IUBAT窶的NTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY Course Outline Part A
Course Number: STA 240
College : College of Arts and Sciences
Program: Bachelor of Science Major: STATISTICS
Course Name : Statistics
Hours/Week: 3 Lecture : 3
Total Hours: 48
Total Week: 16
Credits: 3
Course Goals At the end of the course the students are expected to learn: a)
b) c)
The basic concept of statistics and statistical methods. The methods of collection and presentation of data, the basic concepts of frequency distribution, central tendency, dispersion, estimations, appropriate tests etc. On the basis of that simple statistics how to draw inference and make conclusions. Moreover, they will be able to handle any survey or enquiry or investigation or research in their respective field and from the collected data they will be able to generate informations and presenting the informations in scientific way to produce or write a sensible report.
Course Description: The course is designed to introduce to the students the basic concept and tools of statistics and enable them to relate these to real life problems. Topics include probability concepts and laws, sample spaces, random variables (discrete and continuous); binomial, poisson, uniform, normal, exponential; two-dimensional variates, expected values. Collection, processing, organization and presentation of data, frequency distribution, measure of central tendency and dispersion, confidence limits, estimation and hypothesis testing, regression, correlation, chi square and non-parametic statistics; time series. Type and source of published statistics in Bangladesh.
Evaluation 1. First Term Exam 2. Mid-term Exam 3. Quizzes 4. Assingments
5. Attendance 6. Final Term Exam (Covering the entire course) Total
20% 20% 10% 10% 5% 35% 100%
Course Outcomes and Sub-Outcomes Understand why we study statistics, organize data represent and those in a simple way, understand probability and its use in decision making, understand why a sample is often the only feasible way to learn something about a population, learn tests of hypothesis to face real life situation and familiarize one self with forecasting method.
Prior Learning Assessment Methods Assessment methods include first-term, mid-term and final examination. There will also be announced and unannounced quizzes. Moreover, the course instructor will give assignments when he finds it appropriate.
Developed by
Professor Md. Amanullah Date: 07/05/2011
Instructor Name and Department (Signature):
Md. Mortuza Ahmmed Faculty, Department of Statistics College of Arts and Science
IUBAT–INTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY Course Outline Part B College of : College of Arts and Sciences Program: Bachelor of Science
Major: STATISTICS
Instructor : Md. Mortuza Ahmmed
Room No:322 Phone:01819178019
E-mail: bablu3034@gmail.com
Office Hrs: 8:30 AM - 5:00 PM. at IUBAT Campus (in Schedule date) Councelling Hours: Sunday-Wednesday 10:30AM-12:30PM
Text(s) and Equipment Prem S. Mann, Introductory Statistics Douglas, William and Samuel, Statistical Techniques in Business & Economics McGraw-Hill, 2005 Paul Newbold, W. L. Carlson Thorne (5th Edition), Statistics for Business and Economics Anderson and Sweeney, Statistics for Business and Economics (6th Edition) M.G Mostofa, Introduction to Mathematical Statistics, S. P. Gupta and M.P. Gupta Business Statistics (Latest Edition).
Course Notes (Policies and Procedures) All the definition and theories will be clearly explained in the class lectures and relating problems will be solved. Students must collect these through class notes by regular attendance. Queries will be solved in the class and the task on relative chapters will be delivered during class lectures. All home works will be checked and discussed with the students. Some class tests will be setup to prepare the students for the examination.
Assignment Details Assigment(s) will be provided in the class.
IUBAT窶的NTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY College of Arts amd Sciences (CAAS) Summer Semester -2011 Program: Bachelor of Science Major: STATISTICS Day
Outcome/MaterialCovered
Day 1
Introduction to statistics: scope of statistics
Reference Reading Douglas/ M.G Mostofa/
Gupta
Day 2
Statistics and Related Terms: Definitions and Examples
Douglas/ M.G Mostofa/ Gupta
Day 3
Data Collection and Data Representation:Tabular Representation
Douglas/ M.G Mostofa/ Gupta
Cont.
Douglas/ M.G Mostofa/ Gupta
Data Representation: Graphical representation of Data
Douglas/ M.G Mostofa/ Gupta
Cont.
Douglas/ M.G Mostofa/ Gupta
Descriptive Statistics: Descriptive summary measure, Measures of Central tendency.
Douglas/ M.G Mostofa/ Gupta
Mean, Median, Mode, GM, HM
Douglas/ M.G Mostofa/ Gupta
Day 9
Practical uses of Mean, Median, Mode, GM, HM.
Douglas/ M.G Mostofa/ Gupta
Day 10
Absolute and relative Measures of
Douglas/ M.G Mostofa/ Gupta
Day 11
Uses of absolute and relative Measures of Dispersion.
Douglas/ M.G Mostofa/ Gupta
Day 12
Skewness and Kurtosis, Moments and Descriptive Statistics.
Douglas/ M.G Mostofa/ Gupta
Day 13
Review Simple Correlation: Types of
Day 14
relationships, Scatter diagram,
Day 4 Day 5 Day 6
Day 7
Day 8
Dispersion.
Coefficient of correlation, Co-efficient
Assignment
Douglas/ M.G Mostofa/ Gupta
of determination. Day 15
Properties of correlation. Uses and misuses or abuses of correlation.
Day 16
Interpretation of findings associated with correlation.
Douglas/ M.G Mostofa/ Gupta Douglas/ M.G Mostofa/ Gupta
First term examination begins from Jun-3 and must end by Jun 10, 2011 Day 17
Simple Regression analysis. Estmation
Douglas/ M.G
Due Date
of Coefficient of regression, Drawing the regression line and Co-efficient of determination. Day 18
Day 19
Properties of regression. Uses and misuses or abuses of regression. Interpretation of findings associated with regression.
Mostofa/ Gupta
Douglas/ M.G Mostofa/
Gupta
Douglas/ M.G Mostofa/
Gupta
Day 20
Introduction to Probability, classical, empirical, and subjective approaches to Probability.
Douglas/ M.G Mostofa/ Gupta
Day 21
Conditional probability and joint probability. Some rules for calculating probabilities.
Douglas/ M.G Mostofa/ Gupta
Day 22
Application of a tree diagram to organize and compute probabilities.
Day 23
Discrite Probability Distributions and
Douglas/ M.G Mostofa/ Gupta Douglas/ M.G
its some of the properties.
Mostofa/ Gupta
Practical examples of Discrite Probability Distribution.
Mostofa/ Gupta
Day 24
Day 25
Day 26 Day 27 Day 28
Day 29
Day 30
Day 31 Day 32
Continuous Probability Distributions and its some of the properties. Practical examples of Continuous Probability Distribution.
Douglas/ M.G Douglas/ M.G Mostofa/
Gupta
Douglas/ M.G Mostofa/
Gupta
Review Sampling Methods and Central limit Theorem Defination of Hypothesis, Null Hypothesis, Alternative Hypothesis, Procedure for Tesing Hypothesis. One-tail Test, Two-tail Test, Type one Error, Type Two Error and Power of the Test.
Douglas/ M.G Mostofa/
Gupta
Douglas/ M.G Mostofa/
Gupta
Douglas/ M.G Mostofa/
Gupta
Douglas/ M.G Hypothesis testing, Z-test and t-test. Hypothesis testing, F- test and χ2-test.
Mostofa/ Gupta Douglas/ M.G Mostofa/ Gupta
Mid Term Examination begins from July 03 and must end by July 11, 2011. Day 33
Simple Index Numbers, Construction of Index Numbers
Douglas/ M.G Mostofa/ Gupta
Day 34
Unweighted Indexes: Simple Average of the Price Index, Simple Aggregate Index
Douglas/ M.G Mostofa/ Gupta
Weighted Indexes: Laspeyres Price Index, Paasche Price Index and Fishers’s Price Index.
Douglas/ M.G Mostofa/ Gupta
Value Index and Consumer Price Index.
Douglas/ M.G Mostofa/ Gupta
Day 35
Day 36
Day 37
Day 38
Day 39 Day 40 Day 41 Day 42
Introduction to Time series and Forecasting
Douglas Laurence
Components of a Time Series: Secular Trend, Cyclical Variation, Seasonal Variation, Irregular Variation.
Douglas Laurence
A Moving Average, Weighted Moving Average.
Douglas Laurence
Linear Trend and Forecasting.
Douglas Laurence
Practical examples of Time series and Forecasting.
Douglas Laurence
Review
Final Examination as per scheduled declared by Registry.