilcoxon test D) Mann-Whitney test E) Friedman test Answer: B Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 56) The Kruskal-Wallis test is to be used to determine whether there is a significant difference in the satisfaction rating (alpha = 0.05) between three brands of boxed cake mix. Shoppers were asked to rate their satisfaction on various attributes and an aggregate satisfaction score ranging from 1-50 was computed. The following data were obtained: Cake Mix A Cake Mix B Cake Mix C

19 30 39

21 24 32

25 28 41

22 31 42

33 35 27

For this test, how many degrees of freedom should be used? A) 3 B) 2 C) 4 D) 8 E) 1 Answer: B Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations.

57) The Kruskal-Wallis test is to be used to determine whether there is a significant difference in the satisfaction rating (alpha = 0.05) between three brands of boxed cake mix. Shoppers were asked to rate their satisfaction on various attributes and an aggregate satisfaction score ranging from 1-50 was computed. The following data were obtained: Cake Mix A Cake Mix B Cake Mix C

19 30 39

21 24 32

25 28 41

22 31 42

33 35 27

For this situation, the critical (table) chi-square value is . A) 15.507 B) 7.815 C) 9.488 D) 5.991 E) 3.991 Answer: D Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 58) The null hypothesis in the Kruskal-Wallis test is . A) all populations are identical B) all sample means are different C) x and y are not correlated D) the mean difference is zero E) all populations are not identical Answer: A Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 59) A Kruskal-Wallis test is to be performed. There will be four categories, and alpha is chosen to be 0.10. The critical chi-square value is . A) 6.251 B) 2.706 C) 7.779 D) 4.605 E) 3.234 Answer: A Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 60) A Kruskal-Wallis test is to be performed. There will be five categories, and alpha is chosen 16

to be 0.01. The critical chi-square value is . A) 15.086 B) 13.277 C) 7.779 D) 9.236 E) 8.987 Answer: B Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 61) Performance records for 18 salespersons are selected to investigate whether compensation methods are a significant motivational factor. Compensation Method Straight Salary 18 Straight Commission 27 Salary plus Commission 11

12 34 17

22 34 27

Sales 28 28 27 20 14 30

16 22

A Kruskal-Wallis test is to be performed with α = 0.01. The null hypothesis is . A) Group 1 = Group 2 = Group 3 B) Group 1 ≠ Group 2 ≠ Group 3 C) Group 1 ≥ Group 2 ≥ Group 3 D) Group 1 ≤ Group 2 ≤ Group 3 E) Group 1 ≤ Group 2 ≥ Group 3 Answer: A Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations.

62) Performance records for 18 salespersons are selected to investigate whether compensation methods are a significant motivational factor. Compensation Method Straight Salary 18 Straight Commission 27 Salary plus Commission 11

12 34 17

22 34 27

Sales 28 28 27 20 14 30

16 22

A Kruskal-Wallis test is to be performed with α = 0.01. The critical chi-square value is . A) 15.086 B) 13.277 C) 7.779 D) 9.210 E) 8.657 Answer: D Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 63) Performance records for 18 salespersons are selected to investigate whether compensation methods are a significant motivational factor. Compensation Method Straight Salary 18 Straight Commission 27 Salary plus Commission 11

12 34 17

22 34 27

Sales 28 28 27 20 14 30

16 22

A Kruskal-Wallis test is to be performed with α = 0.01. The calculated K value is . A) 15.086 B) 1.715 C) 7.779 D) 9.210 E) 8.657 Answer: B Diff: 3 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations.

64) Performance records for 18 salespersons are selected to investigate whether compensation methods are a significant motivational factor. Compensation Method Straight Salary 18 Straight Commission 27 Salary plus Commission 11

12 34 17

22 34 27

Sales 28 28 27 20 14 30

16 22

A Kruskal-Wallis test performed with α = 0.01 will result in a decision to . A) reject the null hypothesis B) reject the alternate hypothesis C) do not reject the null hypothesis D) do no reject the alternate hypothesis E) do nothing Answer: C Diff: 3 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. 65) The nonparametric alternative to analysis of variance for a randomized block design is the . A) chi-square test B) Kruskal-Wallis test C) Mann-Whitney U test D) Wilcoxon test E) Friedman test Answer: E Diff: 1 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

66) A local pediatrician office is interested in the "no-show" appointment count for each of the 4 pediatricians. "No-show" appointments represent lost revenues because the physician is idle when patients do not show for their scheduled appointments. Using the data in the table below and an alpha of .10, what is the null hypothesis? week 1 week 2 week 3 week 4 week 5

Physician A 8 7 8 8 4

Physician B 4 3 2 6 2

Physician C 6 4 5 7 5

Physician D 7 6 4 9 10

A) The physicians differ in the no-show rates. B) The no show rates differ by day of week. C) The no show rate between physician is equal. D) The no show rate between days of the week is equal. E) The no show rates are dependent upon both day of week and physician. Answer: C Diff: 1 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

67) A local pediatrician office is interested in the "no-show" appointment count for each of the 4 pediatricians. "No-show" appointments represent lost revenues because the physician will be idle when patients do not show for their scheduled appointments. Using the data in the table below and an alpha of 0.10, what is the critical value of χ2? week 1 week 2 week 3 week 4 week 5

Physician A 8 7 8 8 4

Physician B 4 3 2 6 2

Physician C 6 4 5 7 5

Physician D 7 6 4 9 10

A) 4.61 B) 6.25 C) 7.78 D) 13.28 E) 15.09 Answer: B Diff: 2 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

68) A local pediatrician office is interested in the "no-show" appointment count for each of the 4 pediatricians. "No-show" appointments represent lost revenues because the physician will be idle when patients do not show for their scheduled appointments. Using the data in the table below and an alpha of 0.10, what is the observed value of χ2? week 1 week 2 week 3 week 4 week 5

Physician A 8 7 8 8 4

Physician B 4 3 2 6 2

Physician C 6 4 5 7 5

Physician D 7 6 4 9 10

A) 85.68 B) 75.0 C) 13.68 D) 10.68 E) 23.45 Answer: D Diff: 3 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

69) A local pediatrician office is interested in the "no-show" appointment count for each of the 4 pediatricians. "No-show" appointments represent lost revenues because the physician will be idle when patients do not show for their scheduled appointments. Using the data in the table below and an alpha of 0.10, what is the appropriate decision? week 1 week 2 week 3 week 4 week 5

Physician A 8 7 8 8 4

Physician B 4 3 2 6 2

Physician C 6 4 5 7 5

Physician D 7 6 4 9 10

A) Reject the null hypothesis and conclude at least one physician differs in the no-show rates. B) Reject the null hypothesis and conclude the no show rates differ by day of week. C) Fail to reject the null hypothesis and conclude there is not enough evidence to demonstrate the no show rate between physician is equal. D) Fail to reject the null hypothesis and conclude there is not enough evidence to show the no show rate between days of the week is equal. E) Reject the null hypothesis and conclude the no show rates are dependent upon both day of week and physician. Answer: A Diff: 3 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

70) Four types of bicycle tires are ridden in six different cities to see if the tires lasted about the same number of miles. If there are differences, then that tire could promote its durability to potential customers. Using the data in the table below and an alpha of 0.05, what is the null hypothesis? City 1 City 2 City 3 City 4 City 5 City 6

Tire A 105 142 133 98 114 117

Tire B 157 124 121 136 141 138

Tire C 112 132 138 110 95 129

Tire D 119 144 130 119 128 140

A) The durability of the tires between the cities is equal. B) The durability of the tires is dependent on the city and type of tire. C) The durability of the tires is dependent on the type of tire. D) The durability between the types of tires is equal. E) The durability of the tires is dependent on the city. Answer: D Diff: 1 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

71) Four types of bicycle tires are ridden in six different cities to see if the tires lasted about the same number of miles. If there are differences, then that tire could promote its durability to potential customers. Using the data in the table below and an alpha of 0.05, what is the critical value of χ2? City 1 City 2 City 3 City 4 City 5 City 6

Tire A 105 142 133 98 114 117

Tire B 157 124 121 136 141 138

Tire C 112 132 138 110 95 129

Tire D 119 144 130 119 128 140

A) 7.82 B) 5.99 C) 4.61 D) 9.35 E) 3.84 Answer: A Diff: 2 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

72) Four types of bicycle tires are ridden in six different cities to see if the tires lasted about the same number of miles. If there are differences, then that tire could promote its durability to potential customers. Using the data in the table below and an alpha of 0.05, what is the observed value of χ2? City 1 City 2 City 3 City 4 City 5 City 6

Tire A 105 142 133 98 114 117

Tire B 157 124 121 136 141 138

Tire C 112 132 138 110 95 129

Tire D 119 144 130 119 128 140

A) 77.82 B) 5.00 C) 84.67 D) 9.35 E) 4.00 Answer: E Diff: 3 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

73) Four types of bicycle tires are ridden in six different cities to see if the tires lasted about the same number of miles. If there are differences, then that tire could promote its durability to potential customers. Using the data in the table below and an alpha of 0.05, what is the appropriate decision? City 1 City 2 City 3 City 4 City 5 City 6

Tire A 105 142 133 98 114 117

Tire B 157 124 121 136 141 138

Tire C 112 132 138 110 95 129

Tire D 119 144 130 119 128 140

A) Reject the null and conclude that all four tires have the same durability. B) Fail to reject the null and conclude that there is at least one tire whose durability is different from another tire. C) Reject the null and conclude that there is at least one tire whose durability is different from another tire. D) Fail to reject the null and conclude that all four tires have the same durability. E) Fail to reject the null and conclude that the durability of tires does not change by city. Answer: D Diff: 3 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

74) The attention span of kindergarteners are measured in minutes. A teacher wants to see if there are differences in the attention span of kindergarteners at different times of day. Using a sample of five such children, their attention spans are measured at five different times throughout the day. Using the data in the table below and an alpha of 0.05, what is the null hypothesis? Child 1 Child 2 Child 3 Child 4 Child 5

9am 12 10 7 11 12

11am 5 6 10 8 4

noon 10 9 6 5 11

1pm 7 8 8 4 5

3pm 8 12 13 10 8

A) The attention span between the children is equal. B) The attention span between the times of day is equal. C) The attention span differs based on time of day. D) The attention span between the times of day is equal by child. E) The attention span is dependent on the child. Answer: B Diff: 1 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

9) The attention span of kindergarteners are measured in minutes. A teacher wants to see if there are differences in the attention span of kindergarteners at different times of day. Using a sample of five such children, their attention spans are measured at five different times throughout the day. Using the data in the table below and an alpha of 0.05, what is the observed χ2? Child 1 Child 2 Child 3 Child 4 Child 5

9am 12 10 7 11 12

11am 5 6 10 8 4

noon 10 9 6 5 11

1pm 7 8 8 4 5

3pm 8 12 13 10 8

A) 8.11 B) 7.21 C) 12.00 D) 8.96 E) 9.49 Answer: D Diff: 2 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available.

10) The attention span of kindergarteners are measured in minutes. A teacher wants to see if there are differences in the attention span of kindergarteners at different times of day. Using a sample of five such children, their attention spans are measured at five different times throughout the day. Using the data in the table below and an alpha of 0.05, what is the appropriate conclusion? Child 1 Child 2 Child 3 Child 4 Child 5

9am 12 10 7 11 12

11am 5 6 10 8 4

noon 10 9 6 5 11

1pm 7 8 8 4 5

3pm 8 12 13 10 8

A) Fail to reject the null and conclude that attention spans differ at different times of day. B) Reject the null and conclude that attention spans are the same between children. C) Reject the null and conclude that attention spans are impacted by time of day and child. D) Fail to reject the null and conclude that attention spans differ between children. E) Fail to reject the null and conclude that attention spans are the same at different times of day. Answer: E Diff: 3 Response: See section 17.5 Friedman Test Learning Objective: 17.5: use the Friedman test to determine whether different treatment levels come from the same population when a blocking variable is available. 77) The Spearman correlation coefficient is calculated for a set of data on two variables, x and y. It appears that as the rank of x increases, the rank of y is decreasing. We would expect the Spearman correlation coefficient to be . A) equal to zero B) positive C) negative D) greater than 5 E) greater than 1 Answer: C Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables.

78) Correlation coefficients may be computed for parametric and nonparametric data. If the data are nonparametric, which of the following should be used? A) Pearson correlation coefficient B) Spearman correlation coefficient C) Gaussian correlation coefficient D) De Moivre correlation coefficient E) Gossett correlation coefficient Answer: B Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 79) Correlation coefficients may be computed for parametric and nonparametric data. If the data are interval data, which of the following should be used? A) Pearson correlation coefficient B) Spearman correlation coefficient C) Gaussian correlation coefficient D) De Moivre correlation coefficient E) Gossett correlation coefficient Answer: A Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 80) What is the Spearman rank correlation coefficient for the following set of data? x y

19 30

21 24

25 28

22 31

33 35

A) -10.2 B) -2.35 C) 0.65 D) 0.50 E) 0.05 Answer: D Diff: 3 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables.

81) What is the Spearman rank correlation coefficient for the following set of data? x y

21 18

22 24

35 28

32 22

33 35

A) -0.20 B) 1.00 C) 0.20 D) 0.80 E) -1.20 Answer: D Diff: 3 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 82) Personnel specialist, Steve Satterfield, is assessing a new supervisor's ability to follow company standards for evaluating employees. Steve has the new supervisor rate five hypothetical employees on a scale of one to ten. He is interested in how the new supervisor's ratings correlate with company norms for these benchmark cases.

New Supervisor Company Norm

1 8 8

Employee 2 3 4 8 9 7 6 10 4

5 5 4

The Spearman rank correlation coefficient is . A) 0.80 B) 0.85 C) 0.90 D) 0.95 E) 1.00 Answer: D Diff: 3 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables.

83) Two stock analysts rank five investment portfolios for overall performance and risk.

Broker 1 - Rankings Broker 2 - Rankings

A 4 1

Portfolio B C D 5 2 1 4 3 2

E 3 5

Using these rankings, the Spearman rank correlation coefficient is . A) 0.80 B) 0.20 C) 0.05 D) 0.95 E) 1.00 Answer: B Diff: 3 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 84) A perfect Spearman correlation of +1 or -1 between two variables indicates . A) a perfect linear relationship between the two variables B) a perfect nondecreasing or nonincreasing function of the two variables C) the Pearson correlation is 1 D) the Pearson correlation is +1 or -1 E) the two variables are not related Answer: B Diff: 2 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 85) If the Spearman correlation between variable A and variable B was -0.24, then what could be said about the correlation between these two variables? A) There is a high positive correlation between A and B B) There is a low negative correlation between A and B C) There is a high negative correlation between A and B D) There is no correlation between A and B E) There is a low positive correlation between A and B Answer: B Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables.

86) If the Spearman correlation between variable A and variable B was 0.24, then what could be said about the correlation between these two variables? A) There is a high positive correlation between A and B B) There is a low negative correlation between A and B C) There is a high negative correlation between A and B D) There is no correlation between A and B E) There is a low positive correlation between A and B Answer: E Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 87) A table manufacturer makes tables at 12 different heights for different purposes. The CEO would like to know if customers in two different industries place the same value on each of the different heights based on a Likert scale. Ideally, the CEO would prefer that the preferences of the two industries be independent of each other. Based on this, the CEO is hoping that the Spearman correlation coefficient works out to be a . A) high positive value B) high negative value C) low positive value D) value near 0 E) low negative value Answer: D Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables. 88) A table manufacturer makes tables at 12 different heights for different purposes. The CEO would like to know if customers in two different industries place the same value on each of the different heights based on a Likert scale. Ideally, the CEO would prefer that the preferences of the two industries be distinctly different from each other. Based on this, the CEO is hoping that the Spearman correlation coefficient works out to be a . A) high positive value B) high negative value C) low positive value D) value near 0 E) low negative value Answer: B Diff: 1 Response: See section 17.6 Spearman's Rank Correlation Learning Objective: 17.6: Use Spearman's rank correlation to analyze the degree of association of two variables.

5) A quality control supervisor wishes to determine whether the following measurements are random: 68.2 65.3

65 64.2

66.5 67.6

65 66.5

67.5 66.8

68 68.9

66 67.2

64.5 65

65 71.5

66.9 70.7

68 66

71 68.5

68.5 70.5

63.6 69.5

64.7 70.1

For this purpose, the supervisor computes the median of previous observations, and finds it is 67.05. Then she compares each measurement to that median and assigns an "L" to it if it is below the median and a "U" if it is above the median. She determines that the number of Ls and Us are and , respectively. A) 14; 16 B) 17; 13 C) 16; 14 D) 15; 15 E) 12; 18 Answer: D Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. Bloom's level: Application

6) A quality control supervisor wishes to determine whether the following measurements are random: 68.2 65.3

65 64.2

66.5 67.6

65 66.5

67.5 66.8

68 68.9

66 67.2

64.5 65

65 71.5

66.9 70.7

68 66

71 68.5

68.5 70.5

63.6 69.5

64.7 70.1

For this purpose, the supervisor uses a previous median 67.05 and compares each measurement to the median. She determines that the number of runs is . A) 9 B) 10 C) 11 D) 12 E) 13 Answer: E Diff: 2 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. Bloom's level: Application 91) A quality control supervisor wishes to determine whether the following measurements are random: 68.2 65.3

65 64.2

66.5 67.6

65 66.5

67.5 66.8

68 68.9

66 67.2

64.5 65

65 71.5

66.9 70.7

68 66

71 68.5

68.5 70.5

63.6 69.5

64.7 70.1

For this purpose, the supervisor uses a previous median of 67.05 and compares each measurement to the median to perform a runs test. She determines the number of runs and finds that the critical values of the R score are and . A) 10; 18 B) 12; 19 C) 11; 20 D) 9; 21 E) 10; 22 Answer: E Diff: 2 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. Bloom's level: Application

92) A quality control supervisor wishes to determine whether the following measurements are random: 68.2 65.3

65 64.2

66.5 67.6

65 66.5

67.5 66.8

68 68.9

66 67.2

64.5 65

65 71.5

66.9 70.7

68 66

71 68.5

68.5 70.5

63.6 69.5

64.7 70.1

For this purpose, the supervisor uses a previous median of 67.05 and compares each measurement to that median to perform a runs test. She uses a significance level of 0.05. This is a test. A) right-tailed B) forward-tailed C) two-tailed D) inside-tailed E) left-tailed Answer: C Diff: 1 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. Bloom's level: Application 93) A quality control supervisor wishes to determine whether the following measurements are random: 68.2 65.3

65 64.2

66.5 67.6

65 66.5

67.5 66.8

68 68.9

66 67.2

64.5 65

65 71.5

66.9 70.7

68 66

71 68.5

68.5 70.5

63.6 69.5

64.7 70.1

For this purpose, the supervisor uses a previous median of 67.05 and compares each measurement to the median to perform a runs test. She uses a significance level of 0.05. The appropriate decision is . A) reject the null hypothesis and conclude that the observations are random B) reject the null hypothesis and conclude that the observations are not random C) do not reject the null hypothesis and conclude that the observations are random D) do not reject the null hypothesis and conclude that the observations are not random E) do nothing Answer: C Diff: 3 Response: See section 17.1 Runs Test Learning Objective: 17.1: Use both the small-sample and large-sample runs tests to determine whether the order of observations in a sample is random. Bloom's level: Application 37

94) A researcher wants to determine whether the means of two independent populations are the same. She has two independent random samples, one from each population, shown in the following table: 11.5 12.4

Sample I 15.7 22.8 25.3 16.1

22.5 25.1

12.6 14.8

Sample II 13.8 17.5 10.7 11.8 14.7 10.8

13.5 12.7

She uses the Mann-Whitney U test and a significance level of 0.05. The sum of ranks for sample I is . A) 99 B) 101 C) 102 D) 104 E) 106 Answer: A Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. Bloom's level: Application 95) A researcher wants to determine whether the means of two independent populations are the same. She has two independent random samples, one from each population, shown in the following table: 11.5 12.4

Sample I 15.7 22.8 25.3 16.1

22.5 25.1

12.6 14.8

Sample II 13.8 17.5 10.7 11.8 14.7 10.8

13.5 12.7

She uses the Mann-Whitney U test and a significance level of 0.05. The sum of ranks for sample II is . A) 72 B) 73 C) 75 D) 77 E) 78 Answer: A Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. Bloom's level: Application

96) A researcher wants to determine whether the means of two independent populations are the same. She has two independent random samples, one from each population, shown in the following table: 11.5 12.4

Sample I 15.7 22.8 25.3 16.1

22.5 25.1

12.6 14.8

Sample II 13.8 17.5 10.7 11.8 14.7 10.8

13.5 12.7

She uses the Mann-Whitney U test and a significance level of 0.05. The test statistic is . A) 16 B) 17 C) 18 D) 63 E) 64 Answer: B Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. Bloom's level: Application 97) A researcher wants to determine whether the means of two independent populations are the same. She has two independent random samples, one from each population, shown in the following table: 11.5 12.4

Sample I 15.7 22.8 25.3 16.1

22.5 25.1

12.6 14.8

Sample II 13.8 17.5 10.7 11.8 14.7 10.8

13.5 12.7

She uses the Mann-Whitney U test and a significance level of 0.05. Then μU is . A) 4.5 B) 9 C) 31.5 D) 40 E) 63 Answer: D Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. Bloom's level: Application

98) A researcher wants to determine whether the means of two independent populations are the same. She has two independent random samples, one from each population, shown in the following table: 11.5 12.4

Sample I 15.7 22.8 25.3 16.1

22.5 25.1

12.6 14.8

Sample II 13.8 17.5 10.7 11.8 14.7 10.8

13.5 12.7

She uses the Mann-Whitney U test and a significance level of 0.05. Then σU is . A) 10.055 B) 11.025 C) 11.255 D) 12.025 E) 12.255 Answer: C Diff: 2 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. Bloom's level: Application 99) A researcher wants to determine whether the means of two independent populations are the same. She has two independent random samples, one from each population, shown in the following table:

11.5 12.4

Sample I 15.7 22.8 25.3 16.1

22.5 25.1

12.6 14.8

Sample II 13.8 17.5 10.7 11.8 14.7 10.8

13.5 12.7

She uses the Mann-Whitney U test and a significance level of 0.05. This is a test, and the critical . A) one-tailed; value is 1.96 B) two-tailed; values are ±1.96 C) one-tailed; value is 1.645 D) two-tailed; values are ±1.645 E) one-tailed; value is -1.96 Answer: B Diff: 1 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. Bloom's level: Application

100) A researcher wants to determine whether the means of two independent populations are the same. She has two independent random samples, one from each population, shown in the following table: 11.5 12.4

Sample I 15.7 22.8 25.3 16.1

22.5 25.1

12.6 14.8

Sample II 13.8 17.5 10.7 11.8 14.7 10.8

13.5 12.7

She uses the Mann-Whitney U test and a significance level of 0.05. The appropriate decision is . A) reject the null hypothesis that the two populations have different means B) fail to reject the null hypothesis that the two populations have different means C) reject the null hypothesis that the two populations have equal means D) fail to reject the null hypothesis that the two populations have equal means E) do nothing Answer: C Diff: 3 Response: See section 17.2 Mann-Whitney U Test Learning Objective: 17.2: Use both the small-sample and large-sample cases of the MannWhitney U test to determine if there is a difference in two independent populations. Bloom's level: Application

2) A researcher wants to determine the efficacy of a training program directed at improving concentration, focus, and memory on college students. She selects a random sample of 16 individuals and has them take an initial assessment test before the training program. Then the individuals complete the training program, and at the end they take a final test that's comparable to the initial test. The results are shown in the following table: Individ. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Before 79 74 81 75 72.5 75 67.5 57.5 62.5 77.5 61.5 57.5 61.5 72.5 64 71

After 81 73 82.5 82.5 85 90 83.5 72.5 87.5 80 92.5 80 71 84 78.5 97.5

The analyst uses a Wilcoxon matched-pairs signed rank test with a 0.05 significance level. The test statistic T- = . A) 136 B) 142 C) 134 D) 135 E) 152 Answer: D Diff: 2 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. Bloom's level: Application

3) A researcher wants to determine the efficacy of a training program directed at improving concentration, focus, and memory on college students. She selects a random sample of 16 individuals and has them take an initial assessment test before the training program. Then the individuals complete the training program, and at the end they take a final test that's comparable to the initial test. The results are shown in the following table: Individ. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Before 79 74 81 75 72.5 75 67.5 57.5 62.5 77.5 61.5 57.5 61.5 72.5 64 71

After 81 73 82.5 82.5 85 90 83.5 72.5 87.5 80 92.5 80 71 84 78.5 97.5

The analyst uses a Wilcoxon matched-pairs signed rank test with a 0.05 significance level. The value of μT is . A) 68 B) 69 C) 70 D) 71 E) 72 Answer: A Diff: 2 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. Bloom's level: Application

4) A researcher wants to determine the efficacy of a training program directed at improving concentration, focus, and memory on college students. She selects a random sample of 16 individuals and has them take an initial assessment test before the training program. Then the individuals complete the training program, and at the end they take a final test that's comparable to the initial test. The results are shown in the following table: Individ. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Before 79 74 81 75 72.5 75 67.5 57.5 62.5 77.5 61.5 57.5 61.5 72.5 64 71

After 81 73 82.5 82.5 85 90 83.5 72.5 87.5 80 92.5 80 71 84 78.5 97.5

The analyst uses a Wilcoxon matched-pairs signed rank test with a 0.05 significance level. The value of σT is . A) 17.257 B) 19.339 C) 21.257 D) 23.339 E) 25.257 Answer: B Diff: 2 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. Bloom's level: Application

5) A researcher wants to determine the efficacy of a training program directed at improving concentration, focus, and memory on college students. She selects a random sample of 16 individuals and has them take an initial assessment test before the training program. Then the individuals complete the training program, and at the end they take a final test that's comparable to the initial test. The results are shown in the following table: Individ. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Before 79 74 81 75 72.5 75 67.5 57.5 62.5 77.5 61.5 57.5 61.5 72.5 64 71

After 81 73 82.5 82.5 85 90 83.5 72.5 87.5 80 92.5 80 71 84 78.5 97.5

The analyst uses a Wilcoxon matched-pairs signed rank test with a 0.05 significance level. The observed z score is . A) -3.4645 B) -3.2157 C) -3.0178 D) -2.9857 E) -2.7978 Answer: A Diff: 3 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. Bloom's level: Application

6) A researcher wants to determine the efficacy of a training program directed at improving concentration, focus, and memory on college students. She selects a random sample of 16 individuals and has them take an initial assessment test before the training program. Then the individuals complete the training program, and at the end they take a final test that's comparable to the initial test. The results are shown in the following table: Individ. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Before 79 74 81 75 72.5 75 67.5 57.5 62.5 77.5 61.5 57.5 61.5 72.5 64 71

After 81 73 82.5 82.5 85 90 83.5 72.5 87.5 80 92.5 80 71 84 78.5 97.5

The analyst uses a Wilcoxon matched-pairs signed rank test with a 0.05 significance level. The appropriate decision is . A) fail to reject the null hypothesis that the before and after scores are different B) reject the null hypothesis that that the before and after scores are different C) fail to reject the null hypothesis that the before and after scores are not different D) reject the null hypothesis that the before and after scores are not different E) do nothing Answer: D Diff: 3 Response: See section 17.3 Wilcoxon Matched-Pairs Signed Rank Test Learning Objective: 17.3: Use both the small-sample and large-sample cases of the Wilcoxon matched-pairs signed rank test to compare the difference in two related samples. Bloom's level: Application

7) A researcher wants to study the effects of physical exercise on depression. She randomly selects a group of 24 individuals who are equally depressed. None of these individuals currently exercises. She then randomly assigns them in one of three groups: group 1 stays without exercising (sedentary group), group 2 exercises once a week, and group 3 exercises two times a week. Individuals in groups 2 and 3 meet with a personal trainer so they carry out a very similar exercise program (kinds of exercises and intensity). After two months, the researcher has all individuals take a depression-assessment test. The score of the test goes from 0 = totally miserable to 100 = complete satisfaction. The scores are shown in the following table: Group 1 25 15 20 17 23 21 19 18

Group 2 24 16 19 24 25 25 21 18

Group 3 35 22 31 32 31 29 33 35

The researcher uses a Kruskal-Wallis test and a significance level α = 0.05. The sum of the ranks for group 3, T3, is . A) 157 B) 158 C) 159 D) 160 E) 161 Answer: B Diff: 3 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. Bloom's level: Application

8) A researcher wants to study the effects of physical exercise on depression. She randomly selects a group of 24 individuals who are equally depressed. None of these individuals currently exercises. She then randomly assigns them in one of three groups: group 1 stays without exercising (sedentary group), group 2 exercises once a week, and group 3 exercises two times a week. Individuals in groups 2 and 3 meet with a personal trainer so they carry out a very similar exercising program, both in kinds of exercises and in intensity. After two months, the researcher has all individuals take a depression-assessment test. The score of the test goes from 0 = totally miserable to 100 = complete satisfaction. The scores are shown in the following table: Group 1 25 15 20 17 23 21 19 18

Group 2 24 16 19 24 25 25 21 18

Group 3 35 22 31 32 31 29 33 35

The researcher uses a Kruskal-Wallis test and a significance level α = 0.05. The observed K statistic is . A) 10.16625 B) 10.7525 C) 11.16625 D) 12.7525 E) 13.16625 Answer: E Diff: 3 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. Bloom's level: Application

9) A researcher wants to study the effects of physical exercise on depression. She randomly selects a group of 24 individuals who are equally depressed. None of these individuals currently exercises. She then randomly assigns them in one of three groups: group 1 stays without exercising (sedentary group), group 2 exercises once a week, and group 3 exercises two times a week. Individuals in groups 2 and 3 meet with a personal trainer so they carry out a very similar exercising program, both in kinds of exercises and in intensity. After two months, the researcher has all individuals take a depression-assessment test. The score of the test goes from 0 = totally miserable to 100 = complete satisfaction. The scores are shown in the following table: Group 1 25 15 20 17 23 21 19 18

Group 2 24 16 19 24 25 25 21 18

Group 3 35 22 31 32 31 29 33 35

The researcher uses a Kruskal-Wallis test and a significance level α = 0.05. The relevant number of degrees of freedom is . A) 1 B) 2 C) 3 D) 23 E) 24 Answer: B Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. Bloom's level: Application

10) A researcher wants to study the effects of physical exercise on depression. She randomly selects a group of 24 individuals who are equally depressed. None of these individuals currently exercises. She then randomly assigns them in one of three groups: group 1 stays without exercising (sedentary group), group 2 exercises once a week, and group 3 exercises two times a week. Individuals in groups 2 and 3 meet with a personal trainer so they carry out a very similar exercising program, both in kinds of exercises and in intensity. After two months, the researcher has all individuals take a depression-assessment test. The score of the test goes from 0 = totally miserable to 100 = complete satisfaction. The scores are shown in the following table: Group 1 25 15 20 17 23 21 19 18

Group 2 24 16 19 24 25 25 21 18

Group 3 35 22 31 32 31 29 33 35

The researcher uses a Kruskal-Wallis test and a significance level α = 0.05. The critical chisquare value is . A) 9.35 B) 7.82 C) 7.38 D) 6.82 E) 5.99 Answer: E Diff: 1 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. Bloom's level: Application

11) A researcher wants to study the effects of physical exercise on depression. She randomly selects a group of 24 individuals who are equally depressed. None of these individuals currently exercises. She then randomly assigns them in one of three groups: group 1 stays without exercising (sedentary group), group 2 exercises once a week, and group 3 exercises two times a week. Individuals in groups 2 and 3 meet with a personal trainer so they carry out a very similar exercising program, both in kinds of exercises and in intensity. After two months, the researcher has all individuals take a depression-assessment test. The score of the test goes from 0 = totally miserable to 100 = complete satisfaction. The scores are shown in the following table: Group 1 25 15 20 17 23 21 19 18

Group 2 24 16 19 24 25 25 21 18

Group 3 35 22 31 32 31 29 33 35

The researcher uses a Kruskal-Wallis test and a significance level α = 0.05. The appropriate decision is . A) fail to reject the null hypothesis that the three groups have different levels of depression B) reject the null hypothesis that the three groups have different levels of depression C) fail to reject the null hypothesis that the three groups have equal levels of depression D) reject the null hypothesis that the three groups have equal levels of depression E) fail to reject the null hypothesis that the sample is random Answer: D Diff: 3 Response: See section 17.4 Kruskal-Wallis Test Learning Objective: 17.4: Use the Kruskal-Wallis test to determine whether three or more samples come from the same or different populations. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 18 Statistical Quality Control 1) One definition that captures the spirit of most quality efforts in the business world is that quality is present when a product delivers what is stipulated for it in its specifications. Answer: TRUE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 2) Quality control is the collection of strategies, techniques, and actions taken by an organization to assure itself that it is producing a quality product. Answer: TRUE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 3) Quality control can be undertaken in two distinct ways: after-process control and beforeprocess control. Answer: FALSE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 4) Inspecting the attributes of a finished product to determine whether the product is acceptable, is in need of rework, or is to be rejected is called after-process quality control. Answer: TRUE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 1

5) Measuring product attributes at regular intervals throughout the manufacturing process in an effort to pinpoint problem areas is called in-process quality control. Answer: TRUE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 6) The strategy called Total Quality Management (TQM) was embodied in the principles advocated by the well-known quality guru, W. Edwards Deming. Answer: TRUE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 7) W. Edwards Deming was a quality guru whose principles of quality management can be summarized through four basic tenets, or "Absolutes". Answer: FALSE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 8) The Six Sigma approach essentially calls for the process to approach defect-free status. Answer: TRUE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event.

9) Six Sigma is a methodology for quality and does not relate to a measurement value of the process. Answer: FALSE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 10) A quality circle is a round-table of top-level quality managers. Answer: FALSE Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 11) A schematic representation of all the activities and interactions that occur in a process is called a flowchart. Answer: TRUE Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 12) A Pareto chart is a diagnostic tool that displays possible causes of a quality problem and the interrelationships among the causes. Answer: FALSE Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 13) A control chart is used to control the flow of materials into a process. Answer: FALSE Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 3

14) A scatter diagram is a graphical mechanism for examining the relationship between two variables. Answer: TRUE Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 15) In a flow chart, an oval represents a start or stop in the process. Answer: TRUE Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 16) Another name for a cause-and-effect diagram is a herringbone diagram. Answer: FALSE Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 17) Control charts are used to examine the output of a process for disturbing patterns or for data points that indicate that the process is out of control. Answer: TRUE Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 18) If no variation occurred between manufactured items, the resulting points on a control chart would form a horizontal line. Answer: TRUE Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

19) Two general types of control charts are (1) control charts for manufactured items and (2) control charts for services. Answer: FALSE Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 20) Two general types of control charts are (1) control charts for measurements and (2) control charts for compliance items. Answer: TRUE Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 21) A c chart used for statistical quality control is a chart that shows the count of defects in the process output. Answer: TRUE Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 22) If a process is in control, less than 0.3% of all the points that represent the process output such as the average measurement or the proportion defective should be beyond the upper and lower control limits. Answer: TRUE Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 23) According to Garvin, transcendent quality implies that a product . A) conforms to design and engineering specifications B) has an innate excellence C) has no measurable attributes D) is fit for the consumer's intended use E) has measurable attributes Answer: B Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 5

24) According to Garvin, product quality is . A) value perceived by the customer B) an innate excellence of the product C) not measurable in the product D) fitness for the consumer's intended use E) measurable in the product Answer: E Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 25) A recognized brand name product means higher quality to many consumers. Among Garvin's five quality types, this is an example of . A) measurable quality B) transcendent quality C) product quality D) manufacturing-based quality E) value quality Answer: B Diff: 2 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 26) Higher output wattage means higher quality to some consumers of audio amplifiers. Among Garvin's five quality types, this is an example of . A) user quality B) transcendent quality C) product quality D) manufacturing-based quality E) value quality Answer: C Diff: 2 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 6

27) A manufacturer of washing machines that through its programming, promotes lower water usage per load cycle when compared to the average brand. Among Garvin's five quality types, this is an example of . A) user quality B) transcendent quality C) product quality D) manufacturing-based quality E) value quality Answer: D Diff: 2 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 28) Reengineering is . A) a fine-tuning of the present process B) downsizing of a company C) another name for Deming's 14 points D) the complete redesign of core business processes E) incremental improvement of a core process Answer: D Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 29) Which of the following statements best describes Design for Six Sigma? A) A process for designing a quality improvement team. B) The measure and improve steps of a six sigma initiative. C) A framework for implementing Deming's 14 points. D) A root cause analysis using 100% inspection. E) A quality scheme that emphasizes designing the product or process to perform defect-free. Answer: E Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 7

30) Which of the following is not a part of Deming's 14 points to improve total quality management? A) Create constancy of purpose for improvement of products and services B) Adopt the new philosophy C) Improve constantly and forever every process for planning, production and service D) Increase dependence on mass inspection E) Take action to accomplish the transformation Answer: D Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 31) A company that uses benchmarking as a quality improvement practice will . A) emulate the best practices and techniques used in their industry B) institutionalize the not-invented-here philosophy C) not analyze the competition's product D) rely exclusively on government research for product improvements E) be in the furniture business Answer: A Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 32) An advantage of a just-in-time inventory system is . A) fewer managerial controls on inventory B) lower inventory holding costs C) larger shipment and production lots D) fewer orders per operational year E) more inspection and handling of materials Answer: B Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. 8

33) Which of the following is not a quality control diagnostic technique? A) c control chart B) Flowchart C) Scatter diagram D) x̄ control chart E) Cause-and-effect diagram Answer: B Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 34) According to the following Pareto chart, the most common complaint was

A) user interface B) speaker quality C) camera D) size E) all of the above Answer: A Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts.

35) The Pareto principle would suggest that a quality improvement team focus their efforts on those problems that account for 80% of the complaints. Based on this principle and the Pareto chart below, the team should focus their efforts on which of the following complaints?

A) user interface only B) user interface and speaker quality C) user interface, speaker quality and camera D) speaker quality and camera E) all of the complaints should be a focus Answer: B Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts.

36) Imagine a cause-and-effect diagram had five elements listed at the end of each line, either at the head of the diagram or at the end of each "bone" of the diagram. These elements were transportation, enforcement, late arrivals, policy, and attitude. Of these, which would be most likely to be at the head of the diagram? A) Transportation B) Enforcement C) Late arrivals D) Policy E) Attitude Answer: C Diff: 2 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 37) In many public restrooms, management will keep a sheet on the back of the door noting the times that the restroom is to be cleaned as the column headings and each row denoting a step of the cleaning to be completed. This would be an example of a . A) Pareto chart B) histogram C) scatter plot D) check sheet E) control chart Answer: D Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 38) A histogram is often used by researchers to gain a(n) of the data. A) initial overview B) in-depth analysis C) statistically significant view D) sense of the mean E) view of the dispersion Answer: A Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts.

39) Which of the following is not usually an activity that would be included in a flowchart? A) Starting point B) Processing step C) Decision points D) Person responsible E) Input Answer: D Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 40) A flowchart can help identify within the . A) problems; process B) processes; activities C) main causes; problems D) most frequent problem; activities E) dispersion; process Answer: A Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 41) In reviewing a flowchart, a company would not be able to determine _ in a process. A) the various start points B) how many steps are C) the total time for all activities D) the decision points E) the various inputs needed Answer: C Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts.

42) The main difference between a histogram and a Pareto chart is the . A) values on the y axis B) order of categories on the x axis C) colors used to distinguish differences D) type of data that can be used to create each graph E) direction of the bars, either vertical or horizontal Answer: B Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 43) Working from a Pareto chart, a company wants to address the two most common causes of interruptions in the company's production process. Managers should focus on the of the chart. A) width of each bar B) bars to the far right C) shortest bars D) bars to the far left E) tallest bar Answer: D Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 44) If a manufacturer is looking to determine the causes of declining productivity, the company might first complete a to identify potential causes, and then create a to find which causes are most common. A) scatter plot; histogram B) Pareto chart; histogram C) cause-and-effect diagram; Pareto chart D) Pareto chart; cause-and-effect diagram E) histogram; Pareto chart Answer: C Diff: 2 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts.

45) Say a manufacturer maintains a control chart of the overall product process and determines that the process is out of control too often across all shifts. Managers could most effectively start to determine potential causes by creating a _ . A) histogram with shifts along the x axis B) cause-and-effect diagram for one shift C) scatter plot with shifts on the x axis and causes on the y axis D) Pareto chart with shifts along the x axis E) check sheet with shifts as columns and causes as rows Answer: E Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. 46) A quality circle studying the problem of incorrect prices on purchase orders identified several potential causes: incorrect information from requesting department, out-of-date catalogs from suppliers, defective computer software, and worker practices in the purchasing department. These potential cause-and-effect relationships are best illustrated by a . A) check list B) Pareto chart C) control chart D) point-and-figure chart E) Fishbone diagram Answer: E Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 47) Upper and lower control limits are usually based upon . A) ± 3 standard deviations B) ± 2 standard deviations C) ± 1 standard deviation D) ± 4 standard deviations E) ± 6 standard deviations Answer: A Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

48) A graph that displays computed means for a series of small random samples over a period of time is called a(n) . A) chart B) R chart C) p chart D) c chart E) S chart Answer: A Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 49) A plot of the sample ranges over regular time intervals is called a(n) . A) chart B) R chart C) p chart D) c chart E) S chart Answer: B Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 50) An chart is being developed using 15 samples of size 9 each. The average of 15 sample means is 6.20. The average of the 15 ranges is 0.30. The upper control limit is . A) 6.301 B) 6.267 C) 6.133 D) 6.099 E) 6.312 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

51) An chart is being developed using 15 samples of size 9 each. The average of 15 sample means is 6.20. The average of the 15 ranges is 0.30. The lower control limit is . A) 6.301 B) 6.267 C) 6.133 D) 6.099 E) 6.312 Answer: D Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 52) An R Chart is to be developed for use in quality control. The average of the ranges is calculated to be 0.45. The sample sizes were 9 each. What would the upper control limit be? A) 0.8172 B) 0.6012 C) 0.0828 D) 0.1566 E) 0.7434 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 53) An R Chart is to be developed for use in quality control. The average of the ranges is calculated to be 0.45. The sample sizes were 9 each. What would the lower control limit be? A) 0.8172 B) 0.6012 C) 0.0828 D) 0.1566 E) 0.7434 Answer: C Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

54) An R Chart is to be developed for use in quality control. The average of the ranges is calculated to be 0.60. This was based on several samples of size 7 each. What would the lower control limit be? A) 0.046 B) 0.000 C) 1.154 D) 4.200 E) 0.004 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 55) An R Chart is to be developed for use in quality control. The average of the ranges is calculated to be 0.60. This was based on several samples of size 7 each. What would the upper control limit be? A) 1.514 B) 1.924 C) 4.200 D) 0.600 E) 1.154 Answer: E Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 56) Sarah Soliz, Director of Quality Programs, is designing and R charts for the rod shearing process at Stockton Steel. She has 26 samples of rod length, and each sample included measurements of 5 rods. The mean of the 26 sample means is 112 inches, and mean of the 26 ranges is 0.15 inch. The centerline for her chart is . A) 5 B) 26 C) 0.15 D) 11.2 E) 112 Answer: E Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

57) Sarah Soliz, Director of Quality Programs, is designing and R charts for the rod shearing process at Stockton Steel. She has 26 samples of rod length, and each sample included measurements of 5 rods. The mean of the 26 sample means is 112 inches, and mean of the 26 ranges is 0.15 inch. The upper control limit for her chart is . A) 112.09 B) 5.087 C) 26.087 D) 115.90 E) 110.09 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 58) Sarah Soliz, Director of Quality Programs, is designing and R charts for the rod shearing process at Stockton Steel. She has 26 samples of rod length, and each sample included measurements of 5 rods. The mean of the 26 sample means is 112 inches, and mean of the 26 ranges is 0.15 inch. The lower control limit for her chart is . A) 25.913 B) 4.913 C) 111.91 D) 108.10 E) 112.84 Answer: C Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 59) Sarah Soliz, Director of Quality Programs, is designing and R charts for the rod shearing process at Stockton Steel. She has 26 samples of rod length, and each sample included measurements of 5 rods. The mean of the 26 sample means is 112 inches, and mean of the 26 ranges is 0.15 inch. The centerline for her R chart is . A) 0.15 B) 26 C) 5 D) 112 E) 15 Answer: A Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

60) Sarah Soliz, Director of Quality Programs, is designing and R charts for the rod shearing process at Stockton Steel. She has 26 samples of rod length, and each sample included measurements of 5 rods. The mean of the 26 sample means is 112 inches, and mean of the 26 ranges is 0.15 inch. The upper control limit for her R chart is . A) 0.150 B) 10.57 C) 0.317 D) 2.114 E) 0.713 Answer: C Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 61) Sarah Soliz, Director of Quality Programs, is designing and R charts for the rod shearing process at Stockton Steel. She has 26 samples of rod length, and each sample included measurements of 5 rods. The mean of the 26 sample means is 112 inches, and mean of the 26 ranges is 0.15 inch. The lower control limit for her R chart is . A) 0.150 B) 0.000 C) 0.317 D) 2.114 E) 1.000 Answer: B Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 62) A graph that plots the proportions of items in noncompliance for multiple samples is called a(n) . A) chart B) R chart C) p chart D) c chart E) Pareto chart Answer: C Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

63) A p chart is to be developed for use in quality control. The value of p is calculated to be 0.05. The sample size is 50. What would the upper control limit be? A) 0.142 B) -0.042 C) 0.408 D) 0.092 E) 0.642 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 64) A p chart is to be developed for use in quality control. The value of p is calculated to be 0.54. The sample size is 249. What would the upper control limit be? A) 0.5941 B) 0.6348 C) 0.4452 D) 0.5460 E) 0.5340 Answer: B Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 65) A p chart is to be developed for use in quality control. The value of p is calculated to be 0.54. The sample size is 249. What would the lower control limit be? A) 0.5941 B) -0.6348 C) 0.4452 D) -0.5460 E) 0.5340 Answer: C Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

66) Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), is designing a p chart to monitor the default rate on personal loans at the AFB member banks. Her data include the proportion in default for 30 samples of personal loans. Each sample contains 50 loans, and the average of the 30 proportions is 0.05. The centerline for Ophelia's p chart is . A) 30 B) 0.50 C) 50 D) 1.5 E) 0.05 Answer: E Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 67) Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), is designing a p chart to monitor the default rate on personal loans at the AFB member banks. Her data include the proportion in default for 30 samples of personal loans. Each sample contains 50 loans, and the average of the 30 proportions is 0.05. The upper control limit for Ophelia's p chart is . A) 0.0925 B) 0.0500 C) 0.0308 D) 0.1825 E) 0.1425 Answer: E Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 68) Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), is designing a p chart to monitor the default rate on personal loans at the AFB member banks. Her data include the proportion in default for 30 samples of personal loans. Each sample contains 50 loans, and the average of the 30 proportions is 0.05. The lower control limit for Ophelia's p chart is . A) 0.0000 B) 0.0204 C) 0.0308 D) 0.0149 E) -1.0000 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 21

69) Nels Neugent, Purchasing Manager at Mid-West Medical Center, is designing a p chart to monitor the proportion of defective purchase orders issued at Mid-West. He has the proportion of defective orders for 22 samples of purchase orders. Each sample contains 150 purchase orders, and the average proportion defective is 0.08. The centerline for Nels's p chart is . A) 0.08 B) 75 C) 22 D) 1.76 E) 0.008 Answer: A Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 70) Nels Neugent, Purchasing Manager at Mid-West Medical Center, is designing a p chart to monitor the proportion of defective purchase orders issued at Mid-West. He has the proportion of defective orders for 22 samples of purchase orders. Each sample contains 150 purchase orders, and the average proportion defective is 0.08. The upper control limit for Nels's p chart is . A) 0.1736 B) 0.1465 C) 0.1312 D) 0.0940 E) 0.1845 Answer: B Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 71) Nels Neugent, Purchasing Manager at Mid-West Medical Center, is designing a p chart to monitor the proportion of defective purchase orders issued at Mid-West. He has the proportions defective for 22 samples of purchase orders. Each sample contains 150 purchase orders, and the average proportion defective is 0.08. The lower control limit for Nels's p chart is . A) 0.0447 B) 0.0283 C) 0.0135 D) 0.0000 E) 0.5090 Answer: C Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

72) A graph that plots the number of nonconformances per item for multiple samples is called a(n) . A) chart B) R chart C) p chart D) c chart E) Pareto chart Answer: D Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 73) Jake Flanaghan, Vice President for the inpatient pharmacy at Great Atlantic Medical Center, is designing a c chart to monitor the number of inpatient medication errors that occur each month. The total number of medication errors for January - December last year was 37. The centerline for Jake's c chart is . A) 3.08 B) 37 C) 8.35 D) -2.18 E) 0.00 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 74) Jake Flanaghan, Vice President for the inpatient pharmacy at Great Atlantic Medical Center, is designing a c chart to monitor the number of inpatient medication errors that occur each month. The total number of medication errors for January - December last year was 37. The upper control limit for Jake's c chart is . A) 3.08 B) 37 C) 8.35 D) -2.18 E) 0.00 Answer: C Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

75) Jake Flanaghan, Vice President for the inpatient pharmacy at Great Atlantic Medical Center, is designing a c chart to monitor the number of inpatient medication errors that occur each month. The total number of medication errors for January - December last year was 37. The lower control limit for Jake's c chart is . A) 3.08 B) 37 C) 8.35 D) -2.18 E) 0.00 Answer: E Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 76) Jack Joyner, Director of Quality Control at Covington Castings (CC), is designing a c chart to monitor the number of nonconformances per aluminum casting produced at CC. The total number of nonconformances for 26 castings is 91. The centerline for Jack's c chart is . A) 117.00 B) 0.2857 C) 3.50 D) 65.00 E) 26.00 Answer: C Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 77) Jack Joyner, Director of Quality Control at Covington Castings (CC), is designing a c chart to monitor the number of nonconformances per aluminum casting produced at CC. The total number of nonconformances for 26 castings is 91. The upper control limit for Jack's c chart is . A) 9.11 B) 13.40 C) 3.50 D) 7.61 E) 1.00 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

78) Jack Joyner, Director of Quality Control at Covington Castings (CC), is designing a c chart to monitor the number of nonconformances per aluminum casting produced at CC. The total number of nonconformances for 26 castings is 91. The lower control limit for Jack's c chart is . A) 1.37 B) -2.11 C) 3.50 D) -1.00 E) 0.00 Answer: E Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 79) Which of the following quality control chart conditions is an indicator that the process is, potentially, out-of-control? A) Several consecutive data points between the UCL and the LCL B) A data point above the LCL C) A data point below the UCL D) An upward trend of nine data points E) A data point in the outer 1/3 region Answer: D Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 80) Which of the following quality control chart conditions is an indicator that the process is potentially out-of-control? A) Several consecutive data points between the UCL and the LCL B) A data point below the LCL C) A data point below the UCL D) A data point in the outer 1/3 region E) A data point on the center line Answer: B Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

81) Which of the following quality control chart conditions is an indicator that the process is potentially out-of-control? A) Several consecutive data points between the UCL and the LCL B) A data point above the LCL C) A data point above the UCL D) A data point in the outer 1/3 region E) A data point on the center line Answer: C Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 82) You are conducting a study on the blood glucose levels of 9 patients who are on strict diets and exercise routines. To monitor the mean and range of the blood glucose levels of your patients, you take a blood glucose reading every day for each patient for 20 days. The results are shown below. Day 1 2 3 4 5 6 7 8 9 10

Mean 92.11 90.67 89.44 93.33 89.44 110.20 104.10 108.20 107.70 112.70

Range 80 77 73 81 71 90 120 93 98 145

Day 11 12 13 14 15 16 17 18 19 20

Mean 100.70 96.67 97.33 95.89 98.89 101.22 107.33 108.00 106.89 109.89

Range 89 77 75 80 68 88 81 95 77 80

The centerline for your chart is . A) 112.70 B) 106.7 C) 101.035 D) 100 E) 110 Answer: C Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

7) You are conducting a study on the blood glucose levels of 9 patients who are on strict diets and exercise routines. To monitor the mean and range of the blood glucose levels of your patients, you take a blood glucose reading every day for each patient for 20 days. The results are shown below. Day 1 2 3 4 5 6 7 8 9 10

Mean 92.11 90.67 89.44 93.33 89.44 110.20 104.10 108.20 107.70 112.70

Range 80 77 73 81 71 90 120 93 98 145

Day 11 12 13 14 15 16 17 18 19 20

Mean 100.70 96.67 97.33 95.89 98.89 101.22 107.33 108.00 106.89 109.89

Range 89 77 75 80 68 88 81 95 77 80

The upper control limit for your chart is . A) 112.70 B) 116.18 C) 130.32 D) 145 E) 121.3 Answer: C Diff: 3 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

8) You are conducting a study on the blood glucose levels of 9 patients who are on strict diets and exercise routines. To monitor the mean and range of the blood glucose levels of your patients, you take a blood glucose reading every day for each patient for 20 days. The results are shown below. Day 1 2 3 4 5 6 7 8 9 10

Mean 92.11 90.67 89.44 93.33 89.44 110.20 104.10 108.20 107.70 112.70

Range 80 77 73 81 71 90 120 93 98 145

Day 11 12 13 14 15 16 17 18 19 20

Mean 100.70 96.67 97.33 95.89 98.89 101.22 107.33 108.00 106.89 109.89

Range 89 77 75 80 68 88 81 95 77 80

The lower control limit for your chart is . A) 89.44 B) 92.11 C) 73 D) 71.75 E) 60.5 Answer: D Diff: 3 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts.

85) You are conducting a study on the blood glucose levels of 9 patients who are on strict diets and exercise routines. To monitor the mean and range of the blood glucose levels of your patients, you take a blood glucose reading every day for each patient for 20 days. The results are shown below. The mean of the 9 sample means is 101.03, and the mean of the 9 ranges is 86.9. The results are shown below. Day 1 2 3 4 5 6 7 8 9 10

Mean 92.11 90.67 89.44 93.33 89.44 110.20 104.10 108.20 107.70 112.70

Range 80 77 73 81 71 90 120 93 98 145

Day 11 12 13 14 15 16 17 18 19 20

Mean 100.70 96.67 97.33 95.89 98.89 101.22 107.33 108.00 106.89 109.89

Range 89 77 75 80 68 88 81 95 77 80

From the chart we conclude that . A) the glucose level is not in control for the nine patients on the diet and exercise program B) the glucose level means over the 20-day period fall within the control limits for the nine patients on the diet and exercise program C) exactly one mean is out of the control limits D) some of the patients are not on a strict diet and exercise routine E) exactly two patients are out of the control limits Answer: B Diff: 3 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 86) You are conducting a study on the blood glucose levels of 9 patients who are on strict diets and exercise routines. To monitor the mean and range of the blood glucose levels of your patients, you take a blood glucose reading every day for each patient for 20 days. The mean of the 9 sample means is 101.03, and the mean of the 9 standard deviations is 29.243. The lower control limit for your chart is . A) 70.85 B) 92.11 C) 73 D) 71.75 E) 60.5 Answer: A Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. 29

87) Reengineering . A) calls for continuous improvement B) aims to improve quality considering the present limitations or constraints of the company C) involves the suboptimization of functional units with the goal to optimize the productive process as a whole D) involves determining what the company would be like if it could start from scratch E) involves exclusively a top-down approach Answer: D Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. Bloom's level: Application 88) Six sigma . A) calls for continuous improvement B) only applies to the manufacturing industries C) involves determining what the company would be like if it could start from scratch D) involves long-term quality-improvement projects E) is aimed at achieving defect-free processes Answer: E Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. Bloom's level: Application

89) Six sigma . A) requires that ±3σ (a total width of 6σ) of the product be within specification B) requires that 99.5% of the product be within specification C) contains a formalized problem-solving approach called Bayesian approach D) contains a formalized problem-solving called the DMAIC process E) involves training only production upper- and middle-level managers Answer: D Diff: 2 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. Bloom's level: Application 90) Lean manufacturing doesn't focus on which of the following wastes? A) Transportation B) Inventory C) Excessive training D) Overproduction E) Processing Answer: C Diff: 2 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. Bloom's level: Application 91) Which of the following is not one of Deming's 14 points? A) Break down barriers between staff areas B) Adjust numerical quotas C) Institute training D) Eliminate slogans E) Institute leadership Answer: B Diff: 2 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. Bloom's level: Application 31

92) Which of the following is not true or accurate about Poka-Yoke? A) It's always prevention-based B) Assumes that causes of defects lie in worker errors C) It's used in continuous improvement D) It means "mistake proofing" E) It was developed by a Japanese industrial engineer Answer: A Diff: 1 Response: See section 18.1 Introduction to Quality Control Learning Objective: 18.1: Explain the meaning of quality in business, compare the approaches to quality improvement by various quality gurus and movements, and compare different approaches to controlling the quality of a product, including benchmarking, just-in-time inventory systems, Six Sigma, lean manufacturing, reengineering, poka-yoke, Value Stream Mapping, and Kaizen Event. Bloom's level: Application 93) A flowchart does not include . A) decision points B) time required for each activity C) start point D) a flowline E) activities Answer: B Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. Bloom's level: Application

94) The flowchart symbol for a decision point is A)

D) E)

Answer: C Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. Bloom's level: Application

95) The flowchart start/stop symbol is A)

Answer: E Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. Bloom's level: Application

96) A fishbone diagram is a . A) quantitative tallying of the numbers and types of defects B) schematic representation of all the activities and interactions that occur in a process C) cause-and-effect diagram D) graphical method for evaluating whether a process is or is not in a state of statistical control E) type of checklist Answer: C Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. Bloom's level: Application 97) The most appropriate graphical tool to identify the potential root causes of an error in a production process is a _ . A) check list B) Pareto chart C) fishbone diagram D) point-and-figure chart E) control chart Answer: C Diff: 1 Response: See section 18.2 Process Analysis Learning Objective: 18.2: Compare various tools that identify, categorize, and solve problems in the quality improvement process, including flowcharts, Pareto analysis, cause-and-effect diagrams, control charts, check sheets, histograms, and scatter charts. Bloom's level: Application 98) For a p chart, UCL = 0.64 and LCL = 0.32. Then p = . A) There is not enough information to determine p B) 0.97 C) 0.96 D) 0.48 E) 0.32 Answer: D Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. Bloom's level: Application

99) For a p chart, UCL = 0.64 and LCL = 0.32. Then q = . A) There is not enough information to determine q B) 0.03 C) 0.04 D) 0.52 E) 0.68 Answer: D Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. Bloom's level: Application 100) For a p chart, UCL = 0.64 and LCL = 0.32. Then the overall percentage of compliant items is %. A) There is not enough information to determine the percentage. B) 32 C) 48 D) 52 E) 96 Answer: D Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. Bloom's level: Application 101) For a p chart, UCL = 0.64 and LCL = 0.32. The standard deviation of the proportions is . A) There is not enough information to determine the standard deviation of the proportions B) 0.32 C) 0.16 D) 0.14 E) 0.05 Answer: E Diff: 1 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. Bloom's level: Application

102) For a p chart, the standard deviation of the proportions is 0.079057. The number of items in each sample is 30. Then p is . A) There is not enough information to determine p B) 0.25 C) 0.33 D) 0.45 E) 0.48 Answer: B Diff: 2 Response: See section 18.3 Control Charts Learning Objective: 18.3: Measure variation among manufactured items using various control charts, including x̄ charts, R charts, p charts, and c charts. Bloom's level: Application

Business Statistics, 11e (Black) Chapter 19 Decision Analysis 1) In a decision analysis problem, variables (such as general macroeconomic conditions) which are not under the decision maker's control are called prior probabilities. Answer: FALSE Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 2) In a decision analysis problem, variables (such as investing in common stocks or corporate bonds) which are under the decision maker's control are called decision alternatives. Answer: TRUE Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 3) In a decision analysis problem, variables (such as benefits or rewards that result from investments in common stocks or corporate bonds and from a new product launch) which result from selecting a particular decision alternative are called posterior probabilities. Answer: FALSE Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 4) In a decision-making scenario, if the decision maker knows which state of nature will occur, the scenario is called decision-making under certainty. Answer: TRUE Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 5) In a decision-making scenario, if it is not known which state of nature will occur and further if the probabilities of occurrence of the states are also unknown, the scenario is called decisionmaking under double risk. Answer: FALSE Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and inimax regret.

6) In a decision-making under uncertainty scenario, the decision maker chooses the decision alternative that has the minimum expected (i.e., probability-weighted) payoff among all the available alternatives. Answer: FALSE Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 7) In a decision-making under uncertainty scenario, the decision maker attempts to develop a strategy based on payoffs since virtually no information is available about which state of nature will occur. Answer: TRUE Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 8) In a decision-making under uncertainty scenario, the best decision alternative based on the strategy of minmax regret will always have zero regret. Answer: FALSE Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 9) In a decision-making under uncertainty scenario using the strategy of minimax regret, all the entries in the opportunity loss table must be zero or positive. Answer: TRUE Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 10) In a decision-making scenario, if it is not known which of the states of nature will occur but the probabilities of occurrence of the states are known the scenario is called decision-making under risk. Answer: TRUE Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility.

11) In a decision-making under risk scenario, the expected monetary value of a decision alternative is the arithmetic average of the payoffs to the decision alternative in each state of the nature. Answer: FALSE Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 12) In a decision-making under risk scenario, the expected monetary value of a decision alternative is the weighted average (using the probability of each state of nature as the weight) of the payoffs to the decision alternative in each state of nature. Answer: TRUE Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 13) In decision-making under risk, the expected monetary value without information is the largest of the expected monetary values for the various decision alternatives. Answer: TRUE Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 14) In decision-making under risk, the expected monetary payoff of perfect information is the weighted average of the best payoff for each state of nature (using the probability of the state of nature as the weight). Answer: TRUE Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 15) The expected monetary payoff of perfect information is the value of perfect information. Answer: FALSE Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility.

16) The value of perfect information is the difference between the monetary payoff with perfect information and the expected monetary payoff with no information. Answer: TRUE Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 17) A risk-avoider decision maker will bail out of a risky scenario only if the compensation to bail out is more than the expected monetary payoff from the risky scenario. Answer: FALSE Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 18) A risk-taker decision maker will bail out of risky scenario only if the compensation to bail out is more than the expected monetary payoff from the risky scenario. Answer: TRUE Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 19) The concept of utility can be helpful to the decision analysis of situations which do not lend themselves to expected monetary value analysis. Answer: TRUE Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 20) The expected value of sample information is the ratio of the expected monetary value with information to the expected monetary value without information. Answer: FALSE Diff: 1 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 21) The expected value of sample information is the difference between the expected monetary value with information to the expected monetary value without information. Answer: TRUE Diff: 1 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 4

22) When deciding whether to purchase sample information, the revised probabilities due to that information can be incorporated through the application of Bayes' rule. Answer: TRUE Diff: 2 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 23) If there is a 60% chance that the economy will grow and a 40% chance that it will not, then an investor might expect to make $75 on an investment or -$10 on that same investment, respectively for the state of the economy. In this situation, their expected return would be $49. Answer: FALSE Diff: 1 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 24) If there is a 70% chance that the economy will grow and a 30% chance that it will not, then an investor might expect to make $90 on an investment or $25 on that same investment, respectively for the state of the economy. In this situation, their expected return would be $70.50. Answer: TRUE Diff: 1 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 25) In a decision analysis problem, variables (such as investing in common stocks or corporate bonds) which are under the decision maker's control are called . A) payoffs B) decision alternatives C) states of nature D) revised probabilities E) prior probabilities Answer: B Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table.

26) Dianna Ivy is evaluating a plan to expand the production facilities of International Compressors Company which manufactures natural gas compressors. Dianna feels that the price of coal is a significant factor in her decision, but she cannot control it. For her decision, the different prices of coal represent the _. A) payoffs B) decision alternatives C) states of nature D) revised probabilities E) prior probabilities Answer: C Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 27) Dianna Ivy is evaluating a plan to expand the production facilities of International Compressors Company which manufactures natural gas compressors. Dianna feels that the price of coal is a significant factor in her decision, but she cannot control it. She is able to estimate how much the company would make under various prices of coal. Her estimates would represent the . A) payoffs B) decision alternatives C) states of nature D) revised probabilities E) prior probabilities Answer: A Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 28) Dianna Ivy is evaluating a plan to expand the production facilities of International Compressors Company which manufactures natural gas compressors. Dianna feels that the price of coal is a significant factor in her decision. She is able to estimate how much the company would make under various prices of coal. If Dianna is making her decision under certainty, then she knows the . A) profit possible under various prices of coal B) whether the company will expand its production facilities C) future price of coal D) what decisions will be made at each point E) future state of the economy Answer: C Diff: 2 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table.

29) Dianna Ivy is evaluating a plan to expand the product ion facilities of International Compressors Company which manufactures natural gas compressors. Dianna feels that the price of coal is a significant factor in her decision. Below are her estimates of payoffs from various expansion plans under different prices of coal. If Diana knows the price of coal in the future will be the same as it is today, what expansion plan should she select? PAYOFFS ($mil) Large expansion Medium expansion Small expansion No expansion

Lower -$500 -$300 -$50 $0

Same -$100 $150 $100 $75

Higher $1,000 $800 $400 $300

A) No expansion B) Small expansion C) Medium expansion D) Cannot determine from information E) Large expansion Answer: C Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 30) Dianna Ivy is evaluating a plan to expand the production facilities of International Compressors Company which manufactures natural gas compressors. Dianna feels that the price of coal is a significant factor in her decision. Below are her estimates of payoffs from various expansion plans under different prices of coal. If Diana knows the price of coal in the future will be higher, what expansion plan should she select? PAYOFFS ($mil) Large expansion Medium expansion Small expansion No expansion

Lower -$500 -$300 -$50 $0

Same -$100 $150 $100 $75

Higher $1,000 $800 $400 $300

A) No expansion B) Small expansion C) Medium expansion D) Cannot determine from information E) Large expansion Answer: E Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table.

31) A CEO is looking to determine how much profit the company can make if they purchase one of their competitors. The decision of which competitor to choose is a since the CEO . A) payoff; will be tracking the profit from that decision B) certainty; has control over that decision C) state of nature; has no control over that decision D) decision under uncertainty; has not decided E) decision alternative; has no control over that decision Answer: B Diff: 2 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 32) A CEO is looking to determine how much profit the company can make if they purchase one of their competitors. Key to the decision is how much profit each competitor is likely to make given different levels of future demand in their market. In this situation, the levels of future demand in the market would be considered the . A) payoffs B) decisions under certainty C) decision alternatives D) states of nature E) profits Answer: D Diff: 2 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 33) A CEO is looking to determine how much profit the company can make if they purchase one of their competitors. Key to the decision is how much profit each competitor is likely to make given different levels of future demand in their market. In this situation, the profits of each competitor would be considered the . A) payoffs B) decisions under certainty C) decision alternatives D) states of nature E) options Answer: A Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table.

34) A CEO is looking to determine how much profit the company can make if they purchase one of their competitors. Key to the decision is how much profit each competitor is likely to make given different levels of future demand in their market. Estimates of the profits for each competitor that could be purchased are estimated in the table below based on demand. If the CEO knows that demand will significantly decrease, which competitor should the company purchase?

PAYOFFS ($mil) Competitor A Competitor B Competitor C Competitor D

Significant decrease $50 $25 $15 $55

Small decrease $100 $75 $80 $90

Significant Small increase increase $270 $1,450 $350 $1,330 $300 $1,700 $335 $1,500

A) Competitor A B) Competitor B C) Cannot determine due to uncertainty D) Competitor C E) Competitor D Answer: E Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 35) A CEO is looking to determine how much profit the company can make if they purchase one of their competitors. Key to the decision is how much profit each competitor is likely to make given different levels of future demand in their market. Estimates of the profits for each competitor that could be purchased are estimated in the table below based on demand. If the CEO knows that demand will experience a small increase, which competitor should the company purchase?

PAYOFFS ($mil) Competitor A Competitor B Competitor C Competitor D

Significant decrease $50 $25 $15 $55

Small decrease $100 $75 $80 $90

Small increase $270 $350 $300 $335

Significant increase $1,450 $1,330 $1,700 $1,500

A) Competitor A B) Competitor B C) Cannot determine due to uncertainty D) Competitor C E) Competitor D Answer: B Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 9

36) A CEO is looking to determine how much profit the company can make if they purchase one of their competitors. Key to the decision is how much profit each competitor is likely to make given different levels of future demand in their market. Estimates of the profits for each competitor that could be purchased are estimated in the table below based on demand. If the CEO is unsure about future demand, which competitor should the company purchase?

PAYOFFS ($mil) Competitor A Competitor B Competitor C Competitor D

Significant decrease $50 $25 $15 $55

Small decrease $100 $75 $80 $90

Small increase $270 $350 $300 $335

Significant increase $1,450 $1,330 $1,700 $1,500

A) Competitor A B) Competitor B C) Cannot determine due to uncertainty D) Competitor C E) Competitor D Answer: C Diff: 2 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 37) In decision tables, what is shown along the top of the table as the column headings? A) States of nature B) Decisions under certainty C) Profits D) Decision alternatives E) Payoffs Answer: A Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 38) In decision tables, what is shown along the left side of the table as the row headings? A) States of nature B) Decisions under certainty C) Profits D) Decision alternatives E) Payoffs Answer: D Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table.

39) If management is making a decision under certainty, the certainty is related to the making the final decision . A) payoff amounts; unimportant B) states of nature; almost trivial C) decision alternatives; depending on the state of nature D) states of nature; important E) payoff amounts: almost trivial Answer: B Diff: 2 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. 40) In decision-making under uncertainty, an optimistic approach is the . A) maximin criterion B) maximax criterion C) Hurwicz criterion D) minimax regret strategy E) maximin regret strategy Answer: B Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 41) In decision-making under uncertainty, a pessimistic approach is the . A) maximin criterion B) maximax criterion C) Hurwicz criterion D) minimax regret strategy E) maximin regret strategy Answer: A Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret.

42) In decision-making under uncertainty, the approach that considers only the best and the worst payoffs for each decision alternative is the _ . A) maximin criterion B) maximax criterion C) Hurwicz criterion D) minimax regret strategy E) maximin regret strategy Answer: C Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 43) Dan Hein owns the mineral and drilling rights to a 1,000 acre tract of land. If he drills a well and does not strike oil, his net loss will be $50,000, but if he drills a well and strikes oil, his net gain will be $100,000. If he does not drill, his loss is the cost of the mineral and drilling rights, which amount to $1000. For Dan's decision problem, the variable "drill the well" is one of the . A) payoffs B) decision alternatives C) states of nature D) revised probabilities E) prior probabilities Answer: B Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 44) Dan Hein owns the mineral and drilling rights to a 1,000 acre tract of land. If he drills a well and does not strike oil, his net loss will be $50,000, but if he drills a well and strikes oil, his net gain will be $100,000. If he does not drill, his loss is the cost of the mineral and drilling rights, which amount to $1000. For Dan's decision problem, the variable "net loss of $50,000" is one of the . A) payoffs B) decision alternatives C) states of nature D) revised probabilities E) prior probabilities Answer: A Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret.

45) Dan Hein owns the mineral and drilling rights to a 1,000 acre tract of land. If he drills a well and does not strike oil his net loss will be $50,000, but if he drills a well and strikes oil his net gain will be $100,000. If he does not drill, his loss is the cost of the mineral and drilling rights, which amount to $1000. For Dan's decision problem, the variable "oil in the tract" is one of the . A) payoffs B) decision alternatives C) states of nature D) revised probabilities E) prior probabilities Answer: C Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 46) Dan Hein owns the mineral and drilling rights to a 1,000 acre tract of land. If he drills a well and does not strike oil, his net loss will be $50,000, but if he drills a well and strikes oil, his net gain will be $100,000. If he does not drill, his loss is the cost of the mineral and drilling rights, which amount to $1000. The probability of the state of nature "oil in the tract" is unknown. If Dan is an optimist, he would choose the . A) maximin criterion B) maximax criterion C) Hurwicz criterion D) minimax regret strategy E) maximin regret strategy Answer: B Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 47) Dan Hein owns the mineral and drilling rights to a 1,000 acre tract of land. If he drills a well and does not strike oil his net loss will be $50,000, but if he drills a well and strikes oil his net gain will be $100,000. If he does not drill, his loss is the cost of the mineral and drilling rights, which amount to $1000. The probability of the state of nature "oil in the tract" is unknown. If Dan is a pessimist, he would choose the . A) maximin criterion B) maximax criterion C) Hurwicz criterion D) minimax regret strategy E) maximin regret strategy Answer: A Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 13

48) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 7 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 Using the maximax criterion, the appropriate choice would be . A) d1 B) d2 C) d3 D) d4 E) d5 Answer: A Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 49) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 7 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 Using the maximin criterion, the appropriate choice would be . A) d1 B) d2 C) d3 D) d4 E) d5 Answer: D Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 14

50) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 7 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 Using the Hurwicz criterion with alpha = 0.2, the appropriate choice would be . A) d1 B) d2 C) d3 D) d4 E) d5 Answer: A Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 51) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 7 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 Using the Hurwicz criterion with alpha = 0.1, the appropriate choice would be . A) d1 B) d2 C) d3 D) d4 E) d5 Answer: D Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 15

52) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 7 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 The opportunity loss for the combination "S2" and "d1" is . A) 9 B) 5 C) 3 D) 0 E) -1 Answer: B Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 53) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 7 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 The opportunity loss for the combination "S3" and "d1" is . A) 9 B) 5 C) 3 D) 0 E) -1 Answer: D Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 16

54) Trey Leeman, Operations Manager at National Consumers, Inc. (NCI), is evaluating alternatives for increasing capacity at NCI's Fountain Hill plant. He has identified four alternatives, and has constructed the following payoff table which shows payoffs (in $1,000,000's) for the three possible levels of market demand.

Alternative Lease New Equipment Purchase New Equipment Add Third Shift Do Nothing

Low -0.5 -3 0.5 0

Market Demands Medium High 2 4 0.5 6 0.75 1 0 0

If Trey uses the maximax criterion, the appropriate alternative would be . A) Lease New Equipment B) Purchase New Equipment C) Add Third Shift D) Do Nothing E) Do everything Answer: B Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 55) Trey Leeman, Operations Manager at National Consumers, Inc. (NCI), is evaluating alternatives for increasing capacity at NCI's Fountain Hill plant. He has identified four alternatives, and has constructed the following payoff table which shows payoffs (in $1,000,000's) for the three possible levels of market demand.

Alternative Lease New Equipment Purchase New Equipment Add Third Shift Do Nothing

Low -0.5 -3 0.5 0

Market Demands Medium High 2 4 0.5 6 0.75 1 0 0

If Trey uses the maximin criterion, the appropriate alternative would be . A) Lease New Equipment B) Purchase New Equipment C) Add Third Shift D) Do Nothing E) Do everything Answer: C Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 17

56) Trey Leeman, Operations Manager at National Consumers, Inc. (NCI), is evaluating alternatives for increasing capacity at NCI's Fountain Hill plant. He has identified four alternatives, and has constructed the following payoff table which shows payoffs (in $1,000,000's) for the three possible levels of market demand.

Alternative Lease New Equipment Purchase New Equipment Add Third Shift Do Nothing

Low -0.5 -3 0.5 0

Market Demands Medium High 2 4 0.5 6 0.75 1 0 0

If Trey uses the Hurwicz criterion with alpha = 0.1, the appropriate alternative would be . A) Lease New Equipment B) Purchase New Equipment C) Add Third Shift D) Do Nothing E) Do everything Answer: C Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret.

9) Trey Leeman, Operations Manager at National Consumers, Inc. (NCI), is evaluating alternatives for increasing capacity at NCI's Fountain Hill plant. He has identified four alternatives, and has constructed the following payoff table which shows payoffs (in $1,000,000's) for the three possible levels of market demand. Alternative Lease New Equipment Purchase New Equipment Add Third Shift Do Nothing

Low -0.5 -3 0.5 0

Market Demands Medium High 2 4 0.5 6 0.75 1 0 0

If Trey uses the Hurwicz criterion with alpha = 0.4, the appropriate alternative would be . A) Lease New Equipment B) Purchase New Equipment C) Add Third Shift D) Do Nothing E) Do everything Answer: A Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret.

10) Trey Leeman, Operations Manager at National Consumers, Inc. (NCI), is evaluating alternatives for increasing capacity at NCI's Fountain Hill plant. He has identified four alternatives, and has constructed the following payoff table which shows payoffs (in $1,000,000's) for the three possible levels of market demand. Alternative Lease New Equipment Purchase New Equipment Add Third Shift Do Nothing

Low -0.5 -3 0.5 0

Market Demands Medium High 2 4 0.5 6 0.75 1 0 0

The opportunity loss for the combination "Purchase New Equipment" and "Low" is . A) 0.5 B) 1.5 C) 2.5 D) 3.0 E) 3.5 Answer: E Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 59) Trey Leeman, Operations Manager at National Consumers, Inc. (NCI), is evaluating alternatives for increasing capacity at NCI's Fountain Hill plant. He has identified four alternatives, and has constructed the following payoff table which shows payoffs (in $1,000,000's) for the three possible levels of market demand.

Alternative Lease New Equipment Purchase New Equipment Add Third Shift Do Nothing

Low -0.5 -3 0.5 0

Market Demands Medium High 2 4 0.5 6 0.75 1 0 0

The opportunity loss for the combination "Purchase New Equipment" and "High" is . A) 0.0 B) 0.5 C) 2.5 D) 3.0 E) 3.5 Answer: A Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 20

60) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

If Ray uses the maximax criterion, the appropriate choice would be . A) T-Bills B) Stocks C) Bonds D) Mixture E) None Answer: B Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret.

61) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

If Ray uses the maximin criterion, the appropriate choice would be . A) T-Bills B) Stocks C) Bonds D) Mixture E) None Answer: A Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 62) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

If Ray uses the Hurwicz criterion with alpha = 0.1, the appropriate choice is . A) T-Bills B) Stocks C) Bonds D) Mixture E) None Answer: A Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 22

63) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

If Ray uses the Hurwicz criterion with alpha = 0.5, the appropriate choice is . A) T-Bills B) Stocks C) Bonds D) Mixture E) None Answer: C Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret.

64) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

If Ray uses the Hurwicz criterion with alpha = 0.9, the appropriate choice is . A) T-Bills B) Stocks C) Bonds D) Mixture E) None Answer: B Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 65) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

For the combination of "T-Bills" and "Neutral", the opportunity loss is . A) 0 B) 5 C) 7 D) 8 E) -10 Answer: D Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 24

66) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

For the combination of "Bear" and "Mixture", the opportunity loss is . A) 0 B) 5 C) 13 D) 33 E) -10 Answer: C Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret.

67) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

For the "T-Bills" and "Bonds" choices, the indifference value of Hurwicz's alpha is . A) 0.8267 B) 0.7134 C) 0.6555 D) 0.3333 E) 0.5000 Answer: D Diff: 3 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 68) Ray Crofford is evaluating investment alternatives to invest $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following payoff table which shows expected profits (in $10,000's) for various market conditions.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

For the "Stocks" and "Bonds" choices, the indifference value of Hurwicz's alpha is . A) 0.82 B) 0.71 C) 0.65 D) 0.33 E) 0.50 Answer: A Diff: 3 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. 26

69) Melissa Rossi, Product Manager at National Consumers, Inc. (NCI), is evaluating alternatives for introducing a new package for toothpaste. She has identified four alternative markets, and has constructed the following table which shows NCI's rewards (in $1,000,000's) for various levels of acceptance by the markets and their probabilities. Market Acceptance Market Low (.3) Medium (.4) High (.3) Northeast Only -0.7 0 1 Southeast Only -0.2 0.2 0.8 National -1.5 -0.2 2 None (don't introduce the new package) 0 0 0 If Melissa uses the EMV criterion, the appropriate choice would be . A) Northeast Only B) Southeast Only C) National D) None (don't introduce the new package) Answer: B Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 70) Melissa Rossi, Product Manager at National Consumers, Inc. (NCI), is evaluating alternatives for introducing a new package for toothpaste. She has identified four alternative markets, and has constructed the following table which shows NCI's rewards (in $1,000,000's) for various levels of acceptance by the markets and their probabilities. Market Acceptance Market Low (.3) Medium (.4) High (.3) Northeast Only -0.7 0 1 Southeast Only -0.2 0.2 0.8 National -1.5 -0.2 2 None (don't introduce the new package) 0 0 0 The EMV of introducing the new package in the "Northeast Only" market is . A) $50,000 B) $70,000 C) $90,000 D) $260,000 E) $300,000 Answer: C Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 27

71) Melissa Rossi, Product Manager at National Consumers, Inc. (NCI), is evaluating alternatives for introducing a new package for toothpaste. She has identified four alternative markets, and has constructed the following table which shows NCI's rewards (in $1,000,000's) for various levels of acceptance by the markets and their probabilities. Market Acceptance Market Low (.3) Medium (.4) High (.3) Northeast Only -0.7 0 1 Southeast Only -0.2 0.2 0.8 National -1.5 -0.2 2 None (don't introduce the new package) 0 0 0 The EMV of introducing the new package in the "National" market is . A) $50,000 B) $70,000 C) $90,000 D) $260,000 E) $300,000 Answer: B Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility.

72) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following table which shows expected profits (in $10,000's) for various market conditions and their probabilities. Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

If Ray uses the EMV criterion, the appropriate choice is . A) T-Bills B) Stocks C) Bonds D) Mixture E) Bank CD's Answer: C Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 73) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following table which shows expected profits (in $10,000's) for various market conditions and their probabilities. Market Condition Investment Bull Neutral Bear T-Bills 3 3 3 Stocks 21 11 -30 Bonds 15 4 -3 Mixture 13 6 -10 The EMV of investing in Stocks is . A) $30,000 B) $63,000 C) $78,000 D) $81,000 E) $100,000 Answer: C Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 29

74) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following table which shows expected profits (in $10,000's) for various market conditions and their probabilities. Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

The EMV of investing in Bonds is . A) $30,000 B) $63,000 C) $78,000 D) $81,000 E) $100,000 Answer: D Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 75) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following table which shows expected profits (in $10,000's) for various market conditions and their probabilities.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

The EMV of investing in Mixture is . A) $30,000 B) $63,000 C) $78,000 D) $81,000 E) $100,000 Answer: B Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 30

76) In decision-making under risk, the expected monetary value without information is . A) the weighted average of the best payoff for each state of nature B) the largest of the EMVs for the different decision alternatives C) never smaller than the expected monetary payoff with perfect information D) the average of the EMVs E) half the expected monetary value with information Answer: B Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 77) The expected monetary value without information is $2,500, and the expected monetary payoff with perfect information is $5,000. The expected value of perfect information is . A) $7,500 B) $2,500 C) $1,500 D) $2,000 E) $1,250 Answer: B Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 78) The expected monetary value without information is $60, and the expected monetary payoff with perfect information is $120. The expected value of perfect information is . A) $60 B) $2 C) $180 D) $0.50 E) $120 Answer: A Diff: 1 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility.

79) Melissa Rossi, Product Manager at National Consumers, Inc. (NCI), is evaluating alternatives for introducing a new package for toothpaste. She has identified four alternative markets, and has constructed the following table which shows NCI's rewards (in $1,000,000's) for various levels of acceptance by the markets and their probabilities. Market Acceptance Market Low (.3) Medium (.4) High (.3) Northeast Only -0.7 0 1 Southeast Only -0.2 0.2 0.8 National -1.5 -0.2 2 None (don't introduce the new package) 0 0 0 The expected monetary payoff with perfect information is . A) $570,000 B) $680,000 C) $760,000 D) $830,000 E) $980,000 Answer: B Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 80) Melissa Rossi, Product Manager at National Consumers, Inc. (NCI), is evaluating alternatives for introducing a new package for toothpaste. She has identified four alternative markets, and has constructed the following table which shows NCI's rewards (in $1,000,000's) for various levels of acceptance by the markets and their probabilities. Market Acceptance Market Low (.3) Medium (.4) High (.3) Northeast Only -0.7 0 1 Southeast Only -0.2 0.2 0.8 National -1.5 -0.2 2 None (don't introduce the new package) 0 0 0 The expected value of perfect information is . A) $420,000 B) $570,000 C) $660,000 D) $720,000 E) $890,000 Answer: A Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 32

81) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following table which shows expected profits (in $10,000's) for various market conditions and their probabilities.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

The expected monetary payoff with perfect information is . A) $128,000 B) $137,000 C) $144,000 D) $151,000 E) $127,000 Answer: C Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility.

82) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified four alternatives and constructed the following table which shows expected profits (in $10,000's) for various market conditions and their probabilities.

Investment T-Bills Stocks Bonds Mixture

Market Condition Bull Neutral Bear 3 3 3 21 11 -30 15 4 -3 13 6 -10

The expected value of perfect information is . A) $57,000 B) $63,000 C) $79,000 D) $82,000 E) $87,000 Answer: B Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 83) Frank Forgione has the right to enter a contest where he has a 50% chance of winning $50,000 and a 50% chance of losing $0. It costs Frank nothing to enter the contest. If he is willing to give up his right to enter the contest for a sure payment of $10,000, he is . A) a risk avoider B) an optimist C) a risk taker D) risk neutral (an EMV'er) E) a gambler Answer: A Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility.

84) Frank Forgione has the right to enter a contest where he has a 50% chance of winning $50,000 and a 50% chance of losing $0. It costs Frank nothing to enter the contest. If he is willing to give up his right to enter the contest for a sure payment of $25,000, he is . A) a risk avoider B) an optimist C) a risk taker D) risk neutral (an EMV'er) E) a gambler Answer: D Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. 85) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets.

The probability that the advisor predicts a Bull market and the Bull market is the actual condition p(F1ᴖS1) is . A) 0.78 B) 0.9 C) 0.953 D) 0.923 E) 0.72 Answer: E Diff: 1 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

86) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets.

The probability that the advisor predicts a Bull market, P (F1), is . A) 0.78 B) 0.894 C) 0.953 D) 0.923 E) 1.000 Answer: A Diff: 1 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

87) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets

If the advisor predicts a Bull market the revised probability of a Bull market, P (S1|F1), is . A) 0.877 B) 0.894 C) 0.953 D) 0.923 E) 1.000 Answer: D Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

88) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets.

If the advisor predicts a Bear market the revised probability of a Bear market, P (S2|F2), is . A) 0.524 B) 0.636 C) 0.784 D) 0.812 E) 0.000 Answer: B Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

89) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets

If the advisor predicts a Bull market the EMV of the Bonds alternative, using revised probabilities, is closest to . A) $85,240 B) $25,710 C) $108,450 D) $75,480 E) $90,000 Answer: C Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

90) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets.

If the advisor predicts a Bull market the EMV of the Stocks alternative, using revised probabilities, is closest to . A) $168,900 B) $207,650 C) $157,300 D) $306,000 E) $134,650 Answer: B Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

91) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets.

If the advisor predicts a Bear market the EMV of the Bonds alternative, using revised probabilities, is closest to . A) $36,600 B) $24,600 C) $56,800 D) $48,200 E) $45,800 Answer: B Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

4 91

92) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets.

If the advisor predicts a Bear market the EMV of the Stocks alternative, using revised probabilities, is closest to . A) $132,300 B) -$73,900 C) $127,600 D) -$99,800 E) $105,000 Answer: D Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

4 92

93) Ray Crofford is evaluating investment alternatives for the $100,000 which he inherited from his grandfather. His investment advisor has identified two alternatives and constructed the following tables which show (1) expected profits (in $10,000's) for various market conditions and their probabilities, and (2) the advisor's track record on predicting Bull and Bear markets

The EMV of this investment opportunity with the advisor's prediction is closest to . A) $167,379 B) $174,200 C) $153,900 D) $136,700 E) $140,011 Answer: A Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 94) A particular electronic component is produced at two plants for an electronics manufacturer. Plant A produces 70% of the components used and the remainder are produced by plant B. The probability that a component is defective is 0.02 if it is produced at plant A and 0.01 if it is produced at plant B. The probability that the component is defective is _ . A) 0.3 B) 0.01 C) 0.003 D) 0.176 E) 0.017 Answer: E Diff: 2 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information.

4 93

95) A particular electronic component is produced at two plants for an electronics manufacturer. Plant A produces 70% of the components used and the remainder are produced by plant B. The probability that a component is defective is 0.02 if it is produced at plant A and 0.01 if it is produced at plant B. If the component is defective the revised probability it is produced at plant B, P (B|D), is closest to _ . A) 0.3 B) 0.01 C) 0.003 D) 0.176 E) 0.017 Answer: D Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 96) A particular electronic component is produced at two plants for an electronics manufacturer. Plant A produces 70% of the components used and the remainder are produced by plant B. The probability that a component is defective is 0.02 if it is produced at plant A and 0.01 if it is produced at plant B. If the component is not defective the revised probability it is produced at plant A, P (A|ND), is closest to . A) 0.700 B) 0.824 C) 0.176 D) 0.300 E) 0.698 Answer: E Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 97) A particular electronic component is produced at two plants for an electronics manufacturer. Plant A produces 70% of the components used and the remainder are produced by plant B. The probability that a component is defective is 0.02 if it is produced at plant A and 0.01 if it is produced at plant B. If the component is defective the revised probability it is produced at plant A, P (A|D), is closest to . A) 0.700 B) 0.824 C) 0.176 D) 0.300 E) 0.698 Answer: B Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 4 94

98) With no additional information, an investor expects a monetary value of $2,840 through her investment choices. Additional information on the likelihood of a strong stock market would cost $800. With that additional information, the investor can expect a monetary value of $3,610. The investor purchase the additional information as after paying for the information, the expected monetary value would be . A) should not; -$30 B) should; $800 C) should not; -$800 D) should; 3,610 E) should; $30 Answer: A Diff: 2 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. 99) Which of the following choices is not true about decision theory? A) Decision alternatives for one person can be states of nature for another. B) Payoffs are always positive or zero. C) If you play a card game at a casino, the cards you are dealt are states of nature. D) If you decide to take a MOOC (massive open online course), how much you learn is a payoff. E) In a two-person interaction, it is possible that the sum of the two payoffs is positive. Answer: B Diff: 1 Response: See section 19.1 The Decision Table and Decision-making Under Certainty Learning Objective: 19.1: Make decisions under certainty by constructing a decision table. Bloom's level: Application

4 95

100) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 x 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 If you are using the maximax criterion and decide d1, then x is . A) at least 8 B) more than 8 C) at most 5 D) less than 8 E) at most 2 Answer: D Diff: 1 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. Bloom's level: Application 101) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 y 5 d3 -0.5 0.75 1 d4 0 0 0 d5 x -1 -1 If you are using the maximin criterion and decide d4, then y is . A) at most -1 B) less than -1 C) any value D) cannot be determined without knowing the value of y E) less than -3 Answer: C Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. Bloom's level: Application 4 96

102) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 y 5 d3 -0.5 0.75 1 d4 0 0 0 d5 x -1 -1 If you are using the maximin criterion and decide d4, then y is . A) at most -1 B) less than -1 C) any value D) cannot be determined without knowing the value of y E) less than -3 Answer: C Diff: 2 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. Bloom's level: Application

4 97

103) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 -3 y 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 If you are using Hurwicz criterion with α = 0.3 and decide d2, then y is . A) undetermined; there is not enough information to find the answer B) unfeasible; there is no value of y that would make you choose d2 C) larger than 3.71 D) larger than 8 E) larger than 12.67 Answer: E Diff: 3 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. Bloom's level: Application 104) Consider the following decision table with rewards in $ millions. State of Nature S3 Decision Alternatives S1 S2 d1 -1 2 8 d2 0 6.5 5 d3 -0.5 0.75 1 d4 0 0 0 d5 -1 -1 -1 If you are using Hurwicz and decide d2, then α is . A) undetermined; there is not enough information to find the answer B) unfeasible; there is no value of α that would make you choose d2 C) larger than 0.4 D) less than 0.4 E) less than 0.67 Answer: D Diff: 3 Response: See section 19.2 Decision-making Under Uncertainty Learning Objective: 19.2: Make decisions under uncertainty using the maximax criterion, the maximin criterion, the Hurwicz criterion, and minimax regret. Bloom's level: Application 4 98

105) You are evaluating investment alternatives for a ski resort. There are four alternative investments and their payoffs (in $10,000s) are shown in the following table, depending on the snow conditions for the next season.

Investment d1 d2 d3 d4

Snow Conditions Good Bad 3 1 8 0 12 -4 18 -12

If you use the EMV criterion and you decide investment d2, then the probability that the snow conditions are good is . A) more than 0.5 B) more than 0.17 C) more than 0.25 and less than 0.67 D) less than 0.17 or more than 0.5 E) more than 0.17 and less than 0.5 Answer: E Diff: 3 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. Bloom's level: Application

4 99

106) You are evaluating investment alternatives for a ski resort. There are four alternative investments and their payoffs (in $10,000s) are shown in the following table, depending on the snow conditions for the next season. Investment d1 d2 d3 d4

Snow Conditions Good Bad 3 1 8 0 12 -4 18 -12

If you use the EMV criterion, what is the minimum probability that the conditions will be good for you to decide investment d4? A) 0.4 B) 0.43 C) 0.5 D) 0.57 E) 0.59 Answer: D Diff: 3 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. Bloom's level: Application

107) You are evaluating investment alternatives for a ski resort. There are four alternative investments and their payoffs (in $10,000s) are shown in the following table, depending on the snow conditions for the next season. Investment d1 d2 d3 d4

Snow Conditions Good Bad 3 1 8 0 12 -4 18 -12

If you use the EMV criterion, and the probability that the snow conditions are good is p, what is the expected monetary payoff with perfect information? A) It cannot be determined without a numeric value for the probability p. B) 18p - 12 C) 17p - 1 D) 18p - 1 E) 17p + 1 Answer: E Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. Bloom's level: Application

5 107

108) You are evaluating investment alternatives for a ski resort. There are four alternative investments and their payoffs (in $10,000s) are shown in the following table, depending on the snow conditions for the next season. Investment d1 d2 d3 d4

Snow Conditions Good Bad 3 1 8 0 12 -4 18 -12

If you use the EMV criterion, the probability that the snow conditions are good is p, and you decide investment d3, what is the expected value of perfect information? A) 12p + 3 B) 2p + 3 C) p + 5 D) p + 2 E) p + 1 Answer: C Diff: 2 Response: See section 19.3 Decision-making Under Risk Learning Objective: 19.3: Make decisions under risk by constructing decision trees, calculating expected monetary value and expected value of perfect information, and analyzing utility. Bloom's level: Application 109) A random person is selected from a large population in which 5% are users of a dangerous illegal drug. A drug test that correctly identifies users 99% of the times and nonusers 95% of the time is administered to this individual and gives a positive result. What is the probability that this individual is actually a user of this drug? A) 0.99 B) 0.94 C) 0.61 D) 0.53 E) 0.51 Answer: E Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. Bloom's level: Application

5 108

110) A random person is selected from a large population in which 5% are users of a dangerous illegal drug. A drug test that correctly identifies users 99% of the times and nonusers 95% of the time (this percentage is called "specificity" of the test) is administered to this individual and gives a positive result. What specificity would actually be required to conclude that the probability that this individual is actually a user of this drug is 0.75? A) 0.965 B) 0.973 C) 0.978 D) 0.983 E) 0.985 Answer: D Diff: 3 Response: See section 19.4 Revising Probabilities in Light of Sample Information Learning Objective: 19.4: Revise probabilities in light of sample information by using Bayesian analysis and calculating the expected value of sample information. Bloom's level: Application

5 109