SOLUTIONS MANUAL
SOLUTIONS MANUAL
Solution Manual for Data and Analytics in Accounting An Integrated Approach 1e Ann C. Dzuranin, Guido Geerts, Margarita Lenk
CHAPTER 1 DATA AND ANALYTICS IN THE ACCOUNTING PROFESSION Learning Objectives: LO 1.1: Summarize how advances in data and technology are impacting accounting professionals. LO 1.2: Describe the stages of the data analysis process. LO 1.3: Identify the skills necessary to perform data analysis. LO 1.4: Explain how to apply a data analytics mindset during the data analysis process.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. A LO 1.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
2. C LO 1.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
3. C
LO 1.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
7. D LO 1.2, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
8. C LO 1.2, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
LO 1.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
4. B LO 1 2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
5. B LO 1.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
6. C
9. A LO 1.3, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
10.D LO 1.3, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
11.B
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LO 1.3, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
12.A LO 1.4, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
13.D
14.A LO 1.4, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
15.B LO 1.4, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
LO 1.4, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
ANSWERS TO REVIEW QUESTIONS 1. Both the CPA exam and the CMA exam have added data analytic content to their exams. This is in response to what new professionals need to know as they enter the accounting profession. The CPA Exam Evolution is a strong indication of how the accounting profession is changing. The new CPA exam will have more technology and data analytics questions in the Core exam, as well as the Discipline exams. LO 1.1, BT: K, Difficulty: Easy, TOT: 6 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
2. Changes 1. Ability to use entire data sets to identify exceptions, anomalies, and outliers 2. Automation of manual processes 3. Automation of journal entries 4. Risk identification 5. Forecasting 6. Compliance reporting
Accounting Practice Area ANS: a. Auditing, c. Managerial accounting ANS: a., b., c., d. (All areas) ANS: b. Financial accounting ANS:. a. Auditing, c. Managerial accounting ANS: b. Financial accounting, c. Managerial accounting ANS: d. Tax accounting
LO 1.1, BT: C, Difficulty: Medium, TOT: 8 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
3. Data are raw facts and figures. Technology helps covert that data into information. Information is the knowledge gained from analyzing the data. LO 1.1, BT: C, Difficulty: Medium, TOT: 6 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
4. Purpose 1. Understanding what is happening currently and what has happened in the past.
Method ANS: a. Descriptive
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2. Understanding what should happen to meet our goals and objectives.
ANS: d. Prescriptive
3. Understanding what might happen in the future. 4. Understanding why something happened.
ANS: c. Prescriptive ANS: b. Diagnostic
LO 1.2, BT: C, Difficulty: Medium, TOT: 6 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
5. The first step is to identify the motivation for the analysis. The motivation for a project is the why the analysis is being performed. It is important to understand the reason for the analysis before beginning the analysis. The second step is to set an objective. In other words, “What is the goal of the analysis?” This step will help articulate the goal and specific questions that will be analyzed. The third step is to develop a strategy. In this step, we determine the data and analysis methods necessary to achieve the project’s goal. LO 1.2, BT: K, Difficulty: Easy, TOT: 8 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
6. Extracting is the process in which data is retrieved from a source. This could involve downloading an Excel file or extracting data from a database or a data warehouse. Transforming the data occurs when data is cleaned, restructured, and/or integrated prior to using it for analysis. Loading data is the process of importing transformed data into the software used to perform analyses. LO 1.2, BT: K, Difficulty: Easy, TOT: 8 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
7. Example 1. Develop a model to calculate contribution margin. 2. Get data from a database. 3. Upload data into analysis software. 4. Determine the objective of the analysis. 5. Create a forecast of net income. 6. Identify the data needed for analysis. 7. Identify relationships within the data. 8. Create a visualization to show the results of the analyses. 9. Identify sales patterns.
Data Analysis Process Stage ANS: b. Analyze ANS: b. Analyze ANS:b. Analyze ANS: a. Plan ANS: b. Analyze ANS: a. Plan ANS: b. Analyze ANS: c. Report ANS: b. Analyze
LO 1.2, BT: C, Difficulty: Medium, TOT: 12 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
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8. A data analytics mindset is the habit critically thinking through the planning, analysis, and interpretation of data results before making and communicating a professional choice or decision. Individuals with a data analytics mindset are inquisitive. They always ask “why” when interpreting results, are open to learning new technologies, and take the time to evaluate their own thinking. LO 1.3, BT: C, Difficulty: Medium, TOT: 6min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
9. The ability to communicate well is important in all areas of accounting. In data analytics it is important to have good communication skills to explain the results of analyses so others can understand and act if needed. LO 1.3, BT: C, Difficulty: Medium, TOT: 6min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
10. Definition 1. Willingness to try new technology. 2. The ability to read, write, and communicate data in context. 3. Ability to create effective data visualizations 4. Disciplined reasoning used to investigate, understand, and evaluate an event, opportunity, or an issue.
Skill ANS: c. Technological agility ANS: b. Data literacy ANS: d. Communication ANS: a. Critical thinking
LO1. 3, BT: K, Difficulty: Easy, TOT: 8min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies.
11.
Stakeholders: Understand the internal and external parties impacted by the data analysis results. Purpose: Determine the reason for the analysis. Alternatives: Evaluate and rank all potential data and analysis choices during the data analysis process. Risks: Consider risks to the data and the analysis choices, including assumptions and potential biases. Knowledge: Identify and acquiring the knowledge necessary to properly prepare and interpret the analyses. Self-reflection: Review decisions and processes for lessons learned and apply them to future projects.
LO 1. 4, BT: C, Difficulty: Medium, TOT: 10min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
12. Student answers will vary. General examples follow.
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Motivation: All elements of critical thinking can be applied. For example, it is important to identify the stakeholders so that their motivations are understood. Understanding those motivations allows a better understanding of why the analysis is being performed. Objective: All elements of critical thinking can be applied. For example, staying focused on the purpose of the analysis will make identifying relevant questions easier. Strategy: All elements of critical thinking can be applied. For example, consider alternatives for data analysis and then choose the best data analysis method.
LO 1.4, BT: AP, Difficulty: Hard, TOT: 16 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
13. Student answers will vary. General examples follow.
Prepare data: All elements of critical thinking can be applied. For example, be aware of risks when preparing data. Risks include incomplete or inaccurate data. Incomplete or inaccurate data will result in incorrect analyses. Build information models: All elements of critical thinking can be applied. For example, it may be necessary to consider alternative models and/or alternative data for our models. By considering alternatives, we can feel more confident that we have chosen the best model. Explore data: All elements of critical thinking can be applied. For example, maintaining focus on the project’s purpose will help avoid wasting time exploring the data for irrelevant questions. Staying focused on the purpose of the analysis makes us more efficient.
LO 1.4, BT: AP, Difficulty: Hard, TOT: 14 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
14. Student answers will vary. General examples follow.
Interpret results: All elements of critical thinking can be applied. For example, it is necessary to identify the knowledge that is needed to understand the analysis before we interpret it. When that knowledge is identified, we are better prepared to interpret the analysis results. Communicate results: All elements of critical thinking can be applied. For example, when preparing to communicate results, it helps to consider the stakeholders who will receive the communication. Understanding the stakeholders let’s us better identifythe audience and structure the presentation of results based on the information they need to know.
LO 1.4, BT: AP, Difficulty: Hard, TOT: 14 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
SOLUTIONS TO BRIEF EXERCISES BE 1.1 1. This analysis can be used to identify sales patterns and to help detect unusual sales. If unusual patterns or sales are identified, the auditor can do further testing. 2. The months of August – November look low compared to prior months. December looks high. It would also be helpful to compare this pattern to the prior year to identify any similarities.
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LO 1.1, BT: AN, Difficulty: Easy, TOT: 8 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, H2: Analytics and Accounting Professional Practice
BE 1.2 1. Total Sales = $2,760,509,187.74 Total Costs = $1,964,399,918.41 Total Profit = $796,109,269.33 2. The data has information from 2022 to 2025. A financial dashboard can include tabs for each of the years, quarters, and months that will allow the user to select the period they are most interested in. The data can be displayed on the dashboard for each of the measures. 3. A visualization showing revenue and expenses can be added. Also, a table with the financial information summary can be added. Data related to assets and liabilities would be useful as well. Finally, a comparison of prior year to current year profit in a visualization would be useful. LO 1.1, BT: AN, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
BE 1.3 1. Descriptive 2. Diagnostic 3. Predictive 4. Descriptive LO 1.2, BT: AP, Difficulty: Easy, TOT: 3 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
BE 1.4 Student answers will vary. 1. Financial data: Forecasted sales of scooters in Canada. Estimated costs for the expansion. Sales and expenses for the U.S. operations to be used to estimate expense for the Canada expansion. Non-financial data: Competitor market share. Population of major cities where scooters are used. Weather data. Foreign exchange rates. 2. The financial data listed is internal data. The non-financial data is external. LO 1.2, BT: C, Difficulty: Medium, TOT: 5 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
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BE 1.5 1. The analysis is being performed to investigate profit margin by customer. 2. The goal is to identify if there are any customers that are below the target profit margin. 3. Perform an analysis that shows profit margin by customer and highlight the customers that are below 42% profit margin. LO 1 2, BT: AP, Difficulty: Medium, TOT: 5 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
BE 1.6 1. It would be easier to see if the products were grouped, and then each bar was a different year. That way it would be easier to identify if a product is increasing or decreasing. However, we can still see that in this visual, just not as easily. Blueberry scones seems to have decreased from 2022 to 2023, and then increased in 2024 and decreased in 2025. All products decreased from 2022 to 2023. All products except cinnamon buns increased in 2024. In 2025, poppyseed bagels increased the most. 2. The interpretation can be communicated orally, in writing, or visually. To communicate visually, create a visual with groupings by product as discussed in answer 1. LO 1.2, BT: AP, Difficulty: Medium, TOT: 5 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
BE 1.7 Guide students to the websites for accounting firms or job posting websites such as Indeed.com or Robert Half Accounting. Student answers will vary. Qualifications: Expert knowledge of general financial accounting and cost accounting. Understanding of and ability to adhere to generally accepted accounting principles. Proficient with accounting software. Excellent organizational skills and attention to detail. Excellent written and verbal communication skills. Proficient in Microsoft Office Suite or similar software. Bachelor’s degree in accounting. Responsibilities:
The accountant/auditor will inspect and review, but may also prepare and report, financial transactions and budgetary controls for assigned funds and departments. Examines assigned financial records and statements for accuracy. Analyzes financial data, making recommendations as appropriate to improve accuracy, efficiency, and to reduce costs. Inspects the budget and general ledger for assigned departments and accounts, ensuring that funds are available, and expenditures are assigned correctly. Reviews and/or assists with preparation of financial statements and records provided to federal, state, and internal auditors and similar personnel. As needed, assists with accounting duties for and corrections to listed financial records. As appropriate, identifies and recommends updates to accounting systems and procedures.
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LO 3, BT: S, Difficulty: Medium, TOT: 10 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
BE 1.8 Guide students to the websites for accounting firms or job posting websites such as Accounting Jobs Today.com, Indeed.com, or Robert Half Accounting. Student answers will vary. Qualifications:
Ideal applicants will have proficient knowledge of accounting and tax principles. Bachelors or master’s degree in accounting. CPA license, eligibility to sit for the CPA exam or working toward obtaining license. Strong organizational, communication and interpersonal skills. Customer service skills. Ability to work independently. Ability to analyze complex matters. Enthusiasm for work and eagerness to learn.
Responsibilities:
Preparation of income tax returns for individual, business, and fiduciary clients. Consulting engagements. Direct correspondence with clients. Direct correspondence with federal and state authorities. Work effectively with team members.
LO 1.3, BT: S, Difficulty: Medium, TOT: 10 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
BE 1.9 Guide students to the websites for accounting firms or job posting websites such as Accounting Jobs Today.com, Indeed.com, or Robert Half Accounting. Student answers will vary. Qualifications:
Strong and analytical ability; accuracy with numbers; and extreme attention to detail. Excellent computer skills using excel and Google suite. Experience with basic accounting software. Strong oral and written English communication skills. Communication skills must be effective and courteous requiring high integrity when dealing with restricted and/or highly confidential information. Ability to establish effective working relationships with employees at all levels of the organization.
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Ability to work diplomatically with others to discuss and resolve transactional issues as well as discussion of policies and workflow. Experience with Great Plains accounting software.
Responsibilities:
Support assigned accounts payable functions that include following procurement policy through payment of invoices. Ensure timely payment of all OEF’s payees, working closely with program staff and vendors to develop most efficient and cost-effective means of payment. Ensure accuracy and completeness of supporting documentation, as well as help to ensure compliance and consistency with stated policies and procedures. Supports the purchasing accounts payable system train and support program staff incorrectly using OEF's financial tools, processes, and policies to ensure compliance and efficiency. Responsible for credit card management and reconciliation process. Review transactions for appropriate coding, documentation, and approval.
LO 1.3, BT: S, Difficulty: Medium, TOT: 10 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
BE 1.10 1. I 2. E 3. I 4. E 5. E 6. E 7. E 8. E LO 1.4, BT: C, Difficulty: Easy, TOT: 5 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
BE 1.11 Critical Thinking Element 1. Stakeholders 2. Purpose 3. Alternatives 4. Risks 5. Knowledge 6. Self-reflection
Definition ANS: d. The parties potentially impacted by the results. ANS: a. The reason for the analysis. ANS: c. Different options that are considered and ranked. ANS: f. Obstacles and challenges to our thinking or analyses. ANS: e. The concepts that add meaning to the data. ANS: b. A review of what worked and what did not.
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LO 1.4, BT: C, Difficulty: Easy, TOT: 5min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
SOLUTIONS TO EXERCISES EX 1.1 Student answers will vary. 1. Customers (most of which would be students, alumni, and community members), owners or Investors of the bookstore (assume the managers are not the owners), sales tax authorities 2.
Which products have the highest contribution margin each month? How do sales of these products behave each month? Which products have the largest sales volume? Which products have the highest sales price? Which products are typically sold together (for product bundling opportunities) Which product sales respond to which types of advertising? Could sales revenues increase with online sales options from the store website?
3. Data alternatives might include number of units sold per product, product location in store, price, and cost of each product. Analysis alternatives might include:
Analyze monthly sales by product. This shows if there may be different top three products sold each month. Analyze contribution margin rather than sales revenues. If the goal is to determine profit maximization, contribution margin would be a better analysis than sales revenue because it identifies profit.
4. Data risks include incomplete and incorrect data for each of the analysis alternatives listed. Analysis risks include:
For monthly sales by product: Incorrectly writing information model formulas for total sales. For contribution margin analysis: Incorrectly calculating contribution margin and not considering avoidable fixed costs.
Sales revenues by product by month Contribution margin by product by month
5.
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Contribution Margin per unit Contribution margin ratio per product or overall Contribution margin ratio Net income by product, by month
Asking which products might work to sell as bundles. Asking which of these products might be adapted to have seasonal appeal (Pumpkin, etc.); Slicing results by month may have revealed different results. Considering that contribution margin results could be very different than sales revenues.
6.
LO 1.2, 1.4, BT: AN, Difficulty: Medium, TOT: 15 min, AACSB: Analytic, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies
EX 1.2. Student answers will vary. 1. Investors, federal and state tax authorities. 2.
Was a complete listing of all payroll commission payouts received? Are there certain employees who received the largest payroll commission payouts each month? Are there any patterns in the data regarding the dollar amount of commission paid each month? Do the commissions match the sales records by sales employees?
3. Data risks include incomplete and incorrect data. Analysis risks include sample risks (if samples are used). risk level assumptions, and errors made in analysis, such as during reconciliations. 4. Before beginning the analysis, reflect on the importance of testing the data set for cleanliness and completeness before performing more tests. After completing the analysis, reflect on what worked well and what was challenging. This will help guide future analyses of payroll. LO 1. 2, 1.4, BT: AN, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
EX 1.3 Student answers will vary. Stage Plan
Data Analysis Process 1. What is the motivation for performing the analysis” 2. What is the objective of your data analysis project?
3. What is your strategy to achieve the objective?
Response ANS: Correctly valuing inventory on the balance sheet and cost of goods sold on the income statement. ANS: To identify obsolete inventory by identifying when the last time each inventory item was sold, and which inventory items have sold less than the mean sold last year. ANS: Extract inventory table fields and join with sales invoice tables. Rank inventory sales (volume) from lowest to highest by product.
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Analyze
Report
4. What would you consider in the data set before you being your analysis? 5. What information model might you use to satisfy the objective?
6. Assume your analysis identified several items in inventory that have not sold over the last year. How would explore initial results? 7. Assume your analysis results in a calculation of inventory obsolescence as of year-end. What you consider as you interpret these results? 8. How would you communicate your results to the CFO?
ANS: Preview data fields to see if they are compatible for joining. Check for missing data or unusual data. ANS: Sum quantity sold for each inventory item. Multiply by price to get $ Sales for each inventory item. Rank lowest to highest. Draw a line where the inventory items will be considered obsolete and calculate value using unit cost. ANS: Drill down through initial results by slicing by month to see if there is more intelligence from understanding seasonality of inventory item sales.
ANS: Determine if the obsolete items were correctly categorized and if the same trend is being observed by your competitors for risk of product substitutes. ANS: Create visualizations that illustrate the lack of sales for these inventory items. Create a summary of how the value was determined for the inventory reserve, citing all assumptions used. Display the ranking of obsolete inventory.
LO 1.2,1.4, BT: AN, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
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EX 1.4 Student answers will vary. Stage Plan
Data Analysis Process 1. Motivation
2. Objective
3. Strategy
Analyze
4. Prepare data
5. Build models
6. Explore data
Report
7. Interpret
8. Communicate
Critical Thinking Elements ANS: Stakeholders: IT department, investors Risk: Terminated employees logging in with other employee’s credentials. ANS: Purpose: To find unauthorized access opportunities. Alternatives: To match employee IDs with log-ins or match log-ins with employee IDs. ANS: Alternatives: Selecting from different data from HR and log-in files Risks: Are HR files up to date with regards to currently authorized IDs? ANS: Purpose: The data selected needs to be relevant for the purpose. Risks: The data may be incomplete or incorrect. ANS: Knowledge: What log-ins are most important to capture? Alternatives: Log into system, emails, use of software, or data files ANS: Alternatives: When unauthorized employee IDs are found, search to see which applications they used while they were logged in. Risks: There may be a trojan horse (code that is triggered by access) that could destroy data or processing. ANS: Stakeholders: Does the unauthorized access put anyone at risk? Self-Reflection: What other areas of the AIS might need to be reviewed? ANS: Stakeholders: How are they impacted by the findings? Purpose: Did the findings relate to the original objective and questions?
LO 1.2,1.4, BT: AP, Difficulty: Hard, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
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EX 1.5 Student answers will vary. Stage
Data Analysis Process
Critical Thinking Elements
Plan
1. Motivation
ANS:
2. Objective
3. Strategy
ANS: ANS:
Analyze
4. Prepare data
5. Build models
6. Explore data
ANS: ANS:
I7. Interpret
ANS:
8. Communicate
Purpose: To understand if the estimations of bad debts are reasonably calculated. Knowledge: The GAAP rules for how to estimate bad debts. Alternatives: Accounts receivable data, estimation adjustments, and write offs. Risks: Missing changes in credit customers’ ability to pay obligations. Knowledge: History with each credit customer, credit policies, and past estimates for bad debts. Risks: incorrect data, missing data, Knowledge: GAAP rules for estimating bad debts. Self-reflection: Were the proper aging categories selected? Are they consistent with the industry?
ANS:
Report
Stakeholders: Management, investors, creditors, credit customers Purpose: Determine the net realizable value of accounts receivable.
ANS:
Alternatives: Calculate A/R turnover and days outstanding by customer. Purpose: Stay focused on where the bad debts might be the highest risk. Stakeholders: management, investors, creditors, credit customers Purpose: To determine if the findings answer the objective questions. Stakeholders: management, investors, creditors, credit customers Self-reflection: Is the net realizable value of accounts receivable free of material error? Would this analysis help me perform this task again for another client?
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LO 2, 4, BT: AP, Difficulty: Hard, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
EX 1.6 Student answers will vary. Stage
Data Analysis Process
Critical Thinking Elements
Plan
1. Motivation
ANS: ANS:
2. Objective
3. Strategy
4. Prepare data
5. Build models
6. Explore data
ANS: ANS:
Report
7. Interpret
8. Communicate
Purpose: To determine the solvency risk levels. Knowledge: How the cash flows work in the airline industry.
ANS:
Analyze
Stakeholders: Investors, creditors, regulators Purpose: To determine the bankruptcy risk.
ANS:
ANS:
Alternatives: Choice of ratio to analyze. Risks: Incorrect data in numerators and denominators, missing off balance sheet financing. Risks: Dirty data, incomplete data, and discounting outliers. Knowledge: Which debts have more uncertainty or higher prioritization. Knowledge: Financial statement analysis, xBRL data Purpose: To build trends and industry comparisons on selected ratio over five years. Knowledge: When a significant change in ratio has occurred, read the narratives in the annual report and other news releases for explanation. Self-reflection: Is the change in solvency a temporary change or a longer run change? Stakeholders: Investors, creditors, regulators Self-reflection: What caused the decline in solvency? How can we advise other airlines or other industries how to preserve their solvency? Stakeholders: Investors, creditors, regulators Purpose: To inform the stakeholders how serious the solvency findings are and how long the companies may be able to go before having to take drastic cash flow measures.
LO 1.2,1.4, BT: AP, Difficulty: Hard, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
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EX 1.7 Student answers will vary. Stage
Data Analysis Process
Plan 1. Motivation
2. Objectives
3. Strategy Analyze 4. Prepare data
5. Build models
6. Explore data Report 7. Interpret
8. Communicate
Brief Description
Critical Thinking
ANS: To understand what is not working in the business model or the business operations. ANS: To find the cause for the phenomena of decreasing sales with increasing net income. ANS: The data needs to include prices, variable costs per unit and volume sold. ANS: Test data for integrity and missing values and any data fields that need to be transformed to be analyzable. ANS: Calculate contribution margin per unit, or contribution margin ratio for each product type.
ANS: Stakeholders Purpose ANS: Purpose Risks ANS: Alternatives Risks
ANS: Test if the phenomena is occurring because contribution margin per unit or the number sold is the cause of the changing sales mix. ANS: Ask if there could be another explanation for the results, such as a change in allocated indirect fixed costs. ANS: Prepare a visualization explaining how the sales mix has changed towards products that have higher contribution margin per unit and suggest some future strategies for sales revenues and net income.
ANS: Purpose Knowledge ANS: Knowledge Selfreflection ANS: Purpose Alternatives ANS: Purpose Risks
ANS: Stakeholders Knowledge
LO 1.2, 1.4, BT: AP, Difficulty: Hard, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
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EX 1.8 Student answers will vary. Stage
Data Analysis Process
Brief Description
Critical Thinking Elements
Plan
1. Motivation
2. Objective
3. Strategy
Analyze
4. Prepare data
5. Build models
6. Explore data
Report
7. Interpret
8. Communicate
ANS: To investigate the anticipated tax rate changes on the government revenues and to taxpayers filing as single. ANS: Determine if the tax changes will increase tax revenues for the government (and therefore increase the tax burden on the citizens). ANS: Gather the demographics of the citizens in each category of tax rates. Calculate the mean income in each category. Multiply the mean by the new tax rates and compare to the product of the mean by the old tax rates to calculate the difference. ANS: Collect the related census data, clean dirty data and incomplete data. ANS: Group data into the tax bracket categories. Calculate the mean. Multiply mean by new tax rates. Sum the three categories. ANS: In the groups where an increase or decrease is the findings, break the category or categories into interval ranges and repeat the analysis on range means. ANS: Review and verify all data and analysis steps and review the findings with respect to the purposeful objective. ANS: Prioritize a stakeholder audience and provide visualizations and a summary of findings which
ANS: Stakeholders Purpose ANS: Purpose Risks
ANS: Knowledge Risks
ANS: Alternatives Risks ANS: Knowledge Alternatives ANS: Purpose Knowledge
ANS: Purpose Knowledge ANS: Stakeholders Purpose
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explains the impacts of the new tax rates on both the government and the single tax filing status.
Selfreflection
LO 1.2,1. 4, BT: AP, Difficulty: Hard, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
SOLUTIONS TO PROFESSIONAL APPLICATION CASE PAC 1.1 Auditing: 1. Auditors could examine the data to see if there are any exceptions, anomalies, or outliers in the receipt transactions. Any of these occurrences could indicate a higher risk of material misstatement. 2. Auditors perform substantive analytical procedures to provide evidence to support the assertion that the financial records of an entity are complete, valid, and accurate. Auditors could use the receipt transactions data test the totals of receipts to see if it agrees to the trial balance. LO 1.1,1.2,1.3,1.4, BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking,
PAC 1.2 Financial Accounting: 1. Financial accountants can use SSBI tools to perform analytics and create financial dashboards to support decision-making. Little Tots could use SSBI software to create dashboards that can track financial information such as total revenue, total expenses, and cashflow. 2. Answers may vary. Little Tots should consider creating a financial dashboard that tracks revenue and expenses. They can also track receipts so that they can monitor cashflows. They may also track assets, liabilities, and gross profit. 3. Answers will vary but should include data related to financial performance. LO 1,2,3,4, BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
PAC 1.3 Managerial Accounting: 1. Little Tots should consider the cities with the highest population of children five and under and with the highest median income. Little Tots can use this information to choose the cities that would have the highest potential income. 2. Answers will vary, but students should identify the sales forecast, break-even analysis, and income statement. 3. A budget is a predictive analytic method. LO 1,2,3,4, BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
18
PAC 1.4 Tax Accounting: Data Available Zip Code
Number of Returns (ANS) X
Dollar Amount (ANS)
Reason (ANS) Zzip codes are necessary to be able to compare cities
Size of Adjusted gross income: N/A $1 under $25,000 $25,000 under $50,000
X
$50,000 under $75,000
X
X
$75,000 under $100,000
X
X
$100,000 under $200,000
X
X
$200,000 or more
X
X
Both measures can be used to determine the total amount of income and the number of people in this income range. Both measures can be used to determine the total amount of income and the number of people in this income range. Both measures can be used to determine the total amount of income and the number of people in this income range Both measures can be used to to determine the total amount of income and the number of people in this income range. Both measures can be used to determine the total amount of income and the number of people in this
19
Number of Single Returns
X
Number of Joint Returns
X
Number of Head of Household Returns
X
Total Income Salaries and Wages in AGI
N/A X
Taxable Interest State and Local Income Tax Refunds Business or Professional Net Income Unemployment Compensation Student Loan Interest Total Standard Deduction Total itemized Deduction State and Local Taxes Total Taxes Paid Income before tax credits Earned income credit Additional child tax credit
N/A N/A
income range. These numbers give us an idea of the population. These numbers give us an idea of the population. These numbers are an indication of how many single parents there may be. X
These amounts help to determine wealthier areas
X
These measures are an indication of how many children might be in the area.
N/A N/A N/A N/A N/A N/A N/A N/A N/A X
LO 1,2,3,4, BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Analytic, AICPA BB: Strategic/Critical Thinking
20
CHAPTER 2 FOUNDATIONAL DATA ANALYSIS SKILLS Learning Objectives: LO 2.1. Describe how data is stored in and extracted from a relational database. LO 2.2. Explain how functions help answer data analysis questions. LO 2.3. Illustrate how pivot tables organize and filter data. LO 2.4. Identify descriptive measures used to perform data analysis. LO 2.55. Summarize how data visualization explores and explains data.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. B LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
2. C LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
3. D LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
4. A LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
5. B LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
6. C LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
7. B LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
8. A LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
9. C
LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
10.D LO 2.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
11.A LO2. 2, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.2
12. C LO 2.2, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.2
13. C LO 2.2, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.2
14. B LO 2.2, BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.2
15. C LO 2. 3: BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3
16. C LO 2.3: BT: AP, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3
17. B LO 2.3: BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3
18. D
1
LO 2.3: BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3
19. A LO 2.4: BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4
20. B LO 2.4: BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4
21. C LO 2.4: BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4
22. B LO 2.4: BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, , Section 2.4
23. C LO 2.4: BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, , Section 2.4
24. D LO 2.5: BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
25. B LO 2.5: BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
26. A LO 2.5: BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
27. D LO 2.5: BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
28. C LO 2.5: BT: C, Difficulty: Medium, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
ANSWERS TO REVIEW QUESTIONS 1. Inner join: Joins two tables together by selecting all rows from both tables that have matching values. Left join: Joins two tables together by returning all records from the left table and the matching records from the right table Right join: Joins two tables together and returns all records from the right table and the matched records from the left table. Full join: Joins two tables and returns all records from both the right and left tables, matching data where possible. LO 2.1, BT: K, Difficulty: Easy, TOT: 5 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1
2. Scenario
Function
1. Count the number of cells in an Excel file that have inventory quantities.
ANS: c. COUNT
2. Count the number of cells in an Excel file ANS: d. COUNTIF that have an inventory quantity of 1,150 items.
2
3. Calculate the arithmetic mean of the commission amounts paid to sales personnel during the fourth quarter
ANS: a. AVERAGE
4. Calculate the total sales amount for the period, which is listed in column K of your Excel spreadsheet.
ANS: g. SUM
5. Calculate the total sales amount for the period for customer # 4920 only. The sales amounts are listed in column K in your Excel spreadsheet, and customer numbers are listed in column A of your Excel spreadsheet.
ANS: h. SUMIF
6.Count the number of inventory items listed on the spreadsheet with no inventory quantities.
ANS: f. COUNTBLANK
LO 2.2, BT: AP, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, H2: Basic Functions for Data Analytics, Applying Excel Basic Functions
3. A COUNTIFS function is used to count the number of cells by a more than one criterion. For example, to count all the sales made in 2022 by a specific employee, use a COUNTIFs function. LO 2.2, BT: C, Difficulty: Medium, TOT: 5 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, H2: Basic Functions for Data Analytics, Applying Excel Basic Functions
4. A COUNTBLANK function counts the number of blank cells in a range. For example, to be sure that none of the cells in a client’s data set are missing data, use a COUNTBLANK to find out if and how many cells are missing data. LO 2.2, BT: C, Difficulty: Medium, TOT: 5 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.2
5. 1. Fields: The data elements for use in the pivot table. 2. Columns: When a field is chosen for a column area, only the unique values of the field are listed across the top. 3. Rows: When a field is chosen for the row area, it populates as the first column. All row values are unique values, and duplicates are removed. 4: Values: Each value is kept in a pivot table cell and displays the summarized information. Examples are sum, average, or count. 5: Filters: Apply a restriction to the entire table.
3
LO 2.3, BT: K, Difficulty: Easy, TOT: 6 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, H2: Using Pivot Tables,
6. Excel PivotTables can be filtered by 1) Applying the filter criteria to the Filter field box, 2) using the AutoFilter feature available in the pivot table rows field, and 3) inserting one or more slicers. LO 2.3, BT: C, Difficulty: Medium, TOT: 6 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3
7. The median of a distribution might be more meaningful than the mean if there are outliers in the data set. An outlier can influence the mean, but it does not influence the median. So, in the case of outliers, the median will be more representative of central tendency. LO 2.4, BT: C, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4
8. Standard deviation is the square root of the variance of a data set. Variance is the averaged squared distance between the data points and the mean. So, standard deviation indicates how far an individual observation in the data set might differ from the mean. A low standard deviation indicates the observations in the data set tend to be close to the mean, and a high standard deviation indicates the values are spread out over a wider range. LO 2.4, BT: AP, Difficulty: Hard, TOT: 10 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4
9. A negative correlation means there is an inverse relationship between two variables. As one variable increases, the other decreases. An example of a negative correlation would be temperature and heating costs. As temperature increases, heating costs go down. A positive correlation indicates that as one variable increases, the other variable also increases. Temperature and air-conditioning costs are an example of this. As temperatures rise, air-conditioning costs also rise. LO 2.4, BT: AP, Difficulty: Hard, TOT: 10 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4
10. Both methods use data visualization to analyze data. Exploratory data visualization is the use of data visualization tools and techniques to explore data and find insights. Explanatory data visualization is the use of visualization tools and techniques to communicate the results of an analysis. LO 2.5, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
11. Scenario 1.Your manager provides you with all the sales data by product line for the last two years and asks you to identify sales trends between years. 2.You have analyzed data related to sales trends by country over the past three years and will present that data using a
Visualization Type ANS: Exploratory
ANS: Explanatory
4
tree map 3.Your manager provides you with all the payments made to approved vendors in the past six months and asks you to identify if any payments are outside of expected payment amounts. 4.Your manager provides you with analysis of maintenance expenses for the year and asks you to prepare a pie-chart to illustrate the categories of expenses
ANS: Exploratory
ANS: Explanatory
LO 2.5, BT: AP, Difficulty: Medium, TOT: 6 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
12. The x-axis should have the months of the year as plotted time points, starting with January on the far left and December on the far right. The y-axis should be dollar amounts, starting with zero dollars. There should be two lines, one depicting sales tax in dollars from the current year and a second line depicting sales tax in dollars from the prior year. These lines should be solid, not dotted. LO 2.5, BT: S, Difficulty: Hard, TOT: 10 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
13. Bar charts:
Compare 2 – 7 categories with vertical bars. Use horizontal bars if more than 7 categories or long category labels. Use horizontal labels for better readability. Space bars appropriately and consistently. Use color sparingly or as an accent. Always have a zero baseline.
Area charts:
Do not use with data that has more than 4 categories to avoid confusion and clutter. Start the y-axis at zero or above. Place highly variable data on the top and low variability on the bottom.
LO 2.5, BT: C, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5
14. The chart would have the amount of rework expenses on the y-axis. The x-axis would be months/year, and there would be a line for each of the rework reason code categories. LO 2.5, BT: AP, Difficulty: Hard, TOT: 10 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5, Accounting Discipline: Financial Accounting
SOLUTIONS TO BRIEF EXERCISES BE 2.1 5
1. Primary key 2. Foreign key 3. Primary key 4. Primary key 5. Neither 6. Neither (as the data do not match up between the Employee zip code and the customer zip code. The name of the data field is the same, but the data stored in that field will be different and not able to match between tables. LO 2.1, BT: AP, Difficulty: Easy, TOT: 4 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1, Accounting Discipline: Managerial Accounting.
BE 2.2 1.
Table Restaurant Order
Primary Key RestaurantNumber OrderNumber
Customer1
CustomerID
Foreign Key None RestaurantNumber CustomerID None
2. The Order table and the Customer Table LO 2.1, BT: AP, Difficulty: Easy, TOT: 3 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1, Accounting Discipline: Accounting Information Systems
BE 2.3 The goal of the analysis is to identify inventory that has not sold in the last 12 months. Therefore, the most appropriate join is a left join because it returns all the inventory records from the Inventory on hand file and matches the inventory items with sales from the sales file. The output will have a complete listing of inventory and only those sales records that match with inventory items. LO 2.1, BT: AP, Difficulty: Medium, TOT: 4 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1, Accounting Discipline: Financial Reporting
BE 2.4
1. c. Inner join 2. a. Left join 3. d. Full join 4. a. Left join LO 2.1, BT: AP, Difficulty: Medium, TOT: 4 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1, Accounting Discipline: Financial Accounting
BE 2.5
1. $240.35
6
2. $258.11 3. $180.39 LO 2.2, BT: AP, Difficulty: Medium, TOT: 7 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1, Accounting Discipline: Managerial Accounting
BE 2.6 1. 83,055 =COUNT(D2:D83056) 2. 3,061
=COUNTIF(B2:B83056,2019) 3. 17 =COUNTIFS(B2:B83056,2019,G2:G83056,"FIREFIGHTER") LO 2.2, BT: AP, Difficulty: Medium, TOT: 7 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.2, Accounting Discipline: Accounting Information Systems
BE 2.7
1. Mario’s Pizza Parlour and Sunshine Building Inc both have $18,800 total accounts receivable.
2. Purple Corp.: $14,000
7
LO 2.3, BT: AP, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3, Accounting Discipline: Audit
BE 2.8
1. $990,000 2.
LO 2.3, BT: AP, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3, Accounting: Financial Accounting
BE 2.9 1.
8
2. Blue: 1965
3.
LO 2.3, BT: AP, Difficulty: Hard, TOT: 12 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3, Accounting Discipline: Financial Accounting, Managerial Accounting.
BE 2.10
9
1. Mean: $1,232.94 2. Median: $776.76 3. Mode: $407.56 LO 2.4, BT: AP, Difficulty: Easy, TOT: 4 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4
BE 2.11
1. Skewness: 2.20 2. Kurtosis: 7.33 3.
Frequency
Histogram 4000 3000 2000 1000 0
Frequency
Bins
LO 2.4, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4, Accounting Discipline: Financial Accounting.
10
BE 2.12
Accounts Receivable by Region $700,000 $600,000 $500,000 $400,000 $300,000 $200,000 $100,000 $Less than 60 days
60 - 90 Days CENTRAL
91 - 120 Days EAST
PRAIRIE
121-150 Days
More than 150 Days
WEST
LO 2.5, BT: AP, Difficulty: Medium, TOT: 5 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5, Accounting Discipline: Financial Accounting.
BE 2.13
Number of Accounts by Credit Rating 90 80
78
70 60 50 40 30 16
20
6
10 0 AAA
BBB
CCC
LO 2.5, BT: AP, Difficulty: Medium, TOT: 12 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5, Accounting Discipline: Financial Accounting.
11
BE 2.14
Credit Ratings and Total Loans over 150 Days Past Due $25,000 $20,000 $15,000 $10,000 $5,000 $0 AAA
BBB
CCC
LO 2.5, BT: AP, Difficulty: Hard, TOT: 15 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5, Accounting Discipline: Financial Accounting.
SOLUTIONS TO EXERCISES 12
EX 2.1 1.
2. Join the Employee table to the SalesOrders table and the Customer table to the SalesOrders Table. Customer number from the Customer table and Sale Orders table. EmployeeNumber in the Employee table to the EmployeeNumber in the SalesOrders table. 3. We will need the following fields:
Table Employee
Customer
Fields EmployeeNumber, FirstName LastName Address City State ZipCode CustomerNumber CustomerAddress CustomerCity CustomerState CustomerZipCode ContactFirstName ContactLastName
LO 2.1, BT: C, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.5, Accounting Discipline: Audit.
13
EX 2.2 1.
2. Tables Joined: SalesOrderExpenses & SalesOrders
Fields OrderNumber
3. Tables SalesOrders
Fields Sold Date, SalesVolume,UnitSalePrice
SalesOrderExpenses
All fields
LO 2.1, BT: C, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1, Accounting Discipline: Financial Accounting.
EX 2.3
14
1.
2. Tables Joined Region, SalesOrders
Fields RegionNumber
3. Tables SalesOrders
Fields SalesOrderNumber SoldDate RegionNumber SalesVolume UnitSalePrice
Regions
Region Number RegionName
LO 2.1, BT: C, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1, Accounting Discipline: Managerial Accounting.
EX 2.4
15
1.
2. Tables Joined SalesOrderExpenses & SalesOrders
Fields OrderNumber
3. Tables SalesOrders
Fields OrderNumber SoldDate State
SalesOrderExpenses
OrderNumber SalesTax
LO 2.1, BT: C, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.1, Accounting Discipline: Tax.
EX 2.5 1. Total Inventory costs for 2023: $ 410.00
16
2. Total inventory costs for Cinnamon Buns for 2022 - 2025: $324.80
3. Total inventory cost for cinnamon buns in 2022: $63.80
17
4. Total inventory costs for the city of Thornton 2022 - 2025.: $44.60
5. Total inventory costs for snickerdoodles in 2025 in the city of Brookfield: $2.15
18
LO 2.3, BT: AP, Difficulty: Hard, TOT: 15 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3, Accounting Discipline: Managerial Accounting.
EX 2.6 1. 0.123488 2. The correlation between amount and temperature is weak because the correlation coefficient is 0.123488, which is less than 0.30, which is a weak correlation between the two variables. LO 2.4, BT: AP, Difficulty: Easy, TOT: 5 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4, Accounting Discipline: Managerial Accounting.
EX 2.7 1.
Amount Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum
1,232.94 61.06 776.78 407.56 1,251.39 1,565,977.76 5.78 2.11 8,422.59 12.66 8,435.25 517,835.01
19
Count
420.00
2.
3. In July there are two points, one is $8435.25 and $$6,745.74. In September there is a payment for $7,656.85 These amounts are much higher than the other amounts in the scatterplot. Most observations in the scatterplot are group between $0 and $4,000. All amounts over $4,000 should be examined. LO 2.4, BT: AP, Difficulty: Medium, TOT: 15 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.4, Accounting Discipline: Managerial Accounting.
EX 2.8 1. Single: 31,560 2. Number of Returns by Filing Status
20
Total Number of Returns by Filing Status 35,000
31,560
30,000 25,400 25,000 20,000 15,000 10,000
5,900
5,000 Single
Joint
Head of Household
LO 2.2, 2.5, BT: AP, Difficulty: Medium, TOT: 12 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.2, 2.5, Accounting Discipline: Tax
EX 2.9 1.
Sum of Sales Row Labels 2022 2023 2024 2025 Grand Total
Column Labels Consumer $42,046 $65,903 $81,493 $104,724 $294,166
Retail $90,068 $80,323 $142,200 $109,344 $421,935
Travel Company $27,152 $36,144 $45,504 $59,255 $168,054
Grand Total $159,266 $182,370 $269,197 $273,323 $884,155
2.
21
Sales by Segment $160,000 $140,000 $120,000 $100,000
2022
$80,000
2023 2024
$60,000
2025
$40,000 $20,000 $0 Consumer
Retail
Travel Company
The consumer and travel segments are increasing over the four-year period. The retail segment decreased from 2022 to 2023, increased in 2024, but then decreased in 2025. 3.
Average of Sales
Column Labels
Row Labels 2022 2023 2024 2025
Travel Consumer Retail Company $174 $228 $242 $221 $208 $255 $300 $252 $268 $264 $172 $224
4.
Average Sales by Segment $350 $300 $250 $200
Consumer
$150
Retail Travel Company
$100 $50 $0 2022
2023
2024
2025
22
All segments show a decrease in average sales from 2024 to 2025. 5.
StdDev of Sales
Column Labels
Row Labels 2022 2023 2024 2025
Travel Consumer Retail Company $379 $530 $551 $487 $347 $552 $1,121 $623 $701 $663 $348 $413
Standard Deviation of Sales by Segment $1,200 $1,000 $800 Consumer $600
Retail Travel Company
$400 $200 $0 2022
2023
2024
2025
The line chart shows that the Consumer segment has the largest variation in sales. This is confirmed by examining the standard deviation of sales. LO 2.2, 2.4, 2.5, BT: AP, Difficulty: Medium, TOT: 20 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.2, 2.3, 2.4, 2.5, Accounting Discipline: Managerial Accounting.
EX 2.10 1.
2024 sales and 2025 sales per the PivotTable agree to the dollar amounts of sales recorded in the client’s general ledger.
23
2.
3.
Sales by Product Category $140,000 $120,000 $100,000 $80,000
Camping Gear Paddle
$60,000
Tents $40,000 $20,000 $2024
2025
24
4.
Quarterly Sales by Category $50,000 $45,000 $40,000 $35,000 $30,000 $25,000
Camping Gear
$20,000
Paddle
$15,000
Tents
$10,000 $5,000 $Qtr1
Qtr2
Qtr3
Qtr4
Qtr1
2024
Qtr2
Qtr3
Qtr4
2025
LO 2.2, 2.5, BT: AP, Difficulty: Medium, TOT: 18 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.2, 2.5, Accounting Discipline: Audit.
SOLUTIONS TO PROBLEMS PR 2.1 1.
Sum of Profit Row Labels Bluebird Cafe Breakfast King CO BREW Butters AM Eatery Corvus Coffee Roasters Crema Coffee House Cristos Coffee Dazbog Coffee Denny's Denver Biscuit Company Dutch Bros Coffee Einstein Bros. Bagels Home Cookin Café
Column Labels 2024 442.5 865.7 547.75 705.75 840.45 686.95 133.1 499.05 749.95
2025 683.85 1134.4 808.45 373.75 505.55 471.35 453.3 470.85 1054.15
1287.15 1119.25 525.35 583.6
599.6 992.7 924.65 494.9 25
Janie's Café Java Island Johnson's Corner Kneaders Bakery and Café Krispy Kreme Le Peep Loveland Coffee Company Lucile's Creole Café Modern Market Eatery Momo Lolo Coffee Panera Bread Rainbow Restaurant Red Rooster Restaurant Rocky Mountain Bagels Snooze AM Eatery Starbucks Sweet Bloom Coffee Syrup Downtown Walrus Ice Cream Ziggi's Coffee
487.5 301.35 220.7
657.35 557.15 512.55
567.55 370.35 353.5
285 654.65 615.4
513.3 436.45 975.75 252.85 386.25 760.85 711.45 607.3 644.65 362.55 753.9 1143.75 651.2
426.35
519.75 515.7 845.2 472.45 581.45 252.9 901.3 334 923.05 1288.3 513.55 1014.15 1210.1 340.35
2.
Column Sum of Profit Labels Row Labels 2024 Bluebird Cafe Breakfast King CO BREW Butters AM Eatery Corvus Coffee Roasters Crema Coffee House Cristos Coffee Dazbog Coffee Denny's Denver Biscuit Company Dutch Bros Coffee Einstein Bros. Bagels
2025 54.54% 31.04% 47.59% -47.04% -39.85% -31.39% 240.57% -5.65% 40.56% -53.42% -11.31% 76.01%
26
Home Cookin Café Janie's Café Java Island Johnson's Corner Kneaders Bakery and Café Krispy Kreme Le Peep Loveland Coffee Company Lucile's Creole Café Modern Market Eatery Momo Lolo Coffee Panera Bread Rainbow Restaurant Red Rooster Restaurant Rocky Mountain Bagels Snooze AM Eatery Starbucks Sweet Bloom Coffee Syrup Downtown Walrus Ice Cream Ziggi's Coffee
-15.20% 34.84% 84.88% 132.24% -49.78% 76.77% 74.09% -14.97% -74.09% 96.98% -14.64% -43.76% 51.71% -34.53% -0.78% -44.10% -56.51% -62.94% 39.57% 97.48% -71.87%
3. % Diff > +_ 30% % Diff > -30%
15 (use Countif function) 12 (use Countif function)
LO 2.3, BT: AP, Difficulty: Hard, TOT:25 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.3, Accounting Discipline: Audit.
PR 2.2 1. $590,810.38 2. Department Department of Buildings Department of Health Department of Water Management
Reimbursement paid in 2025 ANS: $201,473.41 ANS: $31,728.76 ANS: $66,252.76
27
3. Mean: $267.70, Median: $250.70, Mode: $266.80
4. $175.18
5.
Amount 3500 3,137.00
3000 2500 2000 1500 1000 500 0 11/4/2024 -500
12/24/2024
2/12/2025
-179.4 4/3/2025
5/23/2025
7/12/2025
8/31/2025
10/20/2025
6. Mean: $267.70 Median: $250.70 Mode: $266.80 Standard Deviation: $175.18
28
12/9/2025
Amount
3500
3,137.00
3000 2500 2000 1500 1000 500 0 11/4/2024 -500
12/24/2024
2/12/2025
4/3/2025 -179.4
5/23/2025
7/12/2025
8/31/2025
10/20/2025
12/9/2025
The scatterplot reveals that there is one transaction with a reimbursement amount of $3,137 and a negative reimbursement amount of -$179 both are unusual given the rest of the reimbursement data. 7. Students can prepare a PivotTable that shows the sum, average and standard deviation of reimbursements for 2025 by department.
29
Students can also prepare a PivotTable to show total reimbursements by job title. The following is an example of a PivotTable filtered for the Top 10 sum of reimbursements for 2025.
LO2.2, 2.4, BT: AP, Difficulty: Hard, TOT: 30 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 2.2, Accounting Discipline: Audit.
PR 2.3 1. The auditor summed the total sales amount in the client provided file noting that the sum of total sales for the year ended December 31, 2025, is $273,323, which agreed to the client’s general ledger without exception.
2. 30
Sales by Region - 2025
22.8% 33.8%
Central East South 23.1%
West
20.4%
Since the pie chart slices are similar in size, a bar chart would be more useful for making comparisons between the regions. 3. The customers with the largest sales in 2025 amounts include:
Answers will vary based on the visualization prepared by the student. One example is to create a bar chart:
31
Total Nick Crebassa Helen Wasserman Todd Sumrall
Total
Caroline Jumper Grant Thornton $-
$2,000.00 $4,000.00 $6,000.00 $8,000.00 $10,000.00
Another effective way to depict the customers with the largest sales amounts is to upload the file into Tableau and create a Tree map.
LO 2. 2, 2.5, BT: AP, Difficulty: Hard, TOT: 30 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.2, 2.5, Accounting Discipline: Audit
PR 2.4 1.
Row Labels 2024 Jan
Sum of Sales $19,320 32
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2025 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Grand Total
$17,164 $19,964 $32,536 $15,924 $18,568 $24,617 $14,964 $27,449 $39,926 $15,431 $23,332 $29,365 $18,663 $22,806 $28,714 $26,705 $24,734 $31,529 $16,845 $17,723 $17,608 $16,470 $22,161 $542,520
2.
Total Sales $45,000 $40,000 $35,000 $30,000 $25,000 $20,000 Total
$15,000 $10,000 $5,000
2023
2024
Sep
Nov
Jul
Mar
May
Jan
Sep
Nov
Jul
Mar
May
Jan
Sep
Nov
Jul
Mar
May
Jan
$0
2025
33
The monthly bar chart does show variation more easily in sales by month. It is clear that in 2024, April and October where the highest-selling months. However, this pattern does not hold in 2025, but there does appear to be a trend in April sales. 3.
$30,000 2024 Oct, $25,888 $25,000 $20,000 2024 Apr, $15,593 2025 Apr, $13,821
$15,000 2023 Apr, $12,757
Camping Gear Paddle
$10,000
Tents
$5,000 $0 Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov 2023
2024
2025
This line chart shows the variability in monthly sales by product type. The paddle category has the most fluctuation, with the largest peak in October of 2024. Tents and camping gear have similar patterns over the two-year period. LO 2, 5, BT: AP, Difficulty: Hard, TOT: 35 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.2, 2.5, Accounting Discipline: Financial Accounting, Managerial Accounting
34
SOLUTIONS TO PROFESSIONAL APPLICATION CASE PAC 2.1 Accounting Information Systems: Tables Sales and Customer
Fields 1. ANS: CustomerID
Sales and Employee
2. ANS: EmployeeID
Sales and Truck
3. ANS: TruckNumber
Sales and Menu
4. ANS: ProductNumber
Employee and Purchases
5. ANS: EmployeeID
Deposits and Cash
6. ANS: Bank Account Number
Purchases and RawMaterials
7. ANS: IngredientID
Purchases and Vendors
8. ANS: VendorID
Question
Table
Fields
Join type
9. Are there vendors that have not made purchases?
ANS: Vendors and Purchases
ANS: VendorID, CompanyName, ReceiptNumber
ANS: Right join
10. Do any employee addresses match vendor addresses?
ANS: Employees, Purchases, Vendors
ANS: Employee ID, ReceiptNumber, VendorID, Address, City, State
ANS: Inner join
11. What is the total sales by menu item?
ANS: Menu, Sales, and Receive Payments
ANS: ProductNumber, Description, OrderID, ReceiptNumber, CashAmount, CreditCardAmount
ANS: Inner join
12. What is the total sales by truck?
ANS: Truck and Sales
ANS: TruckNumber, OrderID, CashAmount, CreditAmount
ANS: Inner join
13. Are there purchases
ANS: Purchases and
ANS: ReceiptNumber,
ANS: Left join
35
made from vendors not in the Vendor table?
Vendors
VendorID
LO 2.1- 2.5, BT: AP, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.1 -2.5, Accounting Discipline: Accounting Information Systems.
PAC 2.2: Auditing 1.
36
37
2. Summary of daily deposits
Credit Card Total Date Cash Deposit Deposit Deposit 5-Jan $ 646.50 $ 514.50 $ 1,161.00 6-Jan $ 826.00 $ 782.50 $ 1,608.50 7-Jan $ 547.00 $ 480.50 $ 1,027.50 12-Jan $ 225.50 $ 364.50 $ 590.00 13-Jan $ 153.50 $ 147.00 $ 300.50 14-Jan $ 635.00 $ 1,035.00 $ 1,670.00 15-Jan $ 3,010.00 $ 2,905.50 $ 5,915.50 9-Feb $ 750.50 $ 620.50 $ 1,371.00 10-Feb $ 2,942.00 $ 2,649.50 $ 5,591.50 11-Feb $ 888.50 $ 842.50 $ 1,731.00 16-Feb $ 532.00 $ 481.50 $ 1,013.50 17-Feb $ 1,106.00 $ 860.50 $ 1,966.50 18-Feb $ 1,084.00 $ 958.50 $ 2,042.50 19-Feb $ 309.50 $ 397.00 $ 706.50 23-Feb $ 754.00 $ 717.00 $ 1,471.00 24-Feb $ 955.50 $ 1,213.50 $ 2,169.00 25-Feb $ 927.00 $ 1,151.00 $ 2,078.00 2-Mar $ 555.00 $ 665.50 $ 1,220.50 3-Mar $ 823.50 $ 516.50 $ 1,340.00 4-Mar $ 542.00 $ 1,065.00 $ 1,607.00 5-Mar $ 463.50 $ 387.00 $ 850.50 9-Mar $ 550.50 $ 651.00 $ 1,201.50 10-Mar $ 1,171.00 $ 915.50 $ 2,086.50 11-Mar $ 2,359.00 $ 2,030.50 $ 4,389.50 12-Mar $ 492.50 $ 371.00 $ 863.50 16-Mar $ 898.00 $ 472.00 $ 1,370.00 17-Mar $ 4,240.50 $ 4,262.00 $ 8,502.50 18-Mar $ 928.50 $ 870.50 $ 1,799.00 19-Mar $ 385.50 $ 603.00 $ 988.50 23-Mar $ 500.50 $ 723.00 $ 1,223.50 24-Mar $ 2,453.00 $ 2,958.00 $ 5,411.00 25-Mar $ 1,908.50 $ 1,904.50 $ 3,813.00 26-Mar $ 757.50 $ 521.00 $ 1,278.50 30-Mar $ 989.50 $ 665.50 $ 1,655.00 31-Mar $ 1,682.50 $ 2,177.00 $ 3,859.50
38
3. Reconciliation of daily deposits
Credit Card Total Difference Date Cash Deposit Deposit Deposit Total Sales Over/(Short) 5-Jan $ 646.50 $ 514.50 $ 1,161.00 $ 1,161.00 $ 6-Jan $ 826.00 $ 782.50 $ 1,608.50 $ 1,598.50 $ 10.00 7-Jan $ 547.00 $ 480.50 $ 1,027.50 $ 1,017.50 $ 10.00 12-Jan $ 225.50 $ 364.50 $ 590.00 $ 610.00 $ (20.00) 13-Jan $ 153.50 $ 147.00 $ 300.50 $ 300.50 $ 14-Jan $ 635.00 $ 1,035.00 $ 1,670.00 $ 1,710.00 $ (40.00) 15-Jan $ 3,010.00 $ 2,905.50 $ 5,915.50 $ 5,925.50 $ (10.00) 9-Feb $ 750.50 $ 620.50 $ 1,371.00 $ 1,371.00 $ 10-Feb $ 2,942.00 $ 2,649.50 $ 5,591.50 $ 5,576.50 $ 15.00 11-Feb $ 888.50 $ 842.50 $ 1,731.00 $ 1,729.00 $ 2.00 16-Feb $ 532.00 $ 481.50 $ 1,013.50 $ 1,013.50 $ 17-Feb $ 1,106.00 $ 860.50 $ 1,966.50 $ 1,966.50 $ 18-Feb $ 1,084.00 $ 958.50 $ 2,042.50 $ 2,082.50 $ (40.00) 19-Feb $ 309.50 $ 397.00 $ 706.50 $ 706.50 $ 23-Feb $ 754.00 $ 717.00 $ 1,471.00 $ 1,471.00 $ 24-Feb $ 955.50 $ 1,213.50 $ 2,169.00 $ 2,169.00 $ 25-Feb $ 927.00 $ 1,151.00 $ 2,078.00 $ 2,078.00 $ 2-Mar $ 555.00 $ 665.50 $ 1,220.50 $ 1,220.50 $ 3-Mar $ 823.50 $ 516.50 $ 1,340.00 $ 1,335.00 $ 5.00 4-Mar $ 542.00 $ 1,065.00 $ 1,607.00 $ 1,609.00 $ (2.00) 5-Mar $ 463.50 $ 387.00 $ 850.50 $ 840.50 $ 10.00 9-Mar $ 550.50 $ 651.00 $ 1,201.50 $ 1,201.50 $ 10-Mar $ 1,171.00 $ 915.50 $ 2,086.50 $ 2,126.50 $ (40.00) 11-Mar $ 2,359.00 $ 2,030.50 $ 4,389.50 $ 4,349.50 $ 40.00 12-Mar $ 492.50 $ 371.00 $ 863.50 $ 863.50 $ 16-Mar $ 898.00 $ 472.00 $ 1,370.00 $ 1,370.00 $ 17-Mar $ 4,240.50 $ 4,262.00 $ 8,502.50 $ 8,602.50 $ (100.00) 18-Mar $ 928.50 $ 870.50 $ 1,799.00 $ 1,799.00 $ 19-Mar $ 385.50 $ 603.00 $ 988.50 $ 988.50 $ 23-Mar $ 500.50 $ 723.00 $ 1,223.50 $ 1,223.50 $ 24-Mar $ 2,453.00 $ 2,958.00 $ 5,411.00 $ 5,431.00 $ (20.00) 25-Mar $ 1,908.50 $ 1,904.50 $ 3,813.00 $ 3,808.00 $ 5.00 26-Mar $ 757.50 $ 521.00 $ 1,278.50 $ 1,278.50 $ 30-Mar $ 989.50 $ 665.50 $ 1,655.00 $ 1,655.00 $ 31-Mar $ 1,682.50 $ 2,177.00 $ 3,859.50 $ 3,859.50 $ LO 2.1-2. 5, BT: AP, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.1-2.5, Accounting Discipline: Audit.
39
PAC 2.3 Financial Accounting: 1.
2.
LO 2.1, -2.5, BT: AP, Difficulty: Medium, TOT: 45 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.1-2.5, Accounting Discipline: Financial Accounting.
PAC 2.4 Managerial Accounting: 1. 40
Row Labels Average of Total Cost Bacon $ 412.62 Beef $ 231.92 Bread stick dough $ 706.14 Flour $ 606.70 Garlic $ 29.88 Green Pepper $ 111.68 Ham $ 346.60 Mozzarella Cheese $ 1,443.20 Mushrooms $ 27.20 napkins $ 192.55 Olive Oil $ 497.07 Onion $ 94.64 Paper plates $ 241.95 Pepperoni $ 564.39 Pineapple $ 40.53 pizza boxes $ 1,790.72 Ricotta Cheese $ 290.35 Salt $ 83.53 Sauce $ 559.68 sausage $ 381.12 Steak $ 583.31 Sugar $ 58.30 Wings $ 721.04 Yeast $ 95.38 Grand Total $ 434.34
2.
41
Row Labels Average of Total Cost Average of Cost per unit Costco $ 147.20 $ 19.37 Flour $ 63.27 $ 2.63 napkins $ 192.55 $ 29.43 Paper plates $ 241.95 $ 17.15 Salt $ 86.49 $ 1.00 Yeast $ 11.64 $ 6.41 Meat Co. $ 420.43 $ 0.18 Bacon $ 412.62 $ 0.19 Beef $ 231.92 $ 0.17 Ham $ 346.60 $ 0.21 Pepperoni $ 564.39 $ 0.21 sausage $ 381.12 $ 0.21 Steak $ 583.31 $ 0.13 RSC $ 806.37 $ 2.50 Bread stick dough $ 706.14 $ 0.56 Flour $ 1,070.08 $ 1.56 Mozzarella Cheese $ 1,679.04 $ 0.29 Olive Oil $ 651.09 $ 0.16 pizza boxes $ 1,790.72 $ 3.57 Ricotta Cheese $ 290.35 $ 0.99 Sauce $ 559.68 $ 36.71 Wings $ 815.10 $ 0.19 Yeast $ 126.21 $ 4.22 Sams Club $ 571.21 $ 0.75 Flour $ 406.46 $ 1.67 Mozzarella Cheese $ 1,334.35 $ 0.40 Olive Oil $ 137.69 $ 0.22 Salt $ 80.56 $ 0.63 Sugar $ 58.30 $ 1.57 Wings $ 532.91 $ 0.16 Yeast $ 76.36 $ 6.43 Walmart $ 63.63 $ 3.11 Garlic $ 29.88 $ 0.40 Green Pepper $ 111.68 $ 0.43 Mushrooms $ 27.20 $ 6.03 Onion $ 94.64 $ 1.37 Pineapple $ 40.53 $ 17.73 Grand Total $ 434.34 $ 2.08 3. Students may approach this problem differently, but the use of a PivotTable and slicers is shown here. Students can use the slicers to analyze each product and compare average prices.
42
LO 2.1-2. 5, BT: AP, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.1-2.5, Accounting Discipline: Managerial Accounting
PAC 2.5 Tax Accounting: 1.
43
Total Fort Lauderdale city, Florida Cape Coral city, Florida Tallahassee city, Florida Port St. Lucie city, Florida Hialeah city, Florida Total
St. Petersburg city, Florida Orlando city, Florida Tampa city, Florida Miami city, Florida Jacksonville city, Florida -
200,000 400,000 600,000 800,000 1,000,000
2.
3. Tallahassee has a tax rate of 7.5%. On sales of $150,000 the tax amount would be $11,250 LO 2.1- 2.5, BT: AP, Difficulty: Medium, TOT: 15 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 2.1 – 2.5, Accounting Discipline: Tax
44
CHAPTER 3 MOTIVATIONS AND OBJECTIVES FOR DATA ANALYSIS Learning Objectives: LO 3.1: Summarize the relationship between motivations, objectives, and data analysis questions. LO 3.2: Demonstrate how to develop descriptive questions. LO 3.3: Demonstrate how to develop diagnostic questions. LO 3.4: Demonstrate how to develop predictive questions. LO 3.5: Demonstrate how to develop prescriptive questions. LO 3.6: Describe motivations and objectives for data analytics in professional practice.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. A
LO 3.2, BT: K, Difficulty: easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
LO 3.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
12.A
2. B
LO 3.2, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
LO 3.1, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Comprehension, AICPA AC: Technology and Tools
13.B
3. E
LO 3.3, BT: K, Difficulty: easy, TOT: 2 min., AACSB: Knowledge, AICPA AC Technology and Tools
LO 3.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
14.C
4. A
LO 3.3, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
LO 3.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
15.C
5. C
LO 3.4, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
LO 3.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
16.C
6. A
LO 3.4, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
LO 3.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
17.D
7. D
LO 3.5, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
LO 3.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
18.B
8. E
LO 3.5, BT: K, Difficulty: easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
LO 4.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
19.A
9. B
LO 3.6, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
LO 3.1, BT: K, Difficulty: easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
20.E
10.A
LO 3.6, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
LO 3.2, BT: K, Difficulty: easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
21.C
11.C
LO 3.6, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
22.D 3-1
LO 3.6, BT: C, Difficulty: medium, TOT: 3 min., AACSB: Comprehension, AICPA AC: Technology and Tools
.
ANSWERS TO REVIEW QUESTIONS 1. 1. New channels, new clients, services, markets, products, and technologies. 2. New or upcoming laws and regulations and changes in requirements. 3. Solving risks and/or problems related to clients, employees, technologies, or supply chain disruptions. 4. Budget to actual price variances, volume or cost variances, and evaluating financial statements for material and/or potential misstatements. LO 3.1, BT: K, Difficulty: Easy, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
2. Financial accountants in public corporations or organizations that need external financing are motivated to perform analyses on how upcoming, new, or existing laws and regulations that will impact the financial statement information for their organizations. Managerial accountants are not as constrained by accounting rules and regulations for their preferred types of data analyses, but they must be aware of any industry-related laws and regulations, and how they may impact their organization’s efforts to execute their strategies. LO 3.1, LO 3.6, BT: C, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools
3. An example would be a financial accountant analyzing how the solvency ratios for a company have trended over the past five years. The managers of that company may have the perspective of wanting more debt leverage so that the company’s wealth can grow quickly. The creditors of that same company may be concerned about getting their payments on time, including both their capital loaned to the company and their interest on any debt paid. LO 3.2, LO 3.4, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
4.
The client’s inventory costing method. How many manufacturing facilities the company has. The number of product lines being manufactured at one time. The assertion(s) the auditor is testing for. How this information can be used in a subsequent analysis of potential slow-moving inventory during the audit.
LO 3.1, 3.4, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
5. Answers will vary.
Has the total dollar amount of employee expense reimbursement increased during the last fiscal year compared to the prior fiscal year? Has the total dollar amount of employee expense reimbursement decreased during the last fiscal year compared to the prior fiscal year?
3-2
Has the average amount of employee expense reimbursement to each employee increased or decreased during the latest fiscal year? Are there any individual employee’s expense reimbursements that increased or decreased by X% during the latest fiscal year compared to the prior fiscal year?
LO 3.2, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
6. Answers will vary.
Have successful login attempts increased during non-business hours? Have failed login attempts increased during non-business hours? Have successful login attempts increased during business hours? Have failed login attempts increased during business hours?
LO 3.2, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
7. Accountants use diagnostic analytics such as anomaly detection, correlation analysis, pattern analysis and trend analysis to provide context about why an outcome occurred. For example, these diagnostic techniques can help auditors to identify anomalies, managerial accountants identify cost patterns, and financial accountants analyze income trends in the data that may contribute to why outcomes occurred. LO 3.3, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
8. Answers will vary.
Are there any anomalies in sales volume for the company’s products? Are there patterns in sales volume for the company’s products? Are there trends in the sales volume for the company’s products?
LO 3.3, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
9. Predictive objectives helps understand what may happen in the future. Descriptive objectives help us understand what has happened in the past and is recorded in the data. LO 3.2, LO 3.4, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
10. A dependent variable is the outcome measure, and independent variables are the variables that influence the dependent variable. The goal of predictive analytics is to build a model that can help predict or better understand the phenomenon in which we are interested. Dependent and independent variables are used to build models that have predictive power. LO 3.4, BT: K, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools
11. Optimization is the process of selecting values of variables that minimize or maximize some quantity of interest. What-if analyses allow professionals to evaluate changes and specific combinations of model inputs and assumptions impact. The objective of predictive analytics is to build on descriptions of the present and predictions about the future to determine the best possible course of action. Optimization models and what-if analyses use a combination of present and predictor variables to identify potential outcomes. LO 3.5, BT: K, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools
12. Initial Question: How many of each product should we produce and sell to maximize contribution margin? 3-3
Sub-Question: Are there constraints that we need to include in our optimization model? How many of each product should we produce to maximize contribution margin and satisfy any constraints? LO 3.5, BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools
13. AIS accountants typically are responsible for the functioning of the accounting system technologies, internal controls, and data storage. They are concerned about system availability, reliability, security, and integrity. The primary stakeholders would be the organization’s management, the board of directors and the investors they represent, and the external auditors. LO 3.6, BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools
14. The primary motivation for auditors to perform data analyses is to provide support for their decisions regarding whether the balances reported on the financial statements are free from material error. Auditors perform data analyses when they evaluate the internal controls of their client, when they are testing the reported balances, and when they are evaluating the related footnote disclosures. LO 3.6, BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools
15. Financial accountants are typically responsible for recording, storing, and reporting the economic transactions and dispositions of assets by an organization during an accounting period. They are motivated to perform data analyses on any transactions and adjusting entries that involve estimations or assumptions, as these may get tested and challenged by the external auditors. LO 3.6, BT: AP, Difficulty: Medium, TOT: 4 min., AACSB: Analytic, AICPA AC: Technology and Tools
16. Tax accountants perform both tax planning and tax compliance assessments for their clients or organizations. The tax code often requires that several tests on the data are performed to determine if the tax entity qualifies for a particular tax treatment or condition. Tax accountants must consider alternative ways to gather the data needed for these determinations and what the minimum data thresholds would have to be to include those calculations in their tax liability determinations. LO 3.6, BT: AP, Difficulty: Medium, TOT: 4 min., AACSB: Analytic, AICPA AC: Technology and Tools
SOLUTIONS TO BRIEF EXERCISES BE 3.1
Motivation 1. Opportunity 2. Regulation or law 3. Problem 4. Process or performance evaluation
Event ANS: c. new industry expertise ANS: d. new standard for leases ANS: a. significant cost increases ANS: b. annual accomplishments
LO 3.1, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
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BE 3.2 Motivation to Perform Analyses
Analytics Area
1. Analyze the mean, median and mode number of failed login attempts after the company changed password requirements to understand if the benefits of enhanced password requirements outweigh the costs.
ANS: a. Descriptive analysis
2. Analyze the correlation between the quantity of phishing attempts reported and employee attendance at cybersecurity training to determine if training is related to reporting of phishing attempts.
ANS: b. Diagnostic analysis
3. Analyze trends of failed login attempts over time to determine if employees are complying with company policy regarding login credentials.
ANS: b. Diagnostic analysis
4. Perform a linear regression analysis to consider the likelihood of future information security breaches based on the independent variables of dollars spent on cybersecurity training and encryption software.
ANS: c. Predictive analysis
5. Perform a what-if analysis to identify the appropriate dollar amount of spending that is necessary to meet the entity’s goals for information security.
ANS: d. Prescriptive analysis
LO 3.1, LO 3.2, LO 3.2, LO 3,3, LO 3.5, LO3,5, LO 3.6 , BT: AP, Difficulty: Easy, TOT: 4 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 3.3
Scenario 1. You are a tax accountant working at a large multi-national company. Executive management is trying to decide the best country in which to expand operations. You have been tasked with identifying independent variables and performing regression analysis to predict potential revenue from this expansion. 2. Your company has several key performance indicators (KPIs) associated with its
Motivation Source ANS: a. Opportunities
ANS: b. Process and performance
assessment
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manufacturing process – especially quality control. You have been tasked with performing an analysis to identify whether quality control benchmarks are being accurately recorded in the information system. 3. You are an external auditor working on a public company manufacturing client. You have been asked to test the operating effectiveness of internal controls associated with the purchasing groups’ compliance with the internal control that all purchase orders above $10,000 are approved by a purchasing supervisor. 4. You are a financial analyst working for a company that distributes consumer products to retailers across the U.S. must find out why sales of your historically most popular product, the electric grill, have been declining.
ANS: b. Process and performance
assessment
ANS: d. Problem solving
LO 3.1, LO 3.6, BT: AP, Difficulty Easy TOT: 4 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 3.4 A good question is one that relates to the objective, is specific, measurable, and can be answered with the available data. LO 3.2, LO 3.6, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA ???
BE 3.5 1. The objective of the analysis is to understand the dollar amount paid to vendors during 2024 and 2025. 2.
Questions 1. Which five vendors did the city pay the most in 2024 and 2025? 2. Considering department spending, which five vendors did the city pay the most in 2024 and 2025 by department? 3. Comparing 2024 and 2025, what is the total amount of spending by fiscal month during 2024 and 2025?
Measures ANS: Sum of check total ANS: Sum of check total
ANS: Sum of check total
3. Answers can vary as students may design an analysis using Tableau, Excel, or Power BI. Next is an analysis design based on using Excel:
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Question 1: Create a PivotTable to group all Check Total payments by vendor. Separate by fiscal year and sort by descending order, identifying the vendors with the highest payment amount first. Question 2: Create a PivotTable to group all Check Total payments by vendor. Separate by fiscal year and add Department as a row. Sort by descending dollar order. Question 3: Create a PivotTable to group all Check Total payments by vendor. Separate by fiscal year and fiscal month. Sort by descending dollar order.
LO 3.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 3.6 Initial Descriptive Analysis Questions or SubQuestions 1. How many units were produced for each product category in the facility? 2. What is the average number of units produced by machine #1065 during first shift, second shift, and third shift? 3. What is the greatest number of units that machine #1810 produced? 4. What is the range of units produced during the period?
Analysis Choice ANS: f ANS: c
ANS: a ANS: b
LO 3.2, BT: AP, Difficulty: Easy TOT: 5 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 3.7 1. The objective of the analysis is to analyze purchase transactions from the current year to see if there are any unusual transactions. The analysis will provide your purchasing group information for understanding the purchasing activity for specified vendors during the period. 2. (1) Are there any unusually large purchase transactions made during the period? (2) Are there any identifiable patterns in purchases for specific vendors during the period? (3) Are there any identifiable patterns in purchases for specific items during the period? 3. (1) Create a scatterplot plotting total PO amount on the y-axis.(2) Create a line graph of purchases over time by vendor. (3) Create a line graph of purchases over time by items. LO 3.3 BT: S, Difficulty: Medium, TOT: 8 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 3.8 Objective
Initial Question
Sub-questions
Possible Measures
Why did sales returns decrease from prior year?
1. ANS: a. What is driving the sales return decrease?
Are there anomalies in sales returns for any specific product the company sells?
Create a scatterplot comparing quantity returned to sales price for items to identify
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anomalies in returns.
Create a scatterplot comparing sales dollars to sales returns for items to identify anomalies in returns.
Why is there a large decrease in sales returns in the Northwest region?
What factors are driving the decrease in sales returns in the Northwest region?
Are there identifiable trends in sales returns for any specific product?
2. ANS: c. Create a trend analysis showing returns by date for each product line for the current year compared to the prior year.
3. ANS: b. Are there unusual patterns in the Northwest region sales returns for certain product lines?
4. ANS: d. Filter data to isolate the northwest region sales returns and examine total sales returns, average sales returns, number of sales returns and sales return quantity.
LO 3.3 BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Knowledge, AICPA AC: Technology and Tools
BE 3.9 Description
Analysis
1. The statistical measures used to evaluate a regression model.
ANS: c. Regression statistics
2. The outcome variable in a regression model
ANS: e. Dependent variable
3. The variable or variables that influence the outcome variable.
ANS: a. Independent variable
4. This type of relationship shows steady increases or decreases over the range of the independent variable.
ANS: b. Linear function
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5. This statistic measures the strength of the relationship between the dependent and independent variables.
ANS: d. Correlation coefficient
6. This statistic explains how well the regression line fits the data.
ANS: f. Adjusted R2
LO 3.4 BT: K, Difficulty: Easy, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
BE 3.10 1. Independent variable: sales volume. Dependent variable: revenue. 2. Independent variable: production volume. Dependent variable: revenue. 3. Independent variable: production volume. Dependent variable: maintenance expenses. 4. Independent variable: unemployment. Dependent variable: sales volume. 5. Independent variable: number of employees. Dependent variable: information security costs. LO 3.4 BT: K, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools
BE 3.11 1. (0.2387 * 9) + 5.1176 = 7.2 2. (.2387 * 35) + 5.1176 = 13.47 LO 3.4, LO 3.6 BT: S, Difficulty: Medium, TOT: 3 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 3.12 Definition
Term ANS: d. Optimization
1. The process of selecting values of variables that minimize or maximize some quantity of interest. ANS: c. Decision variable 2. The unknown values a model seeks to determine.
3. The mathematical equation that describes the output target that we seek to minimize or maximize.
ANS: b. Objective function
4. The limitations, requirements or other restrictions that must be imposed on any solution.
ANS: a. Constraints
LO 3.5 BT: K, Difficulty: Easy, TOT: 3 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 3.13 1. The decision variables in the model are the number of each item produced. 2. The constraints in the model are machine hours per unit and demand per unit. 3. The objective function is the formula for total contribution margin.
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LO 3.5, LO 3.6 BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 3. 14 Answers will vary. 1. The objective of the analysis is to identify possible capital acquisition scenarios. 2. What is the amount of interest the company would pay if we took out a loan for the needed capital? What are the total costs we would incur if we decided to go public? 3. Analyses that could be used to answer the questions include:
Time value of money calculation for interest expense based on the interest rates on available loans. Summation of estimated costs based on projected cost of services required to go public.
LO 3.5, LO 3.6 BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 3.15 1. Inappropriate for practice area. This is a financial accounting question 2. Inappropriate for practice area. This a managerial accounting question 3. Appropriate for practice area. 4. Appropriate for practice area. 5. Appropriate for practice area. LO 3.6 BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Analytic, AICPA NA
BE 3.16 Statement 1. Simone and the information technology team are investigating the possibility of investing in new technologies. 2. Chung would like to improve the company’s information system performance and is evaluating processing time to prepare month-end management reports. 3. Christine wants to identify unusual sales transactions that may have contributed to an unexpected increase in the client’s revenue compared to last year. 4. Daniel is currently using the Scenario Manager function in Excel to identify interest rates, payment terms, and length of a loan to evaluate various capital requirement strategies. 5. Ellis is analyzing a client’s tax liability calculation to identify if there are expenses that are not properly included as deductions.
Practice Area ANS: a. Accounting information systems
ANS: a. accounting information systems
ANS: d. Auditing
ANS: b. Financial accounting
ANS: e. Tax accounting
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6. Pierre is currently examining the relationship between machine hours and maintenance expenses to predict future manufacturing modifications. 7. Mica is performing a regression analysis to estimate the predicted monthly revenue for a retail client. He will then compare the predicted monthly revenue to the client’s recorded amount.
ANS: c. Managerial accounting
ANS: d. Auditing
LO 3.6 BT: AP, Difficulty: Easy, TOT: 10 min., AACSB: Analytic, AICPA: NA
SOLUTIONS TO EXERCISES EX 3.1 This exercise can be completed using multiple software platforms. We show the solution in Excel. Formulas were used to determine the total of sales and the average sales amount: 1. Sum of Sales = $963,114. Formula =SUM(F2:F101) 2. Average of Sales = $9,631. Formula =AVERAGE(F2:F101) 3. A PivotTable was used to list the total sales by customer and the data was sorted to show the sales from highest to lowest.
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4. Conditional formatting was used to create a highlight table in Excel.
Tableau Solution: This question can also be answered using Tableau. A Tableau Packaged Workbook file is provided for students to use to create the same analysis shown in Excel. 1. Open the Tableau Workbook and click on Sheet 1 2. Drag Sales Amount to Text
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3. Right Click on the tab for Sheet 1 and rename the sheet “Total Sales Amount” 4. Right Click on the tab again and choose Duplicate. 5. Change the Sum of Sales Amount to Average by clicking on the down arrow, and then Measure, and then Average.
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6. Add a new worksheet. 7. Drag Customer Name to Rows. 8. Drag Sales Amount to Text. 9. Drag Sales Amount again but to Color. 10. To change the color scale, click on the Sum (Sales Amount) color legend.
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11. Chose Edit Colors and then change the Palette to Custom Sequential:
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LO 3.2 BT: S, Difficulty: Easy TOT: 10 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.2 1. The objective of analysis is to understand production quantity in 2025 compared to 2025 by product. 2. There are many initial questions that students can identify. Examples include:
Has production increased in 2025 compared to 2025 for the Merino Wool - Porridge Chunky Product? Has production increased for the Merino Wool – Tan DK product? Has production increased for the Merino Wool – Undyed Chunky product? Has production increased for the Merino Wool – Undyed DK product?
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3. Students can create a column chart using the software of their choice. The following bar chart was created using a PivotTable and PivotChart as described in Chapter 2.
6000 5492
5000 4411 4000
2988
3000
2847
2024 2466
2000
1847
1976
2025
2084
1000
0 Merino Wool - Porridge Merino Wool - Tan DK Merino Wool - Undyed Merino Wool - Undyed Chunky Chunky DK
4. a. (Note: The use of a PivotTable is not required.)
b. (Note: The use of a PivotTable is not required.)
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c. (Note: The use of a PivotTable is not required.)
LO 3.2, 3.6 BT: S, Difficulty: Medium TOT: 15 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.3 Open the Tableau Packaged Workbook: DA_CH 03_EOC_EX 3.3 City Data 1. Open a new worksheet in Tableau and drag the Vendor Name to Rows and FY to Columns. Next, drag Check Subtotal to Text.
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2. Create a calculation that will calculate the change in vendor payments to the specified vendors between 2024 and 2025. Since we do not need the other vendor’s payment information and the fiscal years other than 2024 and 2025, create two filters. One filter for the specified vendors and a second for the fiscal years 2024 and 2025. First drag all the vendors to the Filters box: you will see an input box that looks like this:
Click None. Using the Enter Search Text box, type in the names of the five specified vendors you are interested in examining. They are:
4-Star Hose & Supply Aecom Techical Services, Inc. WRG, LLC Winston Water Cooler Ltd. Zoetis Inc.
After checking the boxes for each of these vendors, click Apply. Next drag the FY to Filter and uncheck all years except 2024 and 2025. Click apply and OK.
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3.
Create the calculation. Click on the down arrow in the green pill for Sum(chksubtot), and then select Quick Table Calculation and Difference.
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4.
To create a highlight table in which darker colors represent larger numbers, select that chart type from the Show Me options in the top right corner of the screen.
The table will look like this:
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5.
To only show the column for 2025, right click on the 2024 column and choose Hide. Be sure to extend the vendor column so that each vendor’s entire name is displayed.
EX 3.4 Objective
Initial Question
Sub-questions
Possible Measures
Understand cybersecurity training expenses.
What is the total amount of cybersecurity training expense for 2023, 2024 and 2025?
1. ANS: What is the total cybersecurity training expenses by Training Modality for 2023, 2024, 2025?
2. ANS: Sum Training expenses by Training Modality for each year 2023, 2024, 2025.
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What is the percentage change of cybersecurity training expenses between 2024 and 2023 and between 2025 and 2024?
3. ANS: What is the percentage change of cybersecurity training expenses by modality between 2024 and 2023 and between 2025 and 2024?
4. ANS: Percentage change of training expenses
5. To calculate the training expense by modality for each year (2023, 2024, and 2025), the student could use a combination of data filtering and the SUMIF functions in Excel. An efficient method is to create a PivotTable whereby Training modality is in Rows, Month/Year is in Columns, and Sum of Training Expenses is in Values, which is depicted next:
The student can calculate the percentage change of training expenses between 2024 and 2023 by using a combination of data filtering and SUMIF statements. However, an efficient method is to use the
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created pivot table and calculate the difference and percentage change based on the pivot table groupings of training modality, which is show next:
LO 3.2, 3-6 BT: S, Difficulty: Medium, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.5 1 and 2: SalesDollars
ReturnsDollars
SalesQuantity
ReturnsQuantity
1. Totals 2025
18,700,044
225,606
124,674
2,176
1. Totals 2024
14,853,679
168,632
99,032
1,795
Change 2. % Change
3,846,365 25.9%
56,974 33.8%
25,642 25.9%
381 21.2%
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3.
Sales Dollars Compared to Sales Returns 25,000
20,000
15,000
10,000
5,000
-
500,000
1,000,000
1,500,000
2,000,000
2,500,000
4. The scatterplot reveals that there are several data points that should be considered for investigation, including:
December 2025 January 2025 February 2025
LO 3.3, 3.6 BT: S, Difficulty: Medium, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.6 1. Why did sales increase by 5.14%? 2. Answers can vary: Examples include:
What is driving the sales increase compared to prior year? Which customers had increased sales from the prior year? Which customers had decreased sales from the prior year?
3. The potential measures will vary based on the sub-questions student’s articulate for question part 2. Examples include:
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Calculate sales by customer in June 2024 and compare to June 2025. Identify customers with increased sales between June 2024 and June 2025. Identify customers with decreased sales between June 2024 and June 2025.
4.
180,000 160,000 140,000 120,000 100,000
2024
80,000
2025
60,000 40,000 20,000 1
2
3
6
8
10
11
12
13
14
15
16
18
20
21
22
30
5. 120000 100000 80000 60000 40000 20000
2024 2025
0
LO 3.3, 3-6 BT: S, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.7 Students’ solutions will vary. Examples are provided.
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Objective
Initial Question
Sub-questions
Possible Measures
Understand why accounts receivable increased compared to the prior year.
1. ANS: Are there individual customers whose accounts receivable balance increased since the prior year?
2. ANS: Are there new customers?
3. ANS: Create a comparative bar chart indicating total AR by customer.
ANS: Are the existing customers paying within their payment terms?
ANS: Calculate aging buckets (0-30; 31-60; 6190; 90-120; over 120 days) by customer and calculate the overall percentage change by aging bucket.
ANS: Are there individual customers who have older accounts receivable this year compared to prior year?
LO 3.3, 3.6 BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.8 Objective
Initial question
Sub-questions
Possible Measures
Explain why cyber incident reports are increasing.
1. ANS: Is there a trend in cyber incident reports during the year?
Are there more cyber incidents reporting during a specific period of time?
Create a line chart of cyber incident reports over time and review for a trend or pattern.
2. ANS: Is there a relationship between cyber incident reports and training expenses?
3. ANS: Are there more cyber incident reported when the firm spends more money on cyber training expenses?
Perform a correlation between training expenses and cyber incidents reported by using the CORREL function in Excel.
4. Line chart:
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16 14 12 10 8
2023
6
2024
4
2025
2 0
Correlation: The correlation between cyber incidents reported and cybersecurity training expenses is 0.7918, which suggests a strong positive correlation. As training expenses increased cyber incident reports increased.
LO 433, 3.6 BT: S, Difficulty: Medium, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.9 Step 1: Open the data file and sort the data in ascending order by training expenses. Step 2: Create a scatterplot using training expenses on the x-axis and cyber incidents reported on the yaxis.
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Cybersecurity training & Incidents Reported 16 14 12 10 8 6 4 2 0 $-
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
Cyber incidents reported 16 14 12 10 8 6 4 2 0 0
5
10
15
20
25
30
35
40
Cyber incidents reported
Step 3: Click the plus sign on the graph and select Trendline, More Options.
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Step 4: Click on Display Equation on chart and Display R-squared value on chart.
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Cybersecurity training & Incidents Reported 16 14 y = 0.002x - 0.1298 R² = 0.627
12 10 8 6 4 2 0 $-
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
This includes the trendline, the equation and the R2 . LO 3.4, 3.6 BT: S, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.10 Objective
Initial question
Sub-question
Possible Measures
Predict sales returns
How much will sales returns change if we have a 10% increase in sales quantity?
1. ANS: How much will sales returns change for each month?
Regression analysis
2. Input for regression dialog box:
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Output of regression: SUMMARY OUTPUT Regression Statistics Multiple R 0.896758 R Square 0.804175 Adjusted R Square 0.795274 Standard Error 1645.34 Observatio ns 24 ANOVA df Regression
1
Residual
22
Total
23
SS 2.45E+0 8 595571 37 3.04E+0 8
MS 2.45E+ 08 270714 3
Significanc F eF 90.345 04 0.0000
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Coefficie nts
Intercept SalesQuant ity
Standar d Error
-4546.93
2231.99
2.250116
0.23673
t Stat
P-value
2.0371 7 9.5050 01
0.0538 41 3.02E09
Lower 95% 9175.7968 51 1.7591684 41
Upper 95%
Lower 95.0%
Upper 95.0%
81.931 18 -9175.8 2.7410 1.7591 63 68
81.931 18 2.7410 63
3. SalesReturns = - $4,546.93 + $2.25(SalesQuantity) 4. 0.804175 LO 3.5, 3.6 BT: S, Difficulty: Medium, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.11 1. SUMMARY OUTPUT
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations
0.72467701 0.525156768 0.503572985 19488.86612 24
ANOVA
df Regression Residual Total
SS
MS
F
1 9241331312 9241331312 24.33108051 22 8355949862 379815902.8 23 17597281175
Coefficients Intercept 25918.11469 SalesQuantity 13.83134439
Standard Error
t Stat
P-value
26437.67495 0.980347733 0.337573113 2.804036723 4.93265451 6.20238E-05
Significance F 0.00
Lower 95%
Upper 95%
28910.26736 80746.49674 8.016128146 19.64656063
2. Warranty Expense = $25,918 + $13.83(SalesQuantity)
3. Application of the Regression equation:
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Warranty Expense = $25,918 + $13.83(SalesQuantity = 10,385) Warranty Expense = $169,543 Dr. Warranty Expense
$169,543
Cr. Warranty Accrual Liability
$169,543
LO 3.5, 3.6 BT: S, Difficulty: Medium, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.12 1.
2. Maintenance Expense = $19,358.81 + $60.48(Die Cut Changes) 3. Maintenance Expense = $19,358.81 + $60.48(Die Cut Changes) = $19,358.81 + $60.48(650) = $58,671 LO 3.5, 3.6 BT: S, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.13 1. Gross sales. 2. Volume and average price. 3. Gross Sales = $597,355 + 424.7 (volume of product sold) + 1,453 (average price) LO 3.4, 3.6 BT: S, Difficulty: Easy, TOT: 5 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.14 Step 1: Download the worksheet and review for completeness. The worksheet should contain the following (as pictured):
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Step 2: Click Data and Solver (you may have to add Solver Add-in). Enter parameters as follows:
Step 3: Click Solve and select Answer Report.
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Step 4: Results.
LO 3.5, LO 3.6 BT: S, Difficulty: Medium, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.15 1. Step 1: To create the linear optimization model, download the spreadsheet and review for completeness.
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Step 2: Click Data, then Solver. Create the constraints as outlined here.
Step 3: Output is as follows:
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2.
12-foot standard leash: 11 6-foot standard leash: 8 8-foot standard leash: 0 Decorative 12-foot leash: 0 Decorative 6-foot leash: 1 Decorative 8-foot leash: 0
Because the solver function suggested fractions of leashes, round to the nearest whole number. 3. The total gross profit to expect is approximately $42.05 based on the rounded number of leashes produced times the GP per leash. 4. The answer report shows three non-binding constraints: Number of produced 8-foot leashes, decorative 12-foot leashes, and decorative 8-foot leashes.
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LO 3.5 BT: S, Difficulty: Hard,, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.16 1. Students should build a linear optimization model like Illustration 3.24 in the chapter and perform the solver function. Input parameters:
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Solver Result:
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The solver result suggests that the group should produce a total of 14 units as follows (partial units should be rounded to whole numbers):
2.
Ball cap: 5 Go Team t-shirt: 3 Defense Wins Championships t-shirt: 3 Socks: 2 Knee-high socks: 1
3. The total GP expected for each week is: $81.10 4. The answer report suggests one non-binding constraint – which is total hours on the product. This is presented in the following answer report.
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LO 3.5 BT: S, Difficulty: Hard, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 3.17 1.
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2.
Input parameters:
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Solver Result:
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3. Answer report shows that Design hours, Bracelet sales and Earring sales are all binding constraints.
They should make 10 necklaces, 30 bracelets, and 10 earrings. Design hours are binding, as are Bracelet and Earring sales.
4. The owner has excess materials and machining hours. Perhaps they can explore adding another employee for additional design hours to the extent that she has materials and machining hours available. LO 3.5, 3.6, BT: AP, Difficulty: Hard, TOT: 35 min., AACSB: Analytic, AICPA AC: Technology and Tools
SOLUTIONS TO PROFESSIONAL APPLICATION CASE PAC 3.1 Auditing: Answers will vary. What follows are suggested solutions.
Objective
Initial Question
Sub-questions
Possible Measures
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Descriptive Analysis: Understand changes in gross patient revenue (GrossPatientRevenue) for the year 2019, which is the year under audit.
What happened to gross patient revenue during 2019
1. ANS: What was total gross patient revenue by hospital location in 2019? ANS: What was the Total Inpatient Revenue in 2019
2 a. ANS: Sum of Gross Patient Revenue by location. 2 b. ANS: Components of Gross Patient Revenue: Gross Inpatient and Gross Outpatient.
ANS: What was the Total Outpatient Revenue for 2019
Diagnostic Analysis: Understand what happened to gross patient revenue for the year 2019 compared to 2018. Specifically, GrossPatientRevenue, GrossInpatientRevenue, and GrossOutpatientRevenue data points.
Which locations had changes in revenue from 2018 to 2019
3. ANS: Did one specific location contribute to the increase? Were multiple locations contributors to the increase in overall revenue?
4a. ANS: Calculate each of the data fields revenue amount compared to prior year.
ANS: Did outpatient revenue increase greater in 2019 than in 2018?
4b.ANS: Calculate outpatient revenue for each year by facility and calculate the percentage change from 2019 to 2018.
ANS: Did inpatient revenue increase by location?
4c. ANS: Sort by percentage change to understand whether any facility had increases that may be considered an anomaly.
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Predictive Analysis: Develop an expectation for gross patient revenue in 2019 based on the number of Patient Days, Outpatient Visits, and Average Length of Stay.
What factors drive changes in gross revenue?
5. ANS: Are Patient Days, Outpatient Visits and Average Length of Stay related to Gross Patient Revenue?
6. ANS: Perform an ANOVA regression analysis to predict revenue based on factors identified.
ANS: How does predicted revenue compare to actual revenue?
7. Descriptive analyses (2a: Revenue by location for 2019)
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Gross Patient Revenue By Location - 2019 KINDRED HOSPITAL - SAN DIEGO KINDRED HOSPITAL - BREA KINDRED HOSPITAL - RIVERSIDE KINDRED HOSPITAL - SOUTH BAY KINDRED HOSPITAL - RANCHO KINDRED HOSPITAL - PARAMOUNT KINDRED HOSPITAL - BALDWIN PARK KINDRED HOSPITAL - SAN FRANCISCO BAY AREA KINDRED HOSPITAL - ONTARIO KINDRED HOSPITAL - LOS ANGELES KINDRED HOSPITAL - WESTMINSTER KINDRED HOSPITAL - LA MIRADA $-
$100,000,000
$200,000,000
$300,000,000
$400,000,000
Descriptive analyses (2b: Revenue components)
Components of Gross Patient Revenue Row Labels 2018 2019 Grand Total
Sum of GR_IP_TOT $ 2,506,075,426 $ 2,794,920,753 $ 5,300,996,179
Sum of GR_OP_TOT $ 9,806,121 $ 7,291,246 $ 17,097,367
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$2,900,000,000
Components of Gross Patient Revenue - 2018 & 2019 $7,291,246
$2,800,000,000 $2,700,000,000 $2,600,000,000
Sum of GR_OP_TOT Sum of GR_IP_TOT
$9,806,121
$2,500,000,000 $2,794,920,753
$2,400,000,000
$2,506,075,426
$2,300,000,000 2018
2019
Diagnostic Analyses (4a: Revenue by year and by facility):
Diagnostic Analyses (4b: Gross patient revenue by facility and percent change by facility compared to 2018) Sum of GrossPatientRevenue
Column Labels 2018
2019
Grand Total
KINDRED HOSPITAL - BALDWIN PARK
203,673,677
225,891,799
429,565,476
KINDRED HOSPITAL - BREA
135,222,650
129,608,301
264,830,951
KINDRED HOSPITAL - LA MIRADA
415,793,588
411,650,138
827,443,726
KINDRED HOSPITAL - LOS ANGELES
286,964,028
292,450,836
579,414,864
KINDRED HOSPITAL - ONTARIO
258,909,574
270,635,955
529,545,529
215,681,816
215,681,816
Row Labels
KINDRED HOSPITAL - PARAMOUNT
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KINDRED HOSPITAL - RANCHO
197,987,742
205,305,579
403,293,321
KINDRED HOSPITAL - RIVERSIDE
126,978,172
131,354,235
258,332,407
KINDRED HOSPITAL - SAN DIEGO KINDRED HOSPITAL - SAN FRANCISCO BAY AREA
135,152,725
125,934,968
261,087,693
233,750,033
239,849,686
473,599,719
KINDRED HOSPITAL - SOUTH BAY
159,279,254
170,598,331
329,877,585
KINDRED HOSPITAL - WESTMINSTER
362,170,104
383,250,355
745,420,459
Grand Total
2,515,881,547
2,802,211,999
5,318,093,546
Diagnostic Analyses (4c: Gross Inpatient and outpatient revenue by facility for 2018 and 2019)
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Outpatient Percent Change:
Inpatient Percent Change: 3-51
Predictive Analyses
Input the data for GrossPatientRevenue as the Dependent Variable (Y) Range. Input PatientDays, OutpatientVisits, an Average Length of Stay as independent variables in the (X) Range:
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Millions
Predicted Vs. Actual Gross Patient Revenue $450 $400 $350 $300 $250 $200 $150 $100 $50 $-
2019 Gross Patient Revenue - Actual 2019 Gross Patient Revenue - Predicted
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PAC 3.2. Managerial Accounting: Answers will vary. What follows are suggested solutions. Objective
Initial Question
Sub-questions
Possible Measures
Descriptive Analysis:
What happened to labor hours during 2018 and 2019?
1. ANS: What is the total, average, minimum and maximum for each of the data fields in 2017 -2019
2. ANS: Calculate total, average, minimum, maximum for each of the data fields.
Which hospitals have increasing salary expenses from 2018 to 2019?
3. ANS: What is the total salary expense by hospital for each year?
4. ANS: Total salaries
Understand labor Information for 2018 -2019
Diagnostic Analysis: Determine why variables changed between 2018 and 2019
ANS: Average Salaries
ANS: Which hospitals have higher than average salary expenses? Predictive Analysis: Identify if there is a relationship between productive hours and patient revenue.
Can productive hours predict gross patient revenue?
5. ANS: How strong is the relationship between productive hours and patient revenue?
6. ANS: Regression analysis using ProductiveHours as the independent variable and GrossPatientRevenue as the dependent variable.
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7. Descriptive Analysis:
Diagnostic Analysis:
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Predictive Analysis:
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PAC 3.3 Financial Accounting: Answers will vary. What follows are suggested solutions.
Objective
Initial Question
Sub-questions
Possible Measures
Descriptive Analysis:
What happened to gross patient revenue during 2019?
1. ANS: What was total gross patient revenue by hospital location in 2019.
2. ANS: Sum Gross Patient Revenue and all categories that comprise gross patient revenue.
How has revenue changed at the locations?
3. ANS: Which facilities have inpatient services?
4. ANS: Use a pivot table to calculate by-facility in patient revenue
Understand gross inpatient revenue for 2019, which is the year under audit Diagnostic Analysis: Understand what happened to inpatient revenue in 2018 compared to 2019
For which facilities did in-patient services increase in 2019 compared to 2018?
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Predictive Analysis: Identify the utilization measures that are related to in-patient revenue.
Are there any measures correlated to inpatient revenue?
5. ANS: Are the occupancy and length of stay correlated to gross inpatient revenue
6. ANS: Perform a correlation analysis to determine correlated variables.
7. Descriptive Analysis:
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Diagnostic Analysis:
Predictive analysis:
PAC 3.4 Tax Accounting: Answers will vary. What follows are suggested solutions.
Objective Descriptive Analysis: Understand assets, revenue, and income information for Kindred’s non-profit and for-profit hospitals Descriptive Analysis: Understand how IRS 990 data for non-
Initial Question Sub-questions Which hospitals have 1. ANS: What are the the highest income? assets/income/ revenue for Kindred’s non-profit hospitals? ANS: What are the assets/income/ revenue for Kindred’s for-profit hospitals? Are the Kindred 3. ANS: What is the Hospitals average amount of comparable to the assets/income/revenue sample of IRS 990 tax for non-profit hospitals
Possible Measures 2. ANS: Total Assets Total Revenue Net Income
4. ANS: Average Assets Average Income Average Revenue
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profit hospitals compares to the Kindred non-profit hospitals Diagnostic Analysis: Identify trends in assets, revenue, and income for Kindred hospitals.
Diagnostic Analysis: Compare Kindred data to IRS 990 data.
filings.
in the 990 tax filing sample data?
5. ANS: Are there any trends in assets, revenue, and income for Kindred hospitals?
6. ANS: How have assets, revenue, and income for non-profit hospitals increased or decreased over the years?
8. ANS: How do assets, revenue, and income for Kindred hospitals compare to the hospital in the IRS 990 tax filings sample?
ANS: How have assets, revenue, and income for profit hospitals increased or decreased over the years? 9. ANS: How does average assets, revenue, and income for Kindred non-profit hospitals compare to the IRS tax filings sample?
7. ANS: Percent change Total Assets Percent Change Total Revenue Percent Change Net Income
10. ANS: Average Assets Average Income Average Revenue ANS: Kindred: Average TotalAssets Average TotalRevenue Average NetIncome
11. 3-62
Totals by Hospital and Tax Status:
Averages from 990 tax filing data:
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CHAPTER 4 PLANNING DATA AND ANALYSIS STRATEGIES Learning Objectives: LO 4.1 Identify the components of a data analysis project plan. LO 4.2 Describe how to develop a data strategy. LO 4.3 Explain how an analysis strategy is designed. LO 4.4 Summarize data and analyses strategies in professional practice areas.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. A
12. A
LO 4.1, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
LO 4.2, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: N/A
2. D LO 4.1, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
3. D LO 4.1, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
4. E LO 4.1, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation.
5. B LO 4.1, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation.
6. A LO 4.1, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC:
13. A LO 4.2, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA: N/A
14. D LO 4.2, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA: N/A
15. D LO 4.3, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
16. C LO 4.3, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
17.E LO 4.3, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
18. C
Measurement Analysis and Interpretation.
LO 4.3, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
7. C
19. D
LO 4.1, Difficulty: Hard, TOT: 2 min., AACSB: Knowledge, AICPA: N/A
LO 4.3, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
8. E LO 4.1, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
9. D LO 4.1, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
10. C LO 4.2, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA: N/A
11. C LO 4.2, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA: N/A
20. C LO 4.4, Difficulty: Hard, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
21. D LO 4.4, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
22. A
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LO 4.4, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
LO 4.4, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
23. E
25. A
LO 4.4, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
LO 4.4, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
24. C
ANSWERS TO REVIEW QUESTIONS 1.
Step 1: Focus on the objective and questions. These elements of the data analysis process drive the choices made in the remaining steps. Step 2: Select a data strategy by developing data alternatives, ranking those alternatives on varying prioritized factors, and select the best option for the project objective. Step 3: Select an analysis strategy by considering analysis alternatives, ranking them on varying prioritized, and select the best analysis strategy for the project objective. Step 4: Consider risks to data and analysis strategies by evaluating potential threats in each that may influence the reliability of the analysis’s results. Step 5: Embed controls to reduce identified risks in the selected data and analysis strategies.
LO 4.1, Difficulty: Easy, TOT: 6 min., AACSB: Knowledge, AICPA: NA
2. Focusing on objectives and questions when generating data and analysis alternatives allows the professional to pick the most appropriate alternative to satisfy the objective of the analysis. Considering many alternatives makes it more likely that we will choose the most valuable data and analysis methods for the project’s objective and related questions. LO 4.1, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: Strategic Perspective
3. Data risks include incomplete, inaccurate, incorrectly formatted, irrelevant, insufficient, and unrepresentative data in data fields. Some of these risks may be caused by the processes that generate, capture, extract, or import the data into the analysis technology tools. Analysis risks include errors or bias in management estimations and assumptions, and any issues that may be caused by the person performing the analysis. This includes biases, assumptions, the stability of the underlying process generating or extracting the data, a lack of experience and/or knowledge with the technology or the meaning of the analysis results, and errors in the formulas and analysis steps. LO 4.1, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: Strategic Perspective
4.
Categorical data, also referred to as nominal data, are data that identify as group membership and can be represented by symbols, acronyms, or labels. These typically have no numerical measures. Examples include account numbers, or inventory product categories. Ordinal data are ranked categorical data where the difference between the categories does not need to be known or does not need to be equal. An example are quality ratings.
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Interval data are discrete or continuous ordinal data with meaningful, equal distance between the measures so that the difference also has meaning between observations and an arbitrary zero point. Examples are temperatures, dates and time, and credit scores. Ratio data are interval data with a true zero. All mathematical functions can be performed on ratio data. Examples include financial measures such as prices and costs.
LO 4.2, Difficulty: Easy, TOT: 6 min., AACSB: Knowledge, AICPA: NA
5. Data fields must be available and relevant to the project’s objective. The data must help answer the objective questions. Inappropriate data either are not available (or have been determined to cost too much to collect or purchase) or not theoretically connected, correlated, or causally related to the objective measures of interest. LO 4.2, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA: Strategic Perspective
6. Students can select any two of the following common risks:
Using a non-representative sample of data. This risk can be mitigated by verifying the representativeness of the sample. This can be done by evaluating the population characteristics compared to the sample or testing other samples. Including outlier data points. A control for this risk is performing a histogram or quartile analysis to identify, explain, or justify the removal or inclusion of those outliers. Including dirty data such as missing data, incorrect values, or poorly formatted data. This risk can be reduced by performing tests on the data to verify the data integrity, and then cleaning any issues encountered.
LO 4.2, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA: Strategic Perspective
7. The measurement scale impacts which analyses are appropriately performed on that data. For example, if you want to know more about the dispersion of customer’s textual data satisfaction ratings over time, a categorical variable, then transform that data first into an ordinal variable to describe the minimum, maximum, and quartile responses from the customers. LO 4.3, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA: Strategic Perspective
8. It is important to match the type of analysis performed with the data’s measurement scale because the category of variable will dictate the type of analyses can be performed. For example, when analyzing categorical or ordinal variables, we can perform descriptive analyses, but we should not perform predictive analyses, as these analyses do not have any informational value to stakeholders. LO 4.3, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: NA
9. A trend analysis is a time-series analyses used to understand how one interval or ratio variable (or more) changes over a time period. Student responses regarding an example will vary: An accountant may prepare a trend analysis to understand how sales change over the period of a year. LO 4.3, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
10. Student answers will vary:
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Data risk: Difficulty capturing the complete number of spam emails for each employee of the company as different employees may have different definitions of what is a spam email. Data control: Survey employees to learn how they define what is a spam email. Data Risk: The high velocity of spam emails may make data capture challenging. Data control: Perform thorough data cleaning and triangulation to verify data capture. Data risk: Dirty data and outliers. Data control: Create and enforce data retention and destruction policies Data risk: Human behavior cannot be captured, and some employees may reclassify spam email that makes it through the firewall into their inbox and mark it as “not-spam.” Data control: Perform completeness testing of data.
LO 4.4, Difficulty: Hard, TOT: 8 min., AACSB: Analytic, AICPA: Risk Assessment, Analysis and Management
11. Answers will vary.
Auditors are interested in learning where the inappropriate P-card use risk is the highest. A total of the P-cardTransactionAmount data field could be calculated. This P-cardTransactionAmount total could be sliced and then ranked by EmployeeNumber to see which employees are using the P-card for the most value. This P-cardTransactionAmount total could be sliced and then ranked by VendorNumber to see which vendors are being paid the most by using the P-card. This P-cardTransactionAmount total could be sliced and then ranked by VendorNumber and EmployeeNumber to see if there are any unusual patterns between which employees and which vendors are being paid the most by using the P-card. The mean P-cardTransactionAmount could be calculated, and then outlier transactions could be identified for further review by the auditors.
LO 4.4, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
12.Tax accountants must consider the risks of omitted taxable revenues and inclusions of irrelevant and inappropriate expenses. Related controls include indirect tests of reasonableness for data completeness as well as interviewing the clients. LO 4.4, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
SOLUTIONS TO BRIEF EXERCISES BE 4.1 a. 5 b. 1 c. 2 d. 4 e. 3 LO 4.1, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
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BE 4.2 a. 2 b. 3 c. 1 d. 4 LO 4.1, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
BE 4.3 1.K 2. S 3. P 4. A 5. R LO 4.1, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
BE 4.4 Example 1. Customer survey ratings.
Measurement Scale ANS: b. Ordinal
2. Account numbers in the chart of accounts.
ANS: a. Categorical
3. States or regions of the country.
ANS: a. Categorical
4. Book value of depreciable assets.
ANS: d. Ratio
5. Transaction dates, such as the dates orders are shipped.
ANS: c. Interval
6. The credit scores of loan applicants.
ANS: c. Interval
7. The VendorID field.
ANS: a. Categorical
8. Sales revenues.
ANS: d. Ratio
9. The net weight of product boxes shipped.
ANS: d. Ratio
10. Supermarket inventory product type, such as produce, pet food, meat, and frozen foods.
ANS: a. Categorical
LO 4.2, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
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BE 4.5 1. The data field VendorID is a non-measured raw data field because it is data automatically created by the company through identification codes. Each vendor will have a discrete vendor ID, which can be used as a data dimension to analyze vendor-specific data. VendorID is a categorical measurement scale. 2. The VendorQuality data field should be considered ordinal data because it is discrete data with meaningful ordering of the measures but does not require the same distance between levels and does not have a defined zero point. 3. The ItemCost (economic value) and ItemQty (economic quantity) data fields are both measured raw data fields and are ratio data. These are data created or captured by a controlled process and can be discrete or continuous data fields. Measured raw data fields and ratio data can be used to create meaningful calculated data fields. 4. Dirty data: incorrect format, duplicate, or inaccurate data Missing data fields Unreasonable data (i.e., the Snappy Supplies unit cost is significantly higher than other entries, quite the outlier, might be an error) 5. The intern would be expected to ask the client for a copy of PO number 2006 and then review the data included in the PO to determine if it matches the data included in the database. LO 4.2, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
BE 4.6 1. NRD (non-measured raw data) 2. MRD (measured raw data) 3. CAL (Calculated data) 4. CAL (Calculated data) 5. NRD (non-measured raw data) 6. MRD (measured raw data) LO 4.2, Difficulty: Medium TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
BE 4.7 Strategy 1. Aging of accounts receivable’s year-end open invoices on days outstanding to understand what is happening in accounts receivable 2. Estimating cost savings and productivity increases from new AIS investments 3. Creating dollar value stratified sampling plans for investment asset accounts 4. Counting and ranking web server security incidents by week 5. Estimating product revenue increases by product from marketing campaigns 6. Estimating future cash flows from a continuing
Objective ANS: describe
ANS: predict ANS: describe ANS: describe ANS: predict ANS: predict
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ANS: describe ANS: prescribe ANS: predict
ANS: diagnose
LO 4.3, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
BE 4.8 1. Yes 2. No 3. Yes 4. Yes 5. No 6. Yes LO 4.3, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
BE 4.9 1. No 2. No 3. Yes 4. Yes 5. Yes 6. Yes LO 4.3, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
BE 4.10 1. The data sample may not be representative of its population or may have missing or incorrect values. Auditors evaluate transaction frequency and size when they pick their data samples and analyze the processes and controls used to capture the data. They need to consider which missing or incorrect values are legitimate as is, or if they need to be corrected or investigated, and if they implicate risks in other parts of the audit. 2. The underlying conditions impacting the company may have changed, which would make the prior year’s estimations and assumptions less relevant and could justify a change in the estimates and assumptions. For example, estimates in bad debt risk of accounts receivable are impacted by changes in collection policies, new customers, general economy or money supply changes, or changes in existing customers’ liquidity. 3. Missing data may be the biggest data risk, as some confirmation requests do not get fulfilled. Also, confirmations do not address the risk of unrecorded transactions. LO 4.4, Difficulty: Hard, TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
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BE 4.11 Strategy 1. Calculating the average amount of time each department in the revenue cycle takes to perform its function per order. 2. Calculating the cost savings expected from moving to RFID (radio free identification) technologies for inventory identification, pricing, and costing. 3. Estimating how much server space would be needed for the AIS if sales revenues were to increase by 10% each year for the next five years. 4. Detecting at which times of day and by which channel the unauthorized access is occurring to the customer relationship management module.
Chapter 4
Objective ANS: D Descriptive
ANS: P Prescriptive
ANS: P Predictive
ANS: D Diagnostic
LO 4.4, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
BE 4.12 Student answers will vary. Data strategy: Gather the anticipated interest rate, interest payments, and dividend payouts needed for the financing needed depending on the method of financing (e.g. bonds, share, other forms of debt). Analysis strategy: Compare the after-tax interest costs associated with debt financing to the dividends and ownership power loss associated with equity financing, as well as comparing the impacts of either alternative on key financial ratios such as return on investment, and debt-to-equity ratios. LO 4.4, Difficulty: Hard, TOT: 8 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
SOLUTIONS TO EXERCISES EX 4.1 1. e 2. b 3. a 4. b 5. c 6. a 7. c LO 4.1, Difficulty: Easy, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.2 A possible data strategy could include using data fields such as InventoryItemCode, DateOfSale, and InventoryItemUnitsold for the current and prior year. This data could be aggregated by month or quarter and should be reviewed for accuracy, completeness.
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An analysis strategy for this objective could include calculating product line unit sales frequency distributions in annual total and by month or quarter, calculating unit sales changes by product, subtracting last year’s total by this year’s total (either monthly, quarterly, or in annual totals). Visualizing each product’s sales for both years on a graph may provide deeper insights as to the patterns of sales for each product line. LO 4.1, Difficulty: Medium TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.3 1.b 2.c 3.f 4.e 5.d 6.a LO 4.1, Difficulty: Medium, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.4 Student answers will vary. Data fields are identified by corresponding numbers. a. 1. 2, 3, 5, 6, 7, 8, and 10 b. 10, 11, and 13 c. 2, 3, 10, 11, 13, and 14 LO 4.2, Difficulty: Medium, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.5 Student answers will vary. Data strategy: Retrieve completed and credit-earning courses by course code, credit hours and grade earned. Analysis strategy: Calculate overall GPA, business courses GPA, and accounting courses GPA. Data and Analysis Risks: You can assume that your university has high data integrity in academic records but review the data for reasonableness and accuracy if entering each course and grade into an Excel spreadsheet. You do not know what grades you will be earning in your current semester, so if you are including those courses, estimate those grades, which are subject to your biases and assumptions. If you do not include those estimates, then your data is incomplete and will not match the completed program GPA. Decide if you will count the introductory financial accounting course as a business course, an accounting course, or as both (an assumption). Data and Analysis Controls: Control recommendations may include verifying your data entry with the university records data. You may want to test the robustness of your last semester grade estimations by also estimating them more optimistically and less optimistically to determine the sensitivity of your GPA calculations. You may need to decide how many decimal points you should report in your results and if and where you should perform any rounding. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Medium, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.6 Data risks may include incomplete data, incompatible data format, and erroneous data. Controls would include performing tests on the data completeness to identify any null cells or duplicated data, transforming the data fields so that they can be used in your analysis, and verifying the vendor addresses as currently valid (in case any have moved). Also, you could check to see if the vendor 4-9
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address represents from where that vendor ships the purchase to your facilities (and is not the business office address). LO 4.1, 4.2, 4.3, 4.4, Difficulty: Medium, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.7 Student answers will vary. Data strategy: Collect the sales prices and quantities sold for each product by each salesperson or sales team, as well as the corresponding inventory costs from the last year as well as the respective commissions earned last year. Analysis strategy: Evaluate how several different commission strategies might impact units sold. The objective would be to prescribe which commission structure may best save commission costs while most effectively incentivizing the sales team to promote products that will results in higher net income. Data risks: Dirty data such as missing data, incorrect data, and data formatting issues should be evaluated and corrected before beginning the analysis. Also, market sales data in the industry could be reviewed to see which product lines are growing for your company’s competitors to better estimate which product sales may increase with the incentive structures considered (such as commissions based on contribution margin rather than price). Analysis risks: Assumptions regarding whether the sales items and volume would not have changed with the different commission structures. There would be no way to determine how much sales volume could have been increased from last year’s data. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Medium, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.8 Student answers will vary. Data strategy: A possible data strategy may include the following fields for the last four or five years: SaleDate, InvCode, NoSold, InvPrice, and InvCost. Analysis strategy: An analysis strategy could use the last four or five years of data to better predict next year’s sales volumes and mix. Then, calculate the expected gross profit margin ratio based on those results (since this is a retail beauty supply shop, the entire cost of goods sold would be a variable cost). Data and analysis risks: Missing data (the other variable costs that are associated with beauty supply store sales). Analysis risks include incorrect assumptions about future sales or failing to include changes in the market that could influence sales (such as new competitors, both local and online) that could impact the predicted sales mix and volume. Inflation could also negatively impact the expected contribution margins. Data and analysis controls: Preparing both pessimistic and optimistic predictions for a better grasp on how sensitive the predictions are to changes in the sales mix, prices, and costs. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Medium, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.9 Student answers will vary. Analysis strategy: Trend lines could be prepared for each store’s sales to illuminate which stores’ sales are increasing. Percentage increases in sales for each year over the prior year could also be visualized with trend lines. Percentage growth could be ranked from greatest growth to least, so that you could easily see which stores had the greatest growth.
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Analysis strategy risks and controls: Dirty data that could influence your analysis results. There could also be omitted relevant data, such as how many new competitors are in each store’s range or temporary road construction that may have impacted sales negatively. Surveying or interviewing the managers for each store may provide insights as to what was happening to the stores’ sales that cannot be determined from the data strategy selected. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Medium, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.10 Student answers will vary.
Data and Analysis Strategies 1. Data: ANS: Include the following variables Username (categorical) Time login (categorical) Attempt success of failure (binary) (categorical)
Risks
Controls
3. Data: ANS: Dirty data
5. Data: ANS: Verify integrity of data set and clean up dirty data issues.
4. Analysis: ANS: Outlier data points may be skewing results.
6. Analysis : ANS: Explanation or justification of the rule used for outlier removal. Test the analysis without the outlier point (perform sensitivity analysis).
2. Analysis: ANS: Use Excel with the data analytics add-in or statistical software to calculate the mean, median, standard deviation, and distribution of failed login attempts. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Medium, TOT: 12 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.11 1. Identify the salon products that contribute to the salon’s retail store profitability. 2. Data fields: Inventory Code, Inventory Description, Inventory price, and inventory cost. Data Analysis Choices: Calculate each inventory item’s gross margin using subtraction. Subtract the inventory cost field from the inventory price field. Then, sort the data so the highest gross margin items are presented first.
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3. Risks include sales data errors, data entry errors, incomplete data, or inaccurate data. Control choices include verify sales data with associated receipts from the POS, review inventory cost data for reasonableness and review inventory sales prices in the database to the dollar amount on the receipt. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
EX 4.12 1. Data strategy: Include the following data fields: VendorID, VendorName, PODate, ItemCost, ItemQty. Data risks include missing and incorrect data, which can be controlled by proper data preparation. 2. Analysis strategy: Include the following steps: First, create purchase order totals by vendor by year. Second, calculate the totals purchased by vendor for each year, and calculate the difference between 2024 and 2025 by vendor. Third, rank these differences from the largest increase to the largest decrease in purchases by vendor. Analysis risks include errors made in the formulas or ranking, so a control would be to verify these steps. 3. (See solution file.) A variety of software packages can be used to analyze this project, including Excel, Tableau, PowerBI, Alteryx. The sample solution uses Microsoft Excel. Step 1: There is no data field for the total Purchase Order amount. Therefore, first calculate the total PO amount using ItemCost times ItemQty. Name the new field “TotalPOAmount.” Step 2: Next, create a PivotTable using all the data available. Select and drag VendorName to the bottom left panel of the PivotTable fields for Rows, and select and drag the TotalPOAmount (variable created in step 1) to PivotTable Values. Finally, select and drag the PODate into Columns. The result is follows: Sum of total PO amount
Column Labels 2024
Row Labels CTG EastCoast Supply Pep N Supply Playtime Toys Quality Food Production Snappy Supplies Winner's Circle Foods Grand Total
2025
6,872 3,150 4,308 1,327
4,126 1,359 4,769 2,798
10,998 4,509 9,077 4,125
2,715 7,190
818 2,182 4,721 20,773
881 4,897 11,911 46,398
63
25,625
Grand Total
Step 3: Calculate the difference between 2025 and 2024 purchases for each vendor using subtraction. Subtract the 2025 amount from the 2024 amount for each vendor, and rank the results from largest increase to largest decrease. Vendors Playtime Toys
2024 $1,327
2025 Difference $2,798 $1,471
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Quality Food Production Pep N Supply Snappy Supplies EastCoast Supply Winner's Circle Foods CTG
$63 $4,308 $2,715 $3,150 $7,190 $6,872
$818 $4,769 $2,182 $1,359 $4,721 $4,126
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$755 $462 -$533 -$1,791 -$2,469 -$2,747
The vendors with the highest dollar purchases are: Pep N Supply, Playtime Toys, Quality Food Production $8,000.00 $7,000.00 $6,000.00 $5,000.00 $4,000.00 $3,000.00 $2,000.00
2024
$1,000.00
2025
$0.00
LO 4.1, 4.2, 4.3, 4.4, Difficulty: Hard, TOT: 25 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
SOLUTIONS TO PROBLEMS PR 4.1 Answers will vary. Objective and Questions Objective: Understand p-card spending in the current year. Questions: What are the three vendors where the Company spends the most money using pcards?
Data and Analysis Strategies: Data strategy: Use the data provided in the excel file PR 4.1 P Card Data Excel file from Wiley Plus
Analysis strategy: Use
Risks
Controls
1. Data: ANS: Incomplete data: Do I have all the data? Inappropriate data: Are any unusual onetime investment purchased included (outliers)? Are all employees with Pcards included?
3. Data: ANS: Compare purchasing and employee data to source documents.
4. Analysis: ANS:
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Which employee spends the highest dollar amount of money using the pcard?
Data Analytics and Accounting: An Integrated Approach. Excel to create a pivot table that allows grouping of the pcard spending data by vendor and sort by the highest vendor. Use Excel to create a pivot table to group p-card spending amount by employee and sort by the highest dollar amount by employee.
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2. Analysis: ANS: Not knowing how to use Pivot Tables correctly.
Become trained in Pivot tables or have someone else on the team review your work.
ANS: Uncorrected dirty data influencing results– There are some missing employees that were not included, and their purchases are missing, or were nonoperating purpose purchases impacting the results.
ANS: Compare data to employee and Pcard activity source documents to identify missing or inappropriate amounts that could be skewing the results. ANS: Create histograms to evaluate for normal purchase dispersion outliers.
5. Use Excel to create a pivot table that allows grouping of the p-card spending data by vendor and sort by the highest vendor: Place Vendor in Rows, and the Sum of Amount in Values. Then, sort by largest to smallest. You may want to format your solution to eliminate the decimals. Vendor Sheridan Hotel Tom Thumb Anixter, Inc Amazon Superior Linen Service VWR International Integrated Energy Energy, Inc. Party City Natural Water Company B&C Business Products Fuzzy's Taco Shop
Sum of Amount 1,634 1,172 1,012 791 750 746 645 361 343 175 163 130
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Holiday Inn Hotel TJY, Inc. Sheridan Hotel Learning Connection Staples Michaels Whole Foods AOCI Inc. Home Depot Cotton Patch Café
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119 84 70 58 55 53 39 37 16 13
Use Excel to create a pivot table to group p-card spending amount by employee and sort by the highest dollar amount by employee. Employee Owsley, Lamar Adams, John Tatsuya, Jennifer Boil, PJ Smart, Sienna Evans, Donald Campos, Chloe Barton, John Nguyen, Chi Charles, Lewis Parton, Natalie
Sum of Amount 2,271.14 1,238.98 993.29 957.12 901.95 750.23 597.29 395.02 175.30 130.03 54.89
Sheridan Hotel, Tom Thumb, and Anixter, Inc are the vendors with the highest purchase amounts, and Lamar Owsley and John Adams are the employees who have used the p-card for the most purchases. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Hard, TOT: 30 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
PR 4.2 Answers will vary. 1. The following data fields could be included: (listed here with their labels): Inventory Code, Number sold, Inventory Description, Inventory price, and inventory cost. 2. The data strategy would also include an evaluation of the data risks and data controls that would reduce those risks:
The data fields would include: InvCode, NoSold, InvDesc, InvPrice, and InvCost. Risks in these data fields include dirty data issues such as missing, duplicated, and incorrect data. Controls would include performing data field tests for null values, and for consistency tests for price and cost data by InvCode. 4-15
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3. A possible analysis strategy could be:
Create a new calculated variable for GrossMargin by subtracting InvCost from the InvPrice for each InvCode. The resulting field would be GrossMargin. Multiply the newly calculated GrossMargin field by the NoSold data field to get the total gross profit, rather than the gross margin per unit. Sort the data so the highest gross margin items are presented first. Analysis risks include price and cost changes that occurred during the period. These risks can be controlled by performing price and cost consistency tests over the time period, and when changes are noted, then the total gross profit formulas should be weighted average totals based on the units sold at each price and cost.
4. The top two selling products that contributed to the salon’s profitability are: Sihrya's 2 & 1 for Men Sihrya's Conditioner for Men
1,402.08 1,342.00
LO 4.1, 4.2, 4.3, 4.4, Difficulty: Hard, TOT: 25 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
PR 4.3 Answers will vary. 1. Categorical, ordinal, ratio (respectively). 2. There is incomplete data, which may influence the accuracy of the results if they are presented as measures of average responses. Specifically, there are incomplete records such as, the respondent did not complete the survey. In addition, the database team mentioned that they only captured data from survey results completed through September 10th. It is possible that there is an incomplete data set as more surveys were completed after September 10th. Controls would be to verify all missing data and to add survey results after Sept. 10. 3. It is not appropriate to use ordinal variables to create predictive analyses, as these analyses would not have any true meaning. Furthermore, the data only includes comments received between September 110. Ten days may not be enough to capture a representative sample of the underlying population. 4. Student responses will vary based on the analysis they choose to perform.
Objective and Questions Objective: Understand factors that contribute to a guest’s quality rating Question: What is the average quality rating by location?
Data and Analysis Strategy Data strategy: Fields selected for this project: Location Quality rating
Risks
Controls
Data Risks: Incomplete data Respondents may be biased when responding to the survey. Survey data only captured respondents
Data Controls: Look at data entries for obvious data entry errors, such as ratings outside of the rating scale range.
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between September 1 and September 10, more responses to the survey could modify the results of the analysis. Analysis strategy: Use Excel to perform a pivot table and group QualityRating by location. Sort to examine highest, lowest, and average quality rating by location. Count the number of QualityRatings by location.
Analysis risks: Improper analysis given data measurement scales.
Analysis controls:
Verify appropriateness of analysis for each filed’s data measure scales.
Some rows’ incomplete data may be impacting results.
Perform the analysis twice, once with only data that is fully complete The and then again with SpendingAmount data that is partially field may explain complete to see if more of the variance in a guest’s there is a significant difference. satisfaction. The dispersion of the QualityRating field may provide more insights than the average measure alone.
Test the strength of the association between Location and QualityRating. Perform the analysis with SpendingAmount and QualityRating to see if there is a stronger association than Location.
Average Quality Rating by location:
Row Labels
Average of Quality Rating
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Chicago Dallas Houston New Orleans Portland St. Louis Grand Total
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4.17 3.00 2.75 3.60 4.00 3.71 3.48
Highest quality rating per location: Row Labels Chicago Dallas Houston New Orleans Portland St. Louis Grand Total
Max of Quality Rating 5 5 5 5 4 6 6
Lowest quality rating per location: Row Labels Chicago Dallas Houston New Orleans Portland St. Louis Grand Total
Min of Quality Rating 2 1 1 2 4 1 1
Some students may include testing of the correlation between SpendingAmount and QualityRating. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Hard, TOT: 35 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
PR 4.4 Answers will vary. 1. Step 1: The objective is to determine the state jurisdiction with the most customers. The specific objective question: Which cities are Beautiful Bites collecting the most sales tax from? Step 2: Data strategy: Use the sales data from 2025 from the AIS system and to properly prepare the data by verifying that it is complete, accurate, and free of duplicates. (Assume that this data has already been extracted and cleaned for this analysis in the Excel file provided).
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Step 3: Analysis strategy: Create a PivotTable from the data. Select CustomerCity as rows and SalesTaxCollected as the Values. Create a pie chart of results to identify the cities where the largest sales taxes were collected. Step 4: Data risks: Incorrect sales tax rates, and duplicate or missing sales tax collected that may skew the results. The analysis risk involves whether proper calculations in the fields ExtendedSales and SalesTaxCollected were performed. Step 5: Data controls include verifying the data capture, extraction, and cleaning procedures and checking the data for any remaining duplicate or missing rows. The analysis controls would be to verify the accuracy of the calculations which created the ExtendedSales and SalesTaxCollected fields. 2. The solution file has been provided on Wiley’s online learning platform. The resulting PivotTable Row Labels Arvada Broomfield Denver Erie Fort Collins Johnstown Lafayette Longmont Loveland Northglenn Thornton Wheat Ridge
Sum of SalesTaxCollected 10848.68 3790.97 18292.20 4114.56 12357.84 1771.99 2629.78 8821.53 9306.30 3045.44 932.45 4560.00
Grand Total
80471.74
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Sales Tax by City Jurisdiction 932.45
4560.00
Arvada
3045.44
Broomfield 10848.68
3790.97
Denver Erie
9306.30
Fort Collins Johnstown
8821.53
18292.20
Lafayette Longmont Loveland
2629.78 1771.99
12357.84
Northglenn 4114.56
Thornton
The pie chart shows that the three municipalities with the most sales tax collected are Denver, Arvada, and Fort Collins, all cities in Colorado. LO 4.1, 4.2, 4.3, 4.4, Difficulty: Hard, TOT: 35 min., AACSB: Analytic, AICPA: Measurement Analysis and Interpretation
SOLUTIONS TO PROFESSIONAL APPLICATION CASE PAC 4.1 Accounting Information Systems: 1. Do all invoices greater than $10,000 have the Treasurer’s initials included in the AIS to indicate approval? 2. Include the following data fields: (Note: more data fields could be selected, but these are the minimum required). VoucherPackno InvoiceAmt Approved 3. First, filter the invoice data so all invoices greater than $10,000 are presented at the top of the spreadsheet. Keep only these fields and examine the data either included or missing in the “Approved” data field. No calculated fields would need to be created. 4.
Incomplete data: Are any invoices or voucher packages not included in the analysis? Data extraction errors: Were the data in these fields extracted properly and completely from the AIS?
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5. Improperly sorting the invoices, which could cause a failure to review if some invoices over $10,000 were approved or not. 6. Data controls could include doing a sequence check on the VoucherPackno field to determine if there were any missing vouchers, or any extraction errors. Analysis coltrols include verifying the data sorting first on VoucherPackno, and then on Approved data fields, and comparing the results. LO 4.1,4.2, 4.3, 4.4, Difficulty: Hard, TOT: 35 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation.
PAC 4.2 Auditing:
Objective Questions Objective: Identify purchase transactions for further testing Questions: Are there purchase transactions that may be considered anomalies?
Data and Analysis Strategies Data Strategy: InvoiceNo, Invoicedate, QtyInvoice, InvoiceAmt, VendorID, VendorDescription,
Analysis strategy: Perform descriptive statistics to understand total dollar amount and count of purchases made to each vendor
Risks
Controls
1. Data: ANS: Dirty data such as inaccurate data, incomplete data, missing data.
3. Data: ANS: Check for empty data fields, perform reasonableness tests on the fields, and spot check data with its source documents (a few invoices) for accuracy.
2. Analysis: ANS:
Frequency count errors Total purchases formula errors
Perform diagnostic Plotting the x-axis or statistics and create a y-axis data points scatterplot whereby incorrectly the InvoiceDate is on the x-axis and the InvoiceAmount is on the y-axis to identify any outlier
4. Analysis: ANS:
Verify the total invoice amount to the amount recorded in the company’s AIS or to the number of invoices in the accounts payable subsidiary ledger for the vendors most used.
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purchases. 5. Student answers will vary. There are many correct ways to complete this analysis. In the following example, we use Excel. Step 1: Open the data file provided. Select all the data including the column headers. Insert a table, name it “VendorDescription.”
Step 2: With your cursor anywhere in a table cell, insert a PivotChart > PivotTable and PivotChart. Select VendorDescription table as the input and a new worksheet as the output. Select the new PivotTable, and on the top menu, select PivotTable Analyze, and name the table “VendorTotalAmounts.” Then, select the chart and name it “TotalVendorAmounts”. Then, select and drag VendorName to the bottom PivotTable Fields area for Rows, and the InvoiceAmt to the area for Values. Format the PivotTable column cells results as currency. Name this workseet “5. Total Purchases by vendor”. Results look like this: Total Dollar amount of purchases by vendor Row Labels Ambassador Blue Big Texo Britton Parts, Inc. Component Parts Ltd. Die Cast Mart Hultont & Co. Loyciut Holdings LT Distribution Master Makers, Inc. Purple Supplier Quality Sparks Snail Quality Productions
Sum of InvoiceAmt $ 52,899 $ 41,117 $ 30,945 $ 43,946 $ 104,990 $ 30,960 $ 37,713 $ 44,742 $ 18,012 $ 53,803 $ 43,870 $ 22,619 4-22
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Data Analytics and Accounting: An Integrated Approach. $ $ $ $ $
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20,346 32,952 24,411 32,618 635,941
Total Purchases by Vendor 120,000
$ Total Purchases
100,000 80,000 60,000 40,000 Total 20,000 -
Vendor Names
Step 3: With your cursor anywhere in the VendorDescription table on the Data sheet, insert a new PivotChart > PivotTable and PivotChart. Select VendorDescription table as the input and a new worksheet as the output. Select the new PivotTable, and on the top menu, select PivotTable Analyze, and name the table “VendorCounts. Then select the chart and Name the chart “VendorActivityCounts”. Then, select and drag the VendorName to the area for Rows, and the InvoiceAmt to the area for Values. Using the pull-down arrow in this field, change the setting to Count rather than Sum. Name this worksheet “5. Invoice counts by vendor”. Results look like this: Total count of purchases by vendor
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Row Labels Ambassador Blue Big Texo Britton Parts, Inc. Component Parts Ltd. Die Cast Mart Hultont & Co. Loyciut Holdings LT Distribution Master Makers, Inc. Purple Supplier Quality Sparks Snail Quality Productions Stylk, Inc. T&Y Texas Parts V Logic Grand Total
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Count of InvoiceAmt 7 7 5 7 16 5 5 7 3 7 7 3 3 4 4 5 95
Count of Invoices
Total Count of Invoices by Vendor 18 16 14 12 10 8 6 4 2 0 Total
Vendor Names
6. In the Data sheet, select and copy the InvoiceDate and InvoiceAmt fields of data to a new worksheet, so that you are removing it from a PivotTable format, as scatterplots do not work within a pivot table. Select the data and insert a scattergraph chart (not a PivotChart). Edit the axis labels and the chart title. Name this worksheet “6. InvoiceAmt time scatterplot”. Results look like this:
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InvoiceAmt by Date
Invoice Amount
12,000.00 10,000.00 8,000.00 6,000.00 4,000.00 2,000.00 12/30/2020
1/4/2021
1/9/2021
1/14/2021
1/19/2021
1/24/2021
1/29/2021
2/3/2021
Invoice Date LO 4.1,4.2, 4.3, Difficulty: Hard, TOT: 35 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation.
PAC 4.3 Financial Accounting: 1. Which vendors have the highest dollar amount of purchases? Which vendors have the highest number of purchases during this period? 2. Additional data fields could be selected for analysis, but the minimum data fields include VendorID, VendorName, InvoiceAmt 3. Create a PivotTable by dragging VendorName to Rows and InvoiceAmt to Values. Change InvoiceAmt to “count.” 4.
Incomplete or inaccurate data Invoice amount is inaccurate VendorID or VendorName data is incorrectly keyed into the system.
5. Incorrectly grouping vendors because of a formula error, data errors, or incorrect totaling of amounts. 6. Data controls would include a comparison of data to source documents or compare to a payment report to identify completeness of data. Analysis controls would be a verification review of grouping formulas and reasonableness tests for results. LO 4.1,4.2, 4.3, 4.4, Difficulty: Hard, TOT: 35 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation.
PAC 4.4 Managerial Accounting: Answers will vary. 1.
Objective and Questions Objective:
Data and Analysis Strategies Data strategy:
Risks
Controls
1. Data: ANS:
3. Data: ANS:
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VendorID, VendorName, ReceivingNo, ReceivingDate, QualityRate,
Incomplete or inaccurate data
Questions: What is the average quality rating by vendor? What is the highest and lowest quality rating by vendor? How has the quality rating by vendor changed over time?
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Perform sequence and completeness checks on ReceivingNo, InvoiceAmt and VendorName. ANS: Compare data to source documents
Analysis strategy: Calculate the average QualityRating by VendorName. Calculate the minimum and maximum QualityRating by VendorName. Visualize how QualityRating by VendorName has changed over time.
2. Analysis: ANS: Incorrect calculations Inaccurate groupings of data. ANS: There are too many vendors to make a meaningful trend analysis visualization
4. Analysis: ANS: Review groupings. Review calculations. Evaluate results for reasonableness. ANS: Separate the qualityRating over time trend analysis into two visualizations, one for increasing quality ratings and another for decreasing quality ratings.
5. Calculate the average QualityRating by vendor: Step 1: Select the data including the labels in the Data sheet. Insert a table. Go to Table design, and name it “PurchasesData.” Select any cell in this table and Insert a PivotChart > PivotTable and PivotChart using PurchasesData as the input data and a new worksheet for the output.
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Step 2: Go to the new sheet, label it “5. Average Vendor QualityRating”. Similarly name the pivot table and pivot chart. Select the PivotTable. Select and drop the VendorName field to the Rows area and the QualityRate file to the Values area. Change the values setting to Average. Average Quality Rate Row Labels Ambassador Blue Big Texo Britton Parts, Inc. Component Parts Ltd. Die Cast Mart Hultont & Co. Loyciut Holdings LT Distribution Master Makers, Inc. Purple Supplier Quality Sparks Snail Quality Productions Stylk, Inc. T&Y Texas Parts V Logic Grand Total
Average of QualityRate 4.142857143 4.142857143 4.2 4.571428571 4.25 3.4 4.2 4.571428571 4 3.714285714 3.857142857 3.333333333 3.666666667 3.75 4 4.2 4.073684211
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Average Quality Rating
Average Quality Rating by Vendor 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0
Total
Vendor Name
Step 3: Select any cell in the Data sheet table and Insert a PivotChart > PivotTable and PivotChart using PurchasesData as the input data and a new worksheet for the output. Go to the new sheet, label it “5. Min and Max Quality”. Similarly name the PivotTable and PivotChart. Select the PivotTable. Select and drag the VendorName field to the Rows area. With the cursor in the pivot table cells so that the Pivot Table Fields screen appears on the right, drag the QualityRate field to the Values area. Change the values setting to Maximum. Repeat and change the setting. Repeat this step and change the value setting to Minimum. Next, copy the pivot table rows to the worksheet cells below the pivot table, for example, starting in cell A22, and then copy the maximum and minimum column results to the rows to the right of the copied pivot table rows. This will allow you to make a chart (not a pivot chart) with the names of the vendors, and the max and minimum QualityRate values. Select the entire new table and insert a chart. Format the chart title, and axis labels. The results should look similar to this: Row Labels Ambassador Blue Big Texo Britton Parts, Inc. Component Parts Ltd. Die Cast Mart Hultont & Co. Loyciut Holdings LT Distribution Master Makers, Inc.
Max of QualityRate
Min of QualityRate 5 5 5 5 5 5 5 5 5
2 3 3 3 2 2 3 3 3
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Purple Supplier Quality Sparks Snail Quality Productions Stylk, Inc. T&Y Texas Parts V Logic Grand Total
5 5 5 5 5 5 5 5
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3 2 2 3 3 3 3 2
Maximum and Minimum Quality Ratings by Vendor 6
Quality Ratings
5 4 3 2 1
Max of QualityRate
0
Min of QualityRate
Vendor Names
Step 4: Select any cell in the Data sheet table and Insert a PivotChart > PivotTable and PivotChart using PurchasesData as the input data and a new worksheet for the output. Go to the new sheet, label it “5. Increasing Quality A”. Similarly name the PivotTable and PivotChart. Select the PivotTable. Drag the VendorName field to the Columns area. Drag the ReceivingDate to the Rows. Drag the QualityRate file to the Values area. Change the values setting to Average. Go to the resulting PivotTable and select any of the dates listed. Right click and select Group. Select Days, and below, make the Number of days = 7. Select OK. Properly label the axis and chart title. In the resulting chart, select the vendor names that have increasing average quality rating trends from the first to the last point. We will move the decreasing quality rating vendors to another chart because the graph becomes less informative with too many vendors on the same graph.
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Increasing Quality Ratings by Vendor Average Quality Rating for the Week
6 5 4 Big Texo
3
Die Cast Mart
2 1
Loyciut Holdings
0
LT Distribution Master Makers, Inc. Purple Supplier T&Y
Week of Quality Rating
Step 5: Repeat Step 4, but name the new sheet, PivotTable, and PivotChart “Decreasing Quality B”. In the resulting chart, select the vendor names which have decreasing ratings (first versus last rating). Properly label the axis titles and the chart title. Separating these two charts should help management focus on the experiences of the decreasing rating vendors.
Average Quality Rating for the week
Decreasing Quality Rating Trends by Vendor 6 5 4 3 2 1 0
Ambassador Blue Britton Parts, Inc. Component Parts Ltd. Hultont & Co. Quality Sparks Snail Quality Productions Stylk, Inc. V Logic
Week of Quality Rating
LO 4.1,4.2, 4.3, 4.4, Difficulty: Hard, TOT: 35 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation.
PAC 4.5 Tax Accounting:
Objective and Questions
Data and Analysis Strategies
Risks
Controls
4-30
Dzuranin 1e
Objective: Understand the purchases made from each country. Questions: What dollar amount of purchases are made from each country?
How many purchases were made from vendors in each country?
Data Analytics and Accounting: An Integrated Approach.
Data strategy: ShipLocation, InvoiceAmt, Flatduty, TariffAmt, POnumber
Analysis strategy: Use a pivot table to drop the ShipLocation into the rows and the sum of InvoiceAmt into values. Sum purchases by country. Use a pivot table to drop the ShipLocation into the rows and count of PONumber into values. Count the PONumbers by country.
1. Data: ANS: Incomplete or inaccurate data (data that does not agree to source documents)
2. Analysis: ANS: Incorrect groupings and summation by country for InvAmount.
ANS: Incorrect counts for PONumber by country.
Chapter 4
3. Data: ANS: Do a sequence check on the PONumber. Check a few large Invoices o source documents to confirm reasonableness and completeness. 4. Analysis: ANS: Review country total calculations for Sum of purchases by country.
ANS: Review country total counts for PONumber by country.
LO 4.1,4.2, 4.3, 4.4, Difficulty: Hard, TOT: 35 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation.
4-31
CHAPTER 5 ANALYSIS: DATA PREPARATION Learning Objectives: LO 5.1 Explain the process of data profiling. LO 5.2 Describe the extract-transform-load (ETL) process. LO 5.3 Apply patterns to extract data. LO 5.4 Apply patterns to transform columns. LO 5.5 Apply patterns to transform tables. LO 5.6 Apply patterns to transform models. LO 5.7 Apply patterns to data loading issues.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. B
LO 5.4, BT: AP, Difficulty: Medium, TOT: 4 min., AACSB: Analytic, AICPA AC: Technology and Tools
LO 5.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
11. B
2. C
LO 5.4, BT:K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
LO 5.1, BT: AP, Difficulty: Easy TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
12. B
3. C
LO 5.4, BT: AP, Difficulty: Medium, TOT: 3 min., AACSB: Analytic , AICPA AC: Technology and Tools
LO 5.1, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
13. B
4. C
LO 5.5, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
LO 5.1, BT: K, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
14. D
5. D
LO 5.5, BT AP, Difficulty: Medium, Total 2 min, AACSB: Analytic, AICPA AC: Technology and Tools
LO 5.2, BT: K, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
15. B
6. C
LO 5.5, BT: AP, Difficulty: Medium, TOT: 3 min., AACSB: Analytic , AICPA AC: Technology and Tools
LO 5.2, BT: AP, Difficulty: Medium, TOT: 4 min., AACSB: Analytics, AICPA AC: Technology and Tools
16. D
7. D
LO 5.6, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
LO 5.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
17. C
8. C
LO 5.6, BT: AP, Difficulty: Hard, TOT: 3 min., AACSB: Analytic , AICPA AC: Technology and Tools
LO 5.3, BT: AP, Difficulty: Easy, Tot 3 min., AACSB: Analytic, AICPA AC: Technology and Tools
18. C
9. A
LO 5.6, BT: AP, Difficulty: Hard, TOT: 3 min., AACSB: Analytic , AICPA AC: Technology and Tools
LO 5.4, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools.
19. D
10. A
LO 5.7, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic , AICPA AC: Technology and Tools
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LO 5.7, BT: AP, Difficulty: Medium, Total 3 min, AACSB: Analytic, AICPA AC: Technology and Tools
20. B
ANSWERS TO REVIEW QUESTIONS 1. Data preparation is the process of profiling, cleaning, transforming, and integrating data to improve data quality and data structure. It is necessary to engage in data preparation prior to analyzing data because the data quality will affect the quality of insights and decisions made based on those insights. In addition, the structure of the data will determine how effectively the data can be analyzed. LO 5.1, BT: K, Difficulty: Easy, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
2. Two profiling techniques that can be used to identify inconsistencies in the data are:
Create a list of all distinct values, sort them, and identify and review items that mean the same thing. Build a frequency table, which is a table that counts how many times a value occurs. Values with a low frequency might indicate inconsistent data.
LO 5.1, BT: K, Difficulty: Easy, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
3. The three subprocesses of data transformation are cleaning the data, restructuring the data, and integrating the data. Cleaning the data is characterized by adding, modifying, and deleting data. Restructuring the data, also known as data wrangling or data munging, changes how data are organized but does not change the data values. Data integration is the process of connecting related data. There are two key forms of data integration: linking and combining. LO 5.2, BT: K, Difficulty: Easy, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
4. Linking two tables is done by defining relationships between the two tables. These relationships are defined using primary and foreign keys. Combining tables unites information regarding the same entity. Tables are combined through a union or a merge. LO 5.2, BT: K, Difficulty: Easy, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
5. Data matching is a process that compares similar data across multiple columns to link them together, and thus integrate. Data matching can face a variety of issues. For example, when matching names you might have to deal with nicknames, typos, and reversed names. LO 5.2, BT: K, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
6. A data dictionary is a chart of data that describes what data are available and where they can be found. It includes several pieces of information for each field, including field name, a brief description of the field's contents, datatype, whether the field is a primary or foreign key, and whether the field is mandatory and does not allow null values. LO 5.3, BT: K, Difficulty: Easy, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
7. While each data analytics project has unique data preparation challenges, data preparation patterns can help identify most issues and provide guidance for detecting and correcting these data issues. LO 5.3, BT: K, Difficulty: Easy, TOT: 3 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
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8. When transferring data, we can compare the row count that is shown in the status bar of the Excel worksheet with the Column Profile statistics (count) generated in Power Query LO 5.3, BT: K, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
9. The data type of whole number is used to denote data fields that are numeric and can be used in arithmetic functions, including sum, average, minimum, maximum, standard deviation, etc. The text field data type denotes data that are alphanumeric characters that cannot be used in arithmetic functions. LO 5.4, BT: K, Difficulty: Easy, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
10. A composite column contains information regarding two or more characteristics of the same entity. For example, when the name (characteristic) and loyalty status (characteristic) of a customer (entity) appear in the same column. A multi-valued column contains multiple values of the same characteristic. Examples are the different (multiple) certifications (characteristic) of an employee. LO 5.4, BT: K, Difficulty: Medium TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
11. An initial technique to detect inconsistent values is visually scanning the distinct values in a column. A second technique is to use a frequency table to determine if any of the distinct values have a low frequency. If there are inconsistent values, identify the cause of the inconsistent data, modify the values in the source data, or modify the values in the analytical database. An example of inconsistent data would be if some states are listed with their full state name, e.g., California, while others are listed as abbreviations, e.g., CA. LO 5.4, BT: K, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
12. Redundant data among columns in an analytical database can create data inconsistencies. For example, recording a customer's full address and recording the customer's state separately. LO 5.5, BT: K, Difficulty: Easy, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
13. Primary keys uniquely identify the instances in a table. Therefore, their values should be unique and not null. If a primary key is missing, we cannot create relationships between the tables in the analytical database, which will significantly limit the analyses that can be performed. To identify a primary key, examine the column or combination of columns that should be the primary key to confirm no empty values (null) exist and that the count of the column’s cells and the count of its distinct values are the same (uniqueness). If the column (or combined columns) does not meet both criteria, a primary key must be created. One way to do that is to create a new column with an artificial key, such as a number. LO 5.5, BT: K, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
14. Dimensional modeling is a method for defining the structure of a data set in terms of facts and dimensions. Facts are typically numeric values that are analyzed , whereas dimensions are the variables or groups in which the numbers are split. It is important to comply with dimensional modeling principles when building an analytical database to increase understandability and data processing efficiency. LO 5.6, BT: KK, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
15. A union combines two or more tables with the same structure. A join or merge combines two or more tables that describe different characteristics of the same entity. A union cannot be used if the number of columns among the tables is different or when combining columns with different data types. LO 5.6, BT: K, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
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16. A single-valued column has only one value in a cell. That characteristic makes aggregation and filtering easy. Composite and multi-valued columns are much harder to process. For example, listing all certifications for an employee in one cell—e.g., CPA, CMA, CIA--, requires additional effort to determine whether the employee has a specific certification, e.g., CPA. A flat table is one in which column headers do not contain data values useful for analysis. For example, if columns have headers such as 2023, 2024, 2025, then that information cannot be used for filtering purposes. However, if these values become cells in a Year column of a flat file, then aggregation and filtering are easy. LO 5.6, BT: K, Difficulty: Easy, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools
17. Issue
Detection
Correction
A table in your analytical database does not have a primary key.
ANS: Identify null values and check whether the count of all cells and the count of all distinct values are equal.
ANS: Create a primary key that adds a unique value for each instance in the table.
A column in the fixed assets table has a ANS: Review columns to column for date acquired identify overlapping (DateAcquired) and the asset age content among columns. (AssetAge). Age represents how long the asset has been held since the date acquired.
ANS: The AssetAge column should not be transferred from the data source. It should be calculated in the analytical database.
A table with vendor information is named VINFO.
ANS: Visually scan tables for incorrect or ambiguous names.
ANS: Rename table: VendorInformation
Invalid data might have been entered into one of the tables in the database.
ANS: Create and apply ANS: Modify invalid data in intra-table validation rules. source or analytical database.
LO 5.6, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
18. Transforming patterns at the column level pertain to individual columns. Examples include changing a column header or data type. Transformation patterns at the model level look for data issues across tables. An example is combining data from different tables using a union or merge. LO 5.6, BT: K, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
19. Prior to analyzing data, it is important to validate that all data relationships have been defined and that they have been defined correctly. Missing or incorrectly defined data relationships make analytics challenging or even impossible. LO 5.7, BT: K, Difficulty: Easy, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
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20. Even if we confirm that all the data have been transferred into an analytic database, we must also confirm that they were transferred correctly. Incorrect data might result in poor decision-making. The correctness of the data transfer can be checked by calculating and comparing control amounts for the source data and for the data in the analytical database. LO 5.7, BT: Knowledge, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
SOLUTIONS TO BRIEF EXERCISES BE 5.1
LO 5.1, BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 5.2 1. b 2. a 3. c 4. e
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LO 5.1, BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 5.3 Student answers will vary. These are some issues they might identify:
Ambiguous description: The date column might represent a customer's birthday. However, we cannot be sure. Is it possible for some of our customers to be born in 2008 or 2009? CustomerInfo is not a single-valued column: It combines Customer Name and Street/address information. Consistency: The data entered for Gender is inconsistent. Female is represented by two different values: "Female" and "F." The same problem exists for Male. Validity: The email data is not valid for each entry. In some instances, the email does not include the @ symbol. Completeness: For at least two of the entries, all the data was not recorded, and the record is incomplete. Correctness: The name of the first customer, “Steve Millier” is most likely incorrect given that his email address is “steve.miller." Alternatively, the email address might be incorrect.
LO 5.1, BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 5.4 There are several challenges to integrating the data in Table A and Table B. These include:
Reversed descriptions: o In Table A the description is "Blue Dog Collar." The same product in Table B has the description "Dog Collar – Blue." o In Table A the description is "10 Inch Rope Sturdy Lead – Green." The same product in Table B has the product description "Green 10 Inch Rope Sturdy Lead." Typo: In Table A the description is “Red Doc Collar," which is a typo, and the product is correctly named in Table B as "Red Dog Collar." Abbreviation: In Table A the description is "10-inch Rope Sturdy Lead – Black.” Table B's product description uses an abbreviation in the product description "10 Inch Rope Sturdy Lead – Bk."
LO 5.2, BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 5.5 In panel (A), the group name is repeated for all customers in the same group. In panel (B), group becomes its own entity, and the name of a group is recorded only once. The data model in panel (B) saves space at the price of complexity, given that there is an extra relationship—the relationship between Group and Customer—that must be considered during analytics. LO 5.2, BT: AP, Difficulty: Medium TOT: 7 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 5.6 Name
Description
1. ANS: a. SaleID
Unique identifier for each sale made.
Date
2. ANS: g. The date the sale was made.
SaleLocation
3. ANS: f. The location where the sale was made
4. ANS: b. SaleAmount
The total amount of the sale before taxes
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5. ANS: d. LocalTax
The percentage of local taxes collected at the time of the sale
SalesTax
6. ANS: e. The percentage of federal sales taxes collected at the time of the sale.
7. ANS: c. Vat
The value-added-tax percentage to be collected related to the sale
LO 5.3, BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
BE 5.7 Scenario
Data Quality Issue
1. Grace received a data set that had a column titled ClientInfo. Each record in that field contained a client's mailing address.
ANS: a. The column names are incorrect or ambiguous.
2. The data in the Sales column are the dollar amounts of the sale transactions. However, Quinn noted that the data type was classified as alphanumeric characters or text data.
ANS d. The columns do not have the correct data type.
3. Upon inspection of data transferred from the payroll system to a data warehouse, Molly noticed that employee payroll records only included employees with last names beginning A through S.
ANS e. All the data have not been transferred.
4. Barnes received a table of data from the IT group. One of the columns contains both customer name data and payment term data.
ANS b. All columns are not single-valued.
5. Devon extracted data from the company's shipping department log. In addition to data regarding shipments, the log also contains summary statistics such as the total quantity shipped. Upon inspection of the extracted data, Devon notes that the total quantity shipped for the extracted data does not match the "total quantity shipped" number in the log.
ANS c. All the data have not been transferred correctly.
LO 5.3,4 BT: AP, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 5.8 Power Query / Power BI is used as the ETL tool for this solution. The solution steps should be similar when another ETL tool is used. Extract the data and open Power Query:
Transfer the data from the text file to Power BI. Click Get data in the Data group in Power BI’s home ribbon. Select Text/CSV and click the Connect button. Extract the data from the text file by opening the file, then click Transform data. The Power Query editor will open. In the View tab of the Power Query editor, select Column Profile in the Data Preview group.
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1. Data Preparation Pattern 1 is applied. Select the ID column. The Column statistics in the lower half of the screen (part of the column profile) shows that 34 rows, and thus all the transactions, have been transferred: “Count=34”.
2. Data Preparation Pattern 2 is applied. Click on the TotalCost column. The Column statistics in the lower half of the screen (part of the column profile) show that the average total cost per product is $610.98 and thus the data have been transferred correctly.
LO 5.3, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Technology, AICPA AC: Technology and Tools.
BE 5.9
Column
Data Type
CustomerID
1. ANS: e. Text
CustomerName
2. ANS: e.Text
Gender
3. ANS: e.Text
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DateOfBirth
4. ANS: a. Date
Rewards
5. ANS: g. Whole Number
YTDSales
6. ANS: c. Decimal number
NumberOfAppointmentsScheduled
5. ANS: g. Whole Number
LO 6.4, BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Technology, AICPA AC: Technology and Tools.
BE 5.10 Power Query / Excel is used as ETL tool in this solution. The solution steps should be similar when another ETL tool is used. Extract the data:
Transfer the data from the csv file to Excel. Use the Data tab in Excel’s main menu and click on From Text/CSV in the Get & Transform Data group. Open the file (Import) and click Load. Next, to open Power Query, go to the Data tab, select the down arrow next to Get Data in the Get & Transform Data group, and click Launch Power Query Editor. In the Query Settings pane, delete the Changed Type Applied step.
1. Data Preparation Pattern 5 is applied. Click on the ABC (text) data type next to the CustomerID column name:
Change the data type to whole number (123). If a Change Column Type appears, select Add New Step. The column will now contain some errors and the column quality bar (the bar underneath the column header) is now partially red:
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Power Query provides some statistics when the user hovers over the column quality bar: 14 rows—i.e. 70% of the rows—contain errors. The same information is available after clicking the View tab in the main menu. Select Column profile (click the check box), and then CustomerID column.
2. Data Preparation Pattern 7 is applied. Power Query’s column profile function shows what the highest (or maximum) number of rewards is that a customer has earned. Click on the Rewards column and the Column profile function will generate the following statistics:
The highest value for number of rewards is 9.This information might be helpful when looking for incorrect data (data preparation pattern 7).
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3. Data Preparation Pattern 7 is applied. Power Query’s column profile function also displays a Value Distribution Chart that can help identify incorrect or inconsistent data. If you click on the AppointmentDuration column, the following Value distribution chart is generated:
The low frequency (1) of the value 12 might indicate that it is incorrect. Most likely it should be 120. When looking for incorrect data, apply data preparation pattern 7. LO 5.4, BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Technology, AICPA AC: Technology and Tools.
BE 5.11
Data Preparation Pattern 11: Table names should be correct, unambiguous, and intuitive. SalesInfo is correct, unambiguous, and intuitive. Data Preparation Pattern 12: Every table should have a primary key: i.e., a column that has a unique value for each instance and no null values. The table currently does not have a primary key. None of the columns contain unique values. This issue can be addressed by creating an artificial key consisting of unique sequential numbers. Data Preparation Pattern 13: There is no redundant content among columns in the table. Data Preparation Pattern14: There are two intra-table rules that should be tested: 1. For Cash sales, the Customer field should be blank. This rule is applied correctly. 2. A coupon can only be considered when no discounts apply. This rule is not always applied correctly.
More specifically, for the sale with InvoiceNo “1206,” there is a discount and a coupon. LO 5.5, BT: AP, Difficulty: Hard, TOT: 8 min., AACSB: Technology, AICPA AC: Technology and Tools.
BE 5.12 1. SalesInfo better describes the overall content of the table, not just two of the columns. 2. The table does not have a primary key; i.e., a field with no empty cells and a unique value for each row. To address this issue, create an artificial key that sequentially numbers sales: SalesID. LO 5.5, BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Technology, AICPA AC: Technology and Tools.
BE 5.13 Power Query / Excel is used as the ETL tool for this solution. The solution steps should be similar when another ETL tool is used. Step 1: Create a new Excel file called EmployeeInfo, and import the data from both csv files:
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Transfer the data from the csv files to Excel. Use the Data tab in Excel’s main menu and select From Text/CSV in the Get & Transform Data group. Import the Employee.csv file and select Load. Do the same for the EmployeePerformance file.
Step 2: To open Power Query, go to the Data tab in Excel’s main menu. Select the down arrow next to Get Data in the Get & Transform Data group and click Launch Power Query Editor. Step 3: To do some basic cleaning in Power Query, it may be necessary to apply Use First Row as Headers (in the Transform ribbon) to both the Employee and EmployeePerformance tables. The Employee table should look as follows:
The EmployeePerformance table should look as follows:
Step 4: Merge the two tables by selecting the Employee table in the Queries pane:
In the Home ribbon, select Merge Queries and Merge Queries as New. Select the table to join with Employee: EmployeePerformance. The Merge window should now look as follows:
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To merge the two tables, choose the LastName and FirstName fields in both tables. Use the Ctrl key to select multiples fields/columns in the same table. The information at the bottom of the Merge window shows there is a perfect match between the employee names:
Clic OK, and a new table is created called “Merge1”.
Rename this table as “EmployeeInfo.” The new table contains all fields from the Employeetable and an EmployeePerformance column. All values in the EmployeePerformance column show Table. Click on the right upper corner of the column. Power Query will let you select any column from the EmployeePerformance table and add it to the EmployeeInfo table. Select the Level, 2024Hours, and RatePerHour fields. Make sure to leave the Default column name prefix (optional) field blank. Click the OK button.
At this point, you have successfully merged the two tables into one. Close Power Query by clicking Close & Load in the Home ribbon. LO 5.6, BT: AP, Difficulty: Hard, TOT: 8 min., AACSB: Technology, AICPA AC: Technology and Tools.
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BE 5.14 This solution uses Power Query / Excel as the ETL tool. The solution steps should be similar when another ETL tool is used. Step 1: Create a new Excel file (HoneyBeesTransactions) and import the data from both csv files:
Transfer the data from the csv files to Excel. Use the Data tab in Excel’s main menu and click on From Text/CSV in the Get & Transform Data group. Import the HoneyBeesDay1 file and click Load. Do the same for the HoneyBeesDay2 file.
Step 2: Next, to open Power Query, go to the Data tab in Excel’s main menu. Select the down arrow next to Get Data in the Get & Transform Data group. Click Launch Power Query Editor. Step 3: Do some basic cleaning up in Power Query:
When combining two or more tables using a Union (or Append), it is important that they have the same structure. Check whether matching columns have the same name, data type, and type of content. In the HoneyBeesDay1 table, the fifth column is Customer and the sixth column is Server. In the HoneyBeesDay2 table, the fifth column is Server and the sixth column is Customer. Rearrange the order of the columns in the HoneyBeesDay2table: select the Customer table and drag it before the Server table.
Step 4: Use Append to combine both tables:
Select the HoneyBeesdDay1 table in the Queries pane. Next, in the Home ribbon select Append Queries and Append Queries as New. In the Append window, select the table to add to the HoneyBeesdDay1 table: HoneyBeesdDay2.
Power Query then creates a new table called Append1. Rename it “HoneyBeesTransactions”. In the Home ribbon, click Close & Load. LO 5.6, BT: AP, Difficulty: Hard, TOT: 8 min., AACSB: Technology, AICPA AC: Technology and Tools
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.
BE 5.15 1. If the actual time to complete a job is more than double the budgeted time, then the situation should be explored further (red). 2. Red might indicate data anomalies in the actual time, the budgeted time, or both. It might also simply indicate a job that took way too much time. LO 5.6, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Technology, AICPA AC: Technology and Tools.
BE 5.16
The relationship between Delivery and PurchaseOrder is defined between the DeliveryNo field in the Delivery table and the ID field in the PurchaseOrder table. While both fields contain numbers, the wrong numbers are being matched, which results in incorrect data. The relationship should be defined between the PurchaseOrder field in the Delivery table and the ID field in the PurchaseOrder table. The relationship between PurchaseOrder and Vendor is defined between the Vendor field in the PurchaseOrder table and the Name field in the Vendor table. The Vendor field in the PurchaseOrder table contains the vendor ID. Therefore, there are no matching values. The relationship should be defined between Vendor in the PurchaseOrder table and ID in the Vendor table (not name).
LO 5.7, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
SOLUTIONS TO EXERCISES EX 5.1 Power Query / Excel is used as the ETL tool in this solution. The solution steps should be similar when another ETL tool is used. Step 1: Create a new Excel file, name it (e.g., LoyaltyData) and import the data from the Text/CSV file. Transfer the data from the csv file to Excel.
In the Data tab in Excel’s main menu, click From Text/CSV in the Get & Transform Data group. Import the data from the text file by opening it, and click Load.
Step 2: Next, to open Power Query, go to the Data tab in Excel’s main menu, select the down arrow next to Get Data in the Get & Transform Data group. Click Launch Power Query Editor. Step 3: To do some basic cleaning up, in the Queries pane rename the table “LoyaltyData.” The data set looks as follows:
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1. Remove duplicate entries: Step 4: In Power Query, select all columns in the LoyaltyData table:
In the Home tab, click the down arrow next to Remove Rows in the Reduce Rows group. Then select Remove duplicates. As shown in the table, duplicate rows are removed, and the number of rows is reduced from 11 to 9.
2. Address data inconsistencies. The Gender column mixes representations of gender values: Female and F, and Male and M: Step 5: Replace all F values by Female and replace all M values by Male.
Select the Gender column. In the Transform ribbon of Power Query, select Replace Values in the Any Column group. In the Replace Values window, enter “F” in the Value to Find field and “Female” in the Replace With field. Under Advanced options click Match entire cell contents. Repeat to replace M with Male.
(Note that this is an application of Data Preparation Pattern 8.) 3. Address incorrect and ambiguous column names. The Date column contains the customers’ birthday. To better reflect the content of this column, replace “Date” with “DOB”: Step 6: In Power Query, double click on the Date column header and enter “DOB”. Note that this is an application of Data Preparation Pattern 4.
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4. Address composite column issues. The CustInfo column mixes customer name and customer address (street) information. Mixing two or more characteristics in one column makes analysis more difficult. Transform the CustInfo column into two single-valued columns: Step 7: In Power Query, select the CustInfo column.
Next, in the Home ribbon in the Transform group, click the down arrow next to Split Column and select By Delimiter. In the Split Column by Delimiter window, select Comma in the field beneath Select or enter delimiter and then select Left-most delimiter beneath Split at.
(Note that this is an application of Data Preparation Pattern 6.)
The LoyaltyData table should now look as follows:
Replace the header of the first column, CustInfo.1 with “Name” and replace the header of the second column, CustInfo.2 with “Street.”
The result of the different transformation steps is shown next:
In Power Query’s Home ribbon, click Close & Load.
Note that additional transformations might be needed for this data. For example, Libby Monoyer was born in 2956, and Steve Miller’s email address does not include a @ and is therefore incorrect. LOs 5.1, 5.2, 5.4, 5.5 BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.2 Power Query / Excel is used as the ETL tool for this solution. The solution steps should be similar when another ETL tool is used. Step 1: Create a new Excel file (Bargain) and import the data from the three CSV files. Transfer the data from the first CSV file to Excel.
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Use the Data tab in Excel’s main menu and click From Text/CSV in the Get & Transform Data group. Import the data from the EX 5.2 data file by selecting the file and by clicking Load. Repeat the same steps for the two other CSV files.
Step 2: Next, to open Power Query, go to the Data tab in Excel’s main menu.
Select the down arrow next to Get Data in the Get & Transform Data group. Click Launch Power Query Editor.
Step 3: To do some basic cleaning up, in the Queries pane rename the tables as “USVendors,” “CanadiaVendors,” and “Purchases.” For all three tables, in the “Transform ribbon, select Use First Row as Headers. Step 4: Address incorrect and ambiguous column names. The header of the first column in the Purchases table is ambiguous: LIID. Change it to “LineItemID.” In the same table, replace “Ord#” with “OrderNo”. (Note that this is an application of Data Preparation Pattern 4.) Step 5: Address incorrect, inconsistent, incomplete . and invalid values. Inspect each column's content. Note that the Date column in the Purchases table contains a date in 2055, which is a typo and should be changed to “2025.”
Select the Date column. In the Transform ribbon of Power Query, select Replace Values in the Any Column” group. In the Replace Values window, enter “1/3/2025” in the Value to Find field and “1/3/2025” in the Replace With field.
(Note that this is an application of Data Preparation Pattern 4.) Step 6: Ensure that all columns are single-valued. Inspect each column’s content. Note that the ItemCategory column contains both the name of the item purchased and its category. Mixing two or more characteristics in one column makes analysis more difficult.
In Power Query, select the ItemCategory column. Next, in the Home ribbon in the Transform group, click the down arrow next to Split Column, and select By Delimiter. In the Split Column by Delimiter window, select Comma in the field beneath Select or enter delimiter. Then select Left-most delimiter beneath Split at. Replace the header of the first column, ItemVategory.1 with “Type” and replace the header of the second column, ItemCategory.2 with “Category”.
(Note that this is an application of Data Preparation Pattern 6.) This completes the data transformation of the Purchases table. Step 7: Combine split tables. The USVendors table contains data regarding US vendors. The CanadianVendors table contains data regarding Canadian vendors. Both tables have the same structure and should be combined into one table called Vendors.
In the Power Query editor, select the USVendors table in the Queries pane. Next, in the Home ribbon select Append Queries and Append Queries as New”. In the Append window, select the table you would like to add to the USVendors table: CanadianVendors. Power Query creates a new table called Append1. Rename it “Vendors”.
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(Note that this is an application of data preparation pattern 15.) This completes the transformation of the Vendor tables. In the Home ribbon, click Close & Load. LOs 5.1, 5.2, 5.4, 5.6 BT: AP, Difficulty: Hard, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.3. Power Query / Excel is used as the ETL tool in this solution. The solution steps should be similar when another ETL tool is used. Step 1: Extract the data. Create a new Excel file (ElAzteco) and import the data from the file.
Use the Data tab in Excel’s main menu and click on From Text/CSV in the Get & Transform Data group. Import the file by opening it and click Load.
Step 2: Open Power Query.
Go to the Data tab in Excel’s main menu and select the down arrow next to Get Data in the Get & Transform Data group. Click Launch Power Query Editor. In the Queries pane, rename the table as “Sales.” In the Transform ribbon, select Use First Row as Headers.
Step 3: Ensure that all columns are single-valued. All data elements recorded for sales transactions are recorded in one column: SalesInformation. Mixing two or more characteristics in one column makes analysis more difficult. It is nearly impossible to answer the questions listed with the current data structure.
In Power Query, select the SalesInformation column. Next, in the Home ribbon in the Transform group, click the down arrow next to Split Column, and select By Delimiter. In the Split Column by Delimiter window, select Comma in the field beneath Select or enter delimiter and then select Each occurrence of the delimiter beneath Split at. Change the column headers as follows:
Current Name
New Name
SalesInformation.1
ID
SalesInformation.2
SaleDate
SalesInformation.3
Amount
SalesInformation.4
Discount
SalesInformation.5
LoyaltyDiscount
SalesInformation.6
LoyaltyNumber
(Note that this is an application of Data Preparation Pattern 6.)
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Step 4: Ensure that all data have been transferred and transferred correctly:
In the View tab of the Power Query editor, click the check box before Column Profile in the Data Preview group. Select the ID column. The Column Profile statistics (Count) shows that all 10 transactions have been transferred. Next select the Amount column. The Column Profile statistics (Average) shows that the average dollar amount per transaction is 212.1.
This matches the amount given by Fernando, which indicates that the data have been transferred accurately. The SalesInformation table should now look as follows:
(Note that this is an application of Data Preparation Patterns 1 and 2.) In the Home ribbon, click Close & Load. Step 5: Identify invalid values with intra-table rules. The data dictionary contains the following intratable rule: “Only customers enrolled in El Azteco’s loyalty program can receive a loyalty discount.” The validation of this rule can be done by the following Excel formula (Entered in cell G2): =IF(AND(E2>0,ISBLANK(F2)),1,0) The first argument of the IF function is a logical expression. Here, look for sales transactions for which both (AND) a loyalty discount is given but there is no value (ISBLANK) for LoyaltyNumber. Stated differently, look for sales transactions where a loyalty discount is given to a customer who is not enrolled in the loyalty program. If the logical expression is true, then the value “1” is added, otherwise the value “0” is added. Applied to all sales transactions, the Sales table looks as follows:
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For transaction #9, a loyalty discount of 1% was given to a customer who is not enrolled in the loyalty program, or at least the loyalty number for the customer was not entered. (Note that this is an application of Data Preparation Pattern 10.) LO 5.1., 5.2., 5.4, 5.5, 5.7 BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.4 1.
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2.
Data Exploration Pattern 4: Column names become a vocabulary during analysis. Therefore, they should be correct, intuitive, and unambiguous. Names such as Ccode, Cname, Doh, LiID, etc. are not intuitive and should be replaced. In addition, avoid using the same column name across tables—e.g., CustomerName and SalesPersonName. Data Exploration Pattern 6: Multi-valued columns make analysis more complex. The data dictionary indicates that Region is a multi-valued field: “The different regions in which a salesperson operates.” We therefore created a new table, RegionInfo, with a single-valued SalesPerson column. By defining this extra table, we created a snowflake data model. Data Exploration Pattern 20: The snowflake schema breaks the data down in five tables. These tables need to be connected using the primary key-foreign key mechanism. Product, Customer, and Salesperson are foreign keys in the Sale table.
LO 5.4., 5.5, 5.6., 5.7 BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.5 Power Query / Excel is used as the ETL tool in this solution. The solution steps should be similar when another ETL tool is used. Step 1: Extract the data. Create a new Excel file (DepreciationExpenses) and import the data from the two CSV files.
Use the Data tab in Excel’s main menu and click From Text/CSV in the Get & Transform Data group. Import by selecting the first CSV file and click Load. Repeat the same steps for the Second CSV file.
Step 2: Open Power Query:
Go to the Data tab in Excel’s main menu. Select the down arrow next to Get Data in the Get & Transform Data group and click Launch Power Query Editor.
Step 3: Identify irrelevant and unreliable data:
Both tables contain a column that seems to be irrelevant for verifying depreciation expenses: AssetColor in the DepreciationUSA table and Purchaser in the DepreciationMexico table. Note that “Purchases” is used instead of “Purchaser” as column header in the DepreciationMexico table. No correction is needed since we are deleting the column. In Power Query, a column can be removed by selecting it and pressing the Delete key. (If needed, the data can be recovered at a later stage.)
(Note that this is an application of Data Preparation Pattern 3.) Step 4: For analytical purposes, the DepreciationUSA and DepreciationMexico” tables should be combined. This requires that both tables have the same structure. Notice that the DepreciationExpense and AccumulatedDepreciation columns are in a different order. Reverse the order of these two columns in the DepreciationUSA table. Combine tables with a similar structure:
In the Power Query editor, select the DepreciationUSA table in the Queries pane. Next, in the Home ribbon, select Append Queries and Append Queries as New. In the Append window, select the table you would like to add to the DepreciationUSA table: DepreciationMexico.
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Power Query creates a new table called Append1. Rename it “Depreciation”. The Depreciation table should look as follows:
In the Home ribbon, click Close & Load.
(Note that this is an application of Data Preparation Pattern 15.) Step 5: Identify invalid values:
Several validation rules can be considered for the Depreciation table, including “The AccumulatedDepreciation amount should not exceed the AssetPurchasePrice amount”. In Excel, this rule can be implemented as follows: =IF(F2>D2,1,0)
IF the AccumulatedDepreciationAmount (F2) of AssetNumber US783498 (row 2) is greater than AssetPurchasePrice (D2), then assign the value 1 to this cell, otherwise assign the value 0. The value for this test is 1 for the asset with number M92-492, indicating an invalid value. Explore why the number is invalid—is there a typo, incorrect calculation, etc.?
(Note that this is an application of Data Preparation Pattern 14.) LO 5.1., 5.2., 5.4, 5.5, 5.6 BT: AP, Difficulty: Hard, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.6 1. The ProductCode column has two main issues. First, it is a combined column that contains four characteristics: model, color, year, and special features. Each should be split into a separate column. Second, the special features characteristic is multi-valued and should be transformed into a column with single values. 2. The following steps outline how these issues can be addressed. Power Query / Power BI is used as the ETL tool in this solution. The solution steps should be similar when another ETL tool is used. Step 1: Extract the data by creating a new Power BI file—DunnMotors—and import the data from the CSV file.
In the Home tab, select Get data in the Data group. Then select Text/CSV and click Connect. Select the CSV file and click Load.
Step 2: To open Power Query, select Transform data in the Queries group of the Home tab. There is one table in the Queries pane: DunnMotors. Rename it as “Products.”
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Step 3: Split the Product column:
In Power Query, select the Product column in the Products table. Next, in the Home ribbon in the Transform group, click the down arrow next to Split Column, and select By Delimiter. In the Split Column by Delimiter window, select Custom in the field beneath Select or enter delimiter. Enter “-“, and select Each occurrence of the delimiter beneath Split at. The Products table now looks as follows:
Rename Product.1 as “Model,”Product.2 as “Color,” Product.3 as “Year,” and Product.4 as “Features.” Next, further split the Features column. Follow the steps above but use “*” as delimiter. Delete the Features.1 and Features.3 columns and rename the Features.2 column as “Features.”
(Note: to get the correct results it might be necessary to delete the “Changed Type2” step.) Step 4: Create a features table:
In the Queries pane, select the Products table. Right click and select Duplicate. Rename the new table “Features.” Delete the Features column from the Products table. In the Features table, delete all columns except for “ID” and “Features”.
Step 5: Create a single-valued Feature column:
In the Features table, split the Features column using “,” as delimiter. Next, select the ID column. In the Transform ribbon, in the Any Column group, click the down arrow next to Unpivot Columns and select Unpivot Other Columns. The Features table should now look as follows (only a sample is shown):
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Delete the Attribute column and rename the Value column as “Features”.
You have now transformed the multi-valued Features column into a single-valued Features column. LO 5.1., 5.4 BT: AP, Difficulty: Hard, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.7 Power Query / Excel is used as the ETL tool in this solution. The solution steps should be similar when another ETL tool is used. Step 1: To extract the data, create a new Excel file—Hikko—and import the data from the CSV file.
Use the Data tab in Excel’s main menu and click From Text/CSV in the Get & Transform Data group. Select the CSV file and click Load.
Step 2: Open Power Query by going to the Data tab in Excel’s main menu.
Select the down arrow next to Get Data in the Get & Transform Data group. Click Launch Power Query Editor.
Step 3:The columns other than Region represent a specific period—a quarter in a year. These columns can be considered as a multi-valued list of periods. It is difficult to perform an analysis across periods given that the period information is represented as column-headers. Unpivot the Different Period Columns:
Select the Region column. In the Transform ribbon, in the Any Column group, click the down arrow next to Unpivot Columns and select Unpivot Other Columns. Rename Attribute as “Period” and rename Value as “Sales”. The transformed table should look as follows (only the first 10 rows are shown):
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Step 4: To make analysis across years and across quarters easier, split the period column.
In Power Query, select the Period column. Next, in the Home ribbon, in the Transform group, click the down arrow next to Split Column, and select By Delimiter. In the Split Column by Delimiter window, select Colon in the field beneath Select or enter delimiter, and select the Left-most delimiter option. Rename Period.1 as “Year” and Period.2 as “Quarter.” Your table should now look as follows:
With this structure it should be easy to compare revenues across regions, quarters, and years. LO 5.2., 5.5, 5.6 BT: AP, Difficulty: Difficult, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.8 Power Query / Excel is used as the ETL tool in this solution. The solution steps should be similar when another ETL tool is used. Step 1: To extract the data, create a new Excel file—Wilkinson—and import the data from the first CSV file.
Use the Data tab in Excel’s main menu and click From Text/CSV in the Get & Transform Data group.
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Select the CSV file and click Load. Repeat the same steps to add the data in the in second CSV file to the Wilkinson file.
Step 2: Open Power Query by going to the Data tab in Excel’s main menu.
Select the down arrow next to Get Data in the Get & Transform Data group. Click Launch Power Query Editor.
Step 3: The last three columns in the WilkinsonWeek1 table represent days and can be considered a multi-valued list. It is difficult to perform an analysis across weekdays given that the period information is represented as column-headers. Restructure the individual tables:
Select the Id, Employee, JobNo, and WeekNo columns. In the Transform ribbon in the Any Column group, click the down arrow next to Unpivot Columns and select Unpivot Other Columns. Rename Attribute as “Day” and rename Value as “Hours”.
The Day column should be split further into WeekDay and Day:
In Power Query, select the Day column. Next, in the Home ribbon in the Transform group, click the down arrow next to Split Column, and select By Delimiter. In the Split Column by Delimiter window, select Colon in the field beneath Select or enter delimiter, and select the Left-most delimiter option. Rename Day.1 as “Day.” Delete Day.2. This is a redundant column since its information can be derived from the “Day” column. Repeat the same step for the WilkinsonWeek2 table.
The WikinsonWeek1 table now looks as follows:
(Note that this is an application of Data Preparation Patterns 6 and 13.) Step 4: Combine the WilkonsonWeek1 and WilkonsonWeek 2 tables:
Select the WilkonsonWeek 1 table in the Queries pane. Next, in the Home ribbon, select Append Queries and Append Queries as New. In the Append window, select the table you would like to add to the “WilkonsonWeek 1” table: “WilkonsonWeek 2”. Rename the new table “Weeks.”
It becomes easy to determine labor hours (cost) by job, employee, date, weekday, week, etc. (Note that this is an application of Data Preparation Pattern 15.) LO 5.1., 5.4, 5.5, 5.6 BT: AP, Difficulty: Difficult, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
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EX 5.9 Answers will vary, but the following are issues with the data set: Issue 1: Inconsistency Vendor column.
Bother has a low frequency (1) and is misspelled. (Note that this is an application of Data Preparation Pattern 8.) Issue 2: The price for purchase with code 81 is an outlier and most likely incorrect. The prices for all printers of type L12 are shown next. The price of $225 is an outlier. (Note that this is an application of Data Preparation Pattern 10.)
LO 5.1., 5.4 BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.10 Answers will vary, but the following are issues with the data set: Issue 1: Name (Gender) is a combined column that mixes Name and Gender information. (Note that this is an application of Data Preparation Pattern 6.) Issue 2: Inconsistency Title column.
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Both Senior Manager and Sr. Manager are used. (This is an application of data preparation pattern 8.) Issue 3: When comparing the salaries for all staff members, the salary of Jenny Jacobs is unusually high (it is an outlier).
(Note that this is an application of Data Preparation Pattern 7.) LO 5.1., 5.5 BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.11 You would like to test whether salespeople sell to fictitious, non-approved, customers to get their bonus. Stated differently, you test referential integrity--do all values in the Customer field in the Sales table also exist in the Name field in the Customer table? The sale with ID 19271 by salesperson Maggie is to customer Drauf, who is not on the approved list. Maggie sold 35 units to Drauf. It should also be noted that Maggie exceeded the bonus quantity by 5:
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Therefore, this transaction requires further investigation. (Note that this is an application of Data Preparation Pattern 17.) LO 5.6. BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX 5.12 Answers will vary, but the following are consistency, completeness, and validity issues with the data set: Issue 1: The DateOfBirth field has two invalid values:
Driver 3053 is only 10 years old. Driver 3056 has not been born yet.
(Note that this is an application of Data Preparation Pattern 10.) Issue 2: Driver 3055 was only 14 years old when she obtained her driver license. This is an invalid number which implies that either her birthday or the day when she received her driver license are wrong. (Note that this is an application of Data Preparation Pattern 14.) Issue 3: The NumberOfComplaints field has one invalid value. Driver 3051 has a negative value for this field. Drivers cannot have a negative number of complaints. (Note that this is an application of Data Preparation Pattern 10.) Issue 4: The AverageRating field is inconsistent. Two different notations are used for the average ratings. For example, driver 3051 has a rating of 4.3 stars, while driver 3059 has a rating of 4.3. We suggest using the numeric values since they can be used for further calculations. (Note that this is an application of Data Preparation Pattern 8.) Issue 5: The TextCode field also has a completeness issue. Driver 3051 has no value for this field and thus no pickup information can be sent to her. Stated, differently, this is a mandatory field. LO 5.1., 5.4., 5.5 BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
EX.13. Answers will vary, but the following are issues with the data set: Issue 1: Some of the column names are ambiguous and should be renamed: Id >> BatchID EstThrough >> EstimatedThroughputTime ActThrough >> ActualThroughputTime EstQuantity >> EstimatedQuantity ActQuantity >> ActualQuantity (Note that this is an application of Data Preparation Pattern 4.) Issue 2: The Product column has an inconsistency issue: Belgian Waffle and Belgian Wafle. (Note that this is an application of Data Preparation Pattern 8.) Issue 3: EstimatedThroughputTime and ActualThroughputTime are text columns. Both columns should be split and only the numbers should be kept. The data type should be Whole Number.
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Issue 4: Batch 400136 has value D for the Facility column. There are only two facilities, A and B, and therefore, D is an invalid value. (Note that this is an application of Data Preparation Pattern 5.) LO 5.1., 5.4. BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Technology, AICPA AC: Technology and Tools.
SOLUTIONS TO THE PROFESSIONAL APPLICATION CASE PAC 5.1. Auditing: Answers will vary. Some issues with the data set are listed here.
No vendors are linked to the purchase transactions, which makes it impossible to answer some questions, such as the dollar amount bought from a specific vendor (Note that this is an application of Data Preparation Pattern 20). There is a sequence gap for the purchase transactions. The purchase with # 5 is missing. (This is an application of Data Preparation Pattern 9.) There are duplicate records. The information for transaction # 8 is recorded twice. The employee for purchase 8—employee with code 6—does not exist. (This is an application of Data Preparation Pattern 17, more specifically, this is a violation of referential integrity.) Vendor 4, Bob Dylan and employee 4, Andrea Dylan have the same address, which might indicate a fictitious vendor. (This is an application of Data Preparation Pattern 17.)
PAC 5.2. Financial Accounting: 1. The main issue to be addressed is the list (multi-valued) format of the Payments column in the SalesOrders file. As a result, it is impossible to match the values of the Payments column in the SalesOrders table with the values in the CashReceiptNo column in the CashReceipts table. The definition of this relation is essential for the calculation of accounts receivable. This is an application of data preparation pattern 6. 2. Power Query / Excel is used to illustrate this solution. The solution steps will be similar when another ETL tool is used. Step 1: Create a new Excel file—AccountsReceivable—and import the data from both csv files. Transfer the data from the csv files to Excel. Use the Data tab in Excel’s main menu and click From Text/CSV in the Get & Transform Data group. Open the file with the sales orders data and click Load. Do the same for the file with the cash receipts data. Step 2: Open Power Query by going to the Data tab in Excel’s main menu. Select the down arrow next to Get Data in the Get & Transform Data group. Click Launch Power Query Editor. Step 3: Restructure the content of the tables: In the Queries pane, select and right-click the SalesOrder table, and select Duplicate.
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Rename the newly created table—SalesOrder(2)—as “Applied”. In the SalesOrder table, select the Payments column and press the Delete key on your keyboard. In the Applied table, delete all columns except for Order# and Payments.
Step 3: Convert the multi-valued column into a single-valued column: Select the Applied table. In Power Query, select the Payments column in the Applied table. Next, in the Home ribbon, in the Transform group, click the down arrow next to Split Column, and select By Delimiter. In the Split Column by Delimiter window, select Semicolon in the field beneath Select or enter delimiter and then select Each occurrence of the delimiter underneath Split at. The Applied table now looks as follows:
Step 4: Group the data into one column. Connecting the payments in the Applied table with the payments in the CashReceipt table is still challenging given that that they are spread across multiple columns in the former. To group them in one column, first select the Order# column in the Applied table. Following, in the Transform ribbon in the Any Column group, click the down arrow next to Unpivot Columns and select Unpivot Other Columns. Delete the Attribute column and rename the Value column as “Payments”. The Applied table should now look as follows:
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Payments is now a single-valued column. The new table structure makes it easy to answer questions such as: What is the balance for each sale? What is the total accounts receivable amount? For which orders have no payments been received? Which sales orders are paid off in installments? For the solution to this problem, see the PAC 5.3 solution file. PAC 5.3. Managerial Accounting: Answers will vary. Key transformations are listed here. Power Query / Excel is used for illustration, butsolution steps will be similar when another ETL tool is used. Step 1: Extract the data by creating a new Excel file—CostAnalysis—and import the data from the CSV file. Use the Data tab in Excel’s main menu and click From Text/CSV in the Get & Transform Data group Open the file and click Load. Step 2: Open Power Query by going to the Data tab in Excel’s main menu. Select the down arrow next to Get Data in the Get & Transform Data group. Click Launch Power Query Editor. In the Queries pane, rename the CostData table as “CostAnalysis.” Step 3: If needed, in the Transform ribbon, select Use First Row as Headers. Replace the header of the first column, #, with “OrderNumber” (Pattern#4). Step 4: Restructure the data: Select the OrderNumber column. In the Transform ribbon in the Any Column group, click the down arrow next to Unpivot Columns. Select Unpivot Other Columns.
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The table should now look as follows—Only the first ten of fifty-five rows are shown:
Select the Attribute column. In the Home ribbon, click on the down arrow next to Split Colum. Click on By Delimiter. In the Split Column by Delimiter window, select Colon in the Select or enter delimiter field and select Left-most delimiter underneath Split at. Rename the Attribute.1 column header as “Service,” the Attribute.2 column header as “Markup,” and the Value column header as “Price.” Close the Power Query editor by clicking Close & Load in the Home ribbon.
Step 5: In the worksheet, create a Cost column and use the following formula to determine the cost per order per service: =(100*D2)/(100+C2) The worksheet should now look as follows (only the first ten rows are shown):
Answering the questions listed above should now be easy. PAC 5.4. Tax Accounting:
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Student answers will vary. The following are some data issues. Issue 1: The tables cannot be linked because the SalesTaxRates table uses the names of the states while the SalesOrders table uses abbreviations. Stated differently, states are represented inconsistently across tables. (This is an application of Data Preparation Pattern 17.) Issue 2: The SalesTaxRates table mixes two different data types: numbers and text. Text—N/A— cannot be used as part of calculations. Therefore, N/A should be replaced by 0. (This is an application of Data Preparation Pattern 5.)
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Data and Analytics in Accounting: An Integrated Approach
CHAPTER 6 ANALYSIS: INFORMATION MODELING Learning Objectives: 6.1: Describe the foundational concepts of information modeling. 6.2: Apply common information modeling algorithms. 6.3: Develop and implement information models for common accounting data structures.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. A LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
2. B LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
3. B LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
4. A LO 6.1, BT:AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
5. D LO 6.1, BT: AP, Difficulty: Hard, TOT: 4 min., AACSB: Analytic, AICPA AC: Technology and Tools
6. C LO 6.2, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
7. B LO 6.2, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
8. D LO 6.2, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
9. C LO 6.2, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
10.D LO 6.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
11.C LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
12.D LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
13.D LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
14.B LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
15.A LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
16.B LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
17.D LO 6.3, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools Answer: D
18. C LO 6.3, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
19.C LO 6.3, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach
ANSWERS TO REVIEW QUESTIONS 1. The goal of information modeling is to calculate the information that is necessary to conduct the analysis. LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
2. The two tasks involved in information modeling are: •
Understanding what information is required for analysis and what data is needed to produce the information.
•
Identifying the specific measures to be used for analysis based upon the data, and then writing the code that generates the information.
LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
3. A data model defines the structure of a data set. This includes what concepts are described, the tables, and what fields are being used to describe the concepts. An information model extends the data model and consists of calculated columns and measures. LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
4. Calculated columns and measures: • •
A calculated column adds a new column to a table, and its content is calculated starting from the values in other columns. For example, the dollar amount for an invoice line, a calculated column, is determined by multiplying price and quantity. A measure calculates an aggregate such as total revenue or total profit. This aggregate can then be sliced by any possible combination of dimensions. For example, total revenue, the measure, can be sliced by product, by customer, by weekday, and so on.
LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
5. To create a rich set of measures for the fact table and to develop a rich set of dimensions that can break down—or slice—the measures. LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
6. An algorithm is a set of instructions that transform data into information. There are many accounting algorithms. The following algorithms calculate the gross profit margin:
•
Calculate Revenue
• •
Calculate Gross Profit: Revenue – COGS Calculate Gross Profit Margin: Gross Profit / Revenue
LO 6.1, BT: AP, Difficulty: Easy, TOT: 3 min., AACSB Analytics, AICPA AC: Technology and Tools
7. Answers will vary because there are many forms of text calculation. Following are two examples. • •
Combining different pieces of location information into an address. Counting how many times a word occurs in a text. This is a technique often used in sentiment analysis—how many times does a set of positive words occur.
LO 6.2, BT: AP, Difficulty: Easy, TOT: 3 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach 8. Boolean expressions evaluate statements as true or false, making them an excellent classification tool. For example, the following Boolean expression can be used to determine whether a transaction is current (C) or past due (PD). IF number of days due < 30, C, PD The Boolean expression is "number of days due < 30" and the possible classifications are "C" and "PD". LO 6.2, BT: K, Difficulty: Easy, TOT: 3 min., AACSB: Analytic, AICPA AC: Technology and Tools
9. Tables are connected through matching values of primary and foreign keys. Necessary data can be retrieved from multiple tables based on these matching values. LO 6.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
10. A measure calculates an aggregate such as total revenue, total cost, total profit, number of customers, etc. The one measure, multiple analyses principle refers to the fact that the same measure can be broken down in many ways. For example, total revenue can be broken down by customer, sales region, salesperson, product, product category, etc. LO 6.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
11. Mathematical Operator Sum Multiplication Count Concatenation
Type of Information Field ANS: Measure ANS: Calculated column ANS: Measure ANS: Calculated column (text)
Average
ANS: Measure
Division
ANS: Calculate column
Exponentiation
ANS: Calculated column
Standard Deviation
ANS: Measure
LO 6.2, BT: AP, Difficulty: Medium, TOT: 3 min., AACSB: Analytic, AICPA AC: Technology and Tools
12. A measure calculates an aggregate number that can be sliced in many ways. For example, you can slice total profit (the measure) by product, by region, by customer, and so on. A filtered measure filters the data set to be used for the calculation of the aggregate. For example, you may want to analyze U.S. profit, the profit for a well-defined group of products, the profit generated during a specific period, and so on. LO 6.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach 13. To create a new, more complex, measure using existing measures. LO 6.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
14. Who dimensions describe who is involved in accounting transactions. For example, to whom were goods delivered? What dimensions describe what is involved in accounting transactions. For example, what products were sold? When dimensions describe when accounting transactions occur. For example, on what days were the goods delivered? LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
15. A flows relationship describes what resources were acquired or given up as part of a transaction. For example, a purchase might result in an increase (inflow) of raw materials while a sale might result in a decrease (outflow) of finished goods. LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
16. Physical flow information describes “how many.” How many units were bought (inflow), how many units were used in the production process (outflow), how many units were sold (outflow), and so on. Monetary flow information describes “how much.” What is the value (dollar amount) of the units bought (inflow), the units used in the production process (cost, outflow), the units sold (outflow), and so on. LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
17. 1. Product categories (dimension). 2. TotalProfit (fact). 3. Year and state (dimensions). LO 6.3, BT: K, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
18. A calendar table contains a list of all dates for the period to which the analysis applies. In addition, it contains all dates and time-related dimensions that can be used for analysis, such as year, quarter, month, and weekday. These dimensions are calculated columns that are calculated starting from the date values. LO 6.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
19. An internal agent is the employee who is accountable for the transaction. For a purchase transaction, this is typically the buyer. An external agent is an outside person or organization involved in the transaction. For a purchase transaction, this is typically the vendor. LO 6.3, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
20. The occurs relationship records when a transaction takes place. This information is crucial because accountants report financial performance for a given period. LO 6.3, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
21. There can be multiple customers (N) per state and multiple customers (N) per loyalty group.
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Data and Analytics in Accounting: An Integrated Approach
Using a Number of Customers measure, these relationships can be used to answer questions such as: • • • • •
How many customers are there per state? What is the state with the highest number of customers? How many customers are there per loyalty group? What is the loyalty group with the highest number of customers? What % of customers belong to the most elite loyalty group?
LO 6.3, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach
SOLUTIONS TO BRIEF EXERCISES BE 6.1 1. c. information model 2. a. algorithm. 3. d. star schema 4. b. data LO 6.1, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 6.2 Definition
Term
1. An aggregate that is calculated from a set of data that can be used for analytical purposes.
ANS: e. Measure
2. A specification of the structure of a data set showing what concepts are
ANS: c. Data Model
described as tables and what characteristics are used to describe the concepts. 3.An extension to the data model that consists of calculated columns and measures.
ANS: d. Information Model
4.A set of instructions that transform data into information.
ANS: a. Algorithm
LO 6.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 6.3 1. Measure. This is a sum of all quantities sold per salesperson, for a specific title, during a specific period. 2. Measure. The standard deviation is calculated for a set of prices. 3. Calculated column. The annual depreciation expense is calculated for a specific asset. 4. Measure. Determine the highest value of a column that contains the number of complaints per product. 5. Calculated column. Seniority is calculated for each employee. There is no mathematical operation that applies to a set of values. LO 6.1, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach
BE 6.4 1. Disagree. Depreciation data describe the use of fixed assets and therefore a flows (outflows) relationship. 2. Agree. 3. Agree. 4. Agree. LO 6.1,6.3, BT: AP, Difficulty: Easy, TOT: 4 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 6.5 1. Information Model: The total value of shares sold can be calculated in many ways using different software. Next, we illustrate how this can be done with Power BI's DAX language in two steps. Step 1: Determine the value of each transaction. Value = Cost * #Shares (Note: This is an application of Pattern 1: Within-table numeric calculation.) Step 2: The total value of shares sold. TotalValue = Sum(Value) (Note: This is an application of Pattern 5: Single-column aggregation.) 2. Line Chart: The line chart is created by putting TotalValue (measure) on the y-axis and Month (dimension) on the x-axis.
LO 6.2, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach
BE 6.6 1. Information Model: NumberOfProjects = COUNT(Project#) (Note: This is an application of Pattern 5: Single-column aggregation.) AboveBudget = IF ActualExpenses > BudgetedExpenses THEN “YES” ELSE “NO” (Note: This is an application of Pattern 3: Within-table classification.) 2. Pie Chart: NumberOfProjects (measure) is broken down by the values of AboveBudget: Yes and No. Number of projects under or at budget: 125
LO 6.2, BT: AP, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach
BE 6.7 1. Information Model: The total value of Bellafonte's stock portfolio can be calculated in many ways using different software. Next, we illustrate how this can be done with Power BI's DAX language in two steps. Step 1. Determine the value for each stock by multiplying its price and quantities: Value = StockPortfolioData[Price] * StockPortfolioData[NumberOfStocks] (Note: This is an application Pattern 1: Within-table numeric calculation.) Step 2. Determine the total value of Bellafonte’s stock portfolio: TotalValue = Sum(StockPortfolioData[Value]) (Note: This is an application of Pattern 5: Single-column aggregation.) 2. Pie Chart: Once the information model is in place, a report can be created that shows:
•
The stock portfolio's total value:
•
The relative share of each market sector in the portfolio (pie chart):
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach BE 6.8 1. Information Model: The total number of mattresses sold can be determined by summing all the values in the UnitsSold column. The following DAX formula performs such calculation: TotalUnitsSold = Sum(SomNumTestSales[UnitsSold]) (Note: This is an application of Pattern 5: Single-column aggregation.) Report/Analysis: A report can be created that shows the total number of units sold.
As the report shows, the goal was not reached. 2. Information Model: This question can be answered by applying the one measure, multiple analyses principle to the TotalUnitsSold measure. No changes must be made to the information model. Report/Analysis:
As the report shows, four of the salespeople reached their goal: Bruno, Elon, Qingling, and Kanye. 3. Information Model: The information model for this question can be built in two steps. Step 1: First, determine whether the goal, selling 25 mattresses, was met by a salesperson on a specific day. This is a classification problem with two possible outcomes: "Yes" or "No". Using DAX this classification can be coded as follows: MetGoal = If(SomNumTestSales[UnitsSold] >= 25, "Yes", "No") (Note: This is an application of Pattern 3: Within-table classification.) Step 2: Determine how many “yes” days each salesperson had. Using DAX. this calculation can be coded as follows: NumberOfYesDays = Calculate(Countrows(SomNumTestSales),SomNumTestSales[MetGoal]=”Yes”) (Note: This is an application of Pattern 6: Filtered aggregation.)
10
Data and Analytics in Accounting: An Integrated Approach Report/Analysis: The NumberOfYesDays measure can then be used to determine which salespeople had at least four (out of seven) days where they reached the goal.
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach BE 6.9 1. Information Model: There are different ways to create the information model needed to answer KOE's questions. Next, we illustrate a three-step solution created with Power BI's DAX language. Step 1: Create a measure that determines the total of all amounts. TotalAmount = SUM(FinancialData[Amount]) This total has no meaning but is needed for the further development of the information model. (Note: This is an application of Pattern 5: Single-column aggregation.) Step 2: Create measures that determine the total for each of the individual accounts. TotalRevenue = Calculate(FinancialData[TotalAmount],FinancialData[Account]=”Revenue”) TotalCOGS = Calculate(FinancialData[TotalAmount],FinancialData[Account]=”COGS”) TotalOperatingExpenses = Calculate(FinancialData[TotalAmount],FinancialData[Account]=”Operating Expenses”) TotalTax = Calculate(FinancialData[TotalAmount],FinancialData[Account]=”Tax”) (Note: These four measures are applications Pattern 6: Filtered aggregation.) Step 3. Create the measures that determine the net profit margin. NetProfit = FinancialData[TotalRevenue] - FinancialData[TotalCOGS] FinancialData[TotalOpertingExpenses] - FinancialData[TotalTax] NetProfitMargin = FinancialData[NetProfit] / FinancialData[TotalRevenue] (Note: These two measures are applications of Pattern 7: Measure hierarchies.) 2. Analysis/Report: The NetIncomeMargin measure is used to create the top part of the following report. The column chart in the lower part of the report results from applying the "one measure, multiple analysis" principle. The net income margin is calculated for each month—the months are used as filters for the calculations.
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Data and Analytics in Accounting: An Integrated Approach
LO 6.2, BT: AP, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 6.10 1. Information Model: There are many ways to specify the information model underlying Charmes' allocation process. Next, we illustrate a two-step solution created using Power BI. Step 1: For each product line, determine its percentage in terms of total units manufactured last month. PercentageOfUnits = NumberOfUnits[NumberOfUnits] / sum(NumberOfUnits[NumberOfUnits]) (Note: A variation of Pattern 1: Within-table numeric calculation is applied here. An aggregate number is used when creating the calculated column.) Step 2: Determine the total dollar amount of inspection costs allocated to each product line. InspectionCosts = NumberOfUnits[PercentageOfUnits] * 45750 (Note: This is an application Pattern 1: Within-table numeric calculation.) 2. Pie Chart:
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Data and Analytics in Accounting: An Integrated Approach
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 6.11 1. Information Model: There are many ways to specify the information model that can be used to determine Danski’s effective tax rate. Next, we illustrate a three-step solution created using Power BI. Step 1: TotalIncomeBeforeTax = SUM(Amount) WHERE Account="Income Before Tax" (Note: This is an application of Pattern 6: Filtered aggregation.) Step 2: TotalIncomeTaxExpense = SUM(Amount) WHERE Account="Income Tax Expense" (Note: This is an application Pattern 6: Filtered aggregation.) Step 3: EffectiveTaxRate = TotalIncomeTaxExpense / TotalIncomeBeforeTax (Note: This is an application of Pattern 7: Measure hierarchy. ) 2. Report / Analysis: The line chart is created by putting the effective tax rate (measure) on the Y-axis and Year (dimension) on the X-axis.
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Data and Analytics in Accounting: An Integrated Approach
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 6.12 1. The Customerfield (foreign key) is missing in the SalesOrder table. Because the tables are not linked, no customer (who) analysis for the sales orders can be performed. 2. In the SalesPerson table DateOfBirth should be a column, while Age should be a Calculated Column, not the other way around. 3. In the SalesOrder table, SalesOrderLineAmount is a calculated column (SalesPrice * QuantitySold), not a column. LO 6.3, BT: AP, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 6.13 Analysis
Pattern
1. Comparing the relative proportion of customers per sales region.
ANS: b. how many
2. An airline determining the number of hours flown on each type of airplane (e.g., Airbus 321 or Boeing 787) in use.
ANS: a. flows
3. The monthly fluctuations in the number of pesticide applications for a farm.
ANS: c. occurs
4. A comparison of the number of sales completed by contractors versus full-time employees
ANS d. participates
LO 6.3, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach BE 6.14
Revenues are compared across product categories. SalesPersonType (Senior), Country (USA), and Year (2025) are fields that are used as filters. SalesPersonType is a calculated field based on HireDate. LO 6.3, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 6.15 Student answers will vary. Following are five sample questions: 1. How do employees compare based on the number of leases they have been accountable for in 2025 (year)? 2. What is the share of each of the three airplane manufacturers for Mediterranean's fleet? 3. How do lessors compare based on their short-term/long-term leases ratio? 4. Who are the top five 2025 lessors in terms of total lease amount? 5. How many Embraer airplanes are leased from Brazilian lessors? LO 6.3, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools
SOLUTIONS TO EXERCISES EX 6.1 1. 2. 3. 4.
Flows. Dismissal (transaction) of fixed assets (what). Flows. Making a cash (what) payment (transaction). Occurs. Generating quarterly (when) sales (transaction) report. Participates. Return (transaction) of goods to a vendor (who). Flows. Return (transaction) of goods (what) to a vendor.
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Data and Analytics in Accounting: An Integrated Approach 5. Participates. Manufacturing (transaction) a custom-made (who) electric car. Flows. Manufacturing (transaction) a custom-made electric car (what). LO 6.1, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools
EX 6.2 1. Information Model: There are different ways to create the information model that can determine Curves’ net revenues and tax amounts. Next, we illustrate a two-step solution created with Power BI's DAX language. Step 1: Amount to be paid after applying discount and tax to be paid on that amount for each sale. NetSalesAmount = Amount – Discount (Note: This is an application of Pattern 1: Within-table numeric calculation.) TaxAmount = NetSalesAmount * (Tax%/100) (Note: This is an application of Pattern 1:Within-table numeric calculation.) Step 2. Total net amount and total tax amount to be paid. TotalNetSalesAmount = Sum(NetSalesAmount) (Note: This is an application of Pattern 5: Single-column aggregation.) TotalTaxAmount = Sum(TaxAmount) (Note: This is an application of Pattern 5: Single-column aggregation.) 2. Report/Analysis:
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 6.3 1. Information Model: There are different ways to create the information model that can be used to analyze FonzieBikes’ estimated standard costs. Next, we illustrate a two-step solution created with Power BI's DAX language. Step 1: Estimated standard cost for an item. STDCost = STDquantity * STDprice (Note: This is an application of Pattern 1:Within-table numeric calculation.)
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Data and Analytics in Accounting: An Integrated Approach Step 2: Total estimated standard costs. TotalStandardCost = Sum(STDCost) (Note: This is an application of Pattern 5: Single column aggregation.) 2. Report/Analysis:
LO 6.2, BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 6.4 1. Information Model: There are different ways to create the information model to analyze how much money Emory & Grant’s employees spent on lodging at the different hotel chains. Next, we present a solution where the information model consists of a calculated column (Hotel) that determines whether the merchant is Hilton, Marriott, or Other, and a measure (TotalAmountSpent) that determines how much money they have spent on lodging. Hotel = IF Vendor contains “Hilton” THEN “Hilton” ELSE IF Vendor contains “Marriott” THEN “Marriott” ELSE “Other” (Note: This is an application of Pattern 2: Within-table text calculation.) TotalAmountSpent = SUM(Amount). (Note: This is an application of Pattern 5: Single-column aggregation.) 2. Reports/Analyses: Report 1: Amount spent per hotel chain
Report 2: Relative proportion of lodging expenses per hotel chain
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Data and Analytics in Accounting: An Integrated Approach
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 6.5 1. Information Model: Create an algorithm that determines whether a candidate is qualified (Classification), “Yes” or “No.” Qualified = IF (Age >= 30 AND Age <= 50) AND (Audit = “Yes” OR Tax = “Yes”) AND (Level >= 3) THEN “Yes” ELSE “No” (Note: This is an application of Pattern 2: Within-Table Classification.) 2. Report/Analysis: This report is created using the Qualified (calculated) column: Identify candidates with a value “Yes” for the Qualified column.
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach EX 6.6 1. Information Model: There are different ways to create the information model that can be used to determine Old Marine’s January 2025 total net revenue. We illustrate a three-step solution created with Power BI's DAX language. Step 1: Create a calculated column for the Orders table that determines the shipping cost for each order. The cost data can be found in the Shipments table. ShippingCost = IF Shipments.Type = "O" THEN 0 ELSE Shipments.amount (Note: This is an application of Pattern 4: Across-table calculation.) Step 2: Use the information in the ShippingCost column to determine the net revenue (amount) for each order. Create a NetRevenue column in the Orders table. NetRevenue = Orders.Amount – Orders.ShippingCost (Note: This is an application of Pattern 1: Within-table calculation.) Step 3: Calculate the Total Net Revenue. TotalNetRevenue = Sum(NetRevenue) (Note: This is an application of Pattern 5: Single-column aggregation.) 2. Report/Analysis:
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 6.7 1. Information Model: There are different ways to create the information model that can be used to determine Berok’s profit margin ratio. We illustrate a three-step solution created with Power BI's DAX language. Step 1: Create a measure that calculates the total revenues for all firms during the 2021-2025 period. TotalRevenue = Sum(Revenue) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 2: Create a measure that calculates the total profits for all firms during the 2021-2025 period. TotalProfit = Sum(Profit) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 3: Create a measure that calculates the profit margin for all firms during the 2021-2025 period.
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Data and Analytics in Accounting: An Integrated Approach ProfitMargin = TotalProfit / TotalRevenue (Note: This is an application of Pattern 7: Measure hierarchy.) 2. Reports/Analyses: a. For the first report, slice ProfitMargin by company and year.
b. For the second report, slice ProfitMargin by company.
LO 6.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 6.8 1. Information Model: There are different ways to create the information model that can be used to determine Ruppetware’s bonus amounts. Next, we illustrate a four-step solution created with Power BI's DAX language. Step 1: Determine the total 2025 sales. Total 2025 Sales = Sum(2025) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 2: Determine the 2025 target. 2025 Target = Average(2024) * 1.05 (Note: This is an application of Pattern 5: Single-column aggregation.) Step 3: Compare 2025 sales with the 2025 target. Variance = ((Total 2025 Sales) / (2025 Target)) – 1 (Note: This is an application of Pattern 7: Measure hierarchy.) Step 4. Determine individual bonuses based on the variance. BONUS = IF Variance >= 0.1 THEN 15000 ELSE IF Variance >= 0.05 AND < 0.1 THEN 10000 ELSE IF variance > 0 AND < 0.05 THEN 5000 ELSE 0 (Note: This is an application of Pattern 3: Within-table classification.)
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Data and Analytics in Accounting: An Integrated Approach 2. Report/Analysis:
LO 6.2, BT: AP, Difficulty: Hard, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 6.9 1. Information Model: There are different ways to create the information model that can be used to determine which trucks exceed the maximum weight and which trucks are loaded for less than 75%. Next, we illustrate a four-step solution created with Power BI's DAX language. Step 1: Create a calculate column (Pallets table) that convert pallet weights from KG to pounds. Weight = WeightKG * 2.20462 (Note: This is an application of Pattern 1: Within-table numeric calculation.) Step 2: Create a measure (Pallets table) that determines the total weight to be transported (in pounds). TotalWeight = Sum(Weight) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 3: Create a measure (Truck table) that determines the total maximum weight for all trucks. TotalMaximumWeight = sum(MaximumWeight) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 4: Create a measure (Truck table) that determines the load ratio for all trucks. Load = [TotalWeight] /[TotalMaximumWeight] (Note: This is an application of Pattern 7: Measure hierarchy.) 2. Report/Table:
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Data and Analytics in Accounting: An Integrated Approach
LO 6.2, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 6.10 1. Information Model: There are different ways to create the information model that can be used to determine Maurer and Cook’s 401K contributions. Next, we illustrate a three-step solution created with Power BI's DAX language. Step 1: Determine the employer’s matching contributions. Using Power BI’s DAX language the following formula can be used to create the EmployerMatchingContribution calculated column. EmployerMatchingContribution = MaurerCookData[EmployeeContribution] * 0.75 (Note: This is an application of Pattern 1: Within-table numeric calculation.) Step 2: Determine the vested employer matching contribution based on the company’s vesting policies. Using Power BI’s DAX language the following formula can be used to create the VestedEmployerContribution calculated column. VestedEmployerContribution = switch( TRUE(), MaurerCookData[YearsOfService] < 2, 0, MaurerCookData[YearsOfService] = 2, MaurerCookData[EmployerMatchingContribution] * 0.2, MaurerCookData[YearsOfService] = 3, MaurerCookData[EmployerMatchingContribution] * 0.4, MaurerCookData[YearsOfService] = 4, MaurerCookData[EmployerMatchingContribution] * 0.6, MaurerCookData[YearsOfService] = 5, MaurerCookData[EmployerMatchingContribution] * 0.8, MaurerCookData[YearsOfService] >= 6, MaurerCookData[EmployerMatchingContribution] ) (Note: This is an application of Pattern 3: Within-table classification.) Step 3: Determine an employee’s total vested contribution. Using Power BI’s DAX language the following formula can be used to create the TotalVestedContribution calculated column. TotalVestedContribution = MaurerCookData[EmployeeContribution] + MaurerCookData[VestedEmployerContribution]
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Data and Analytics in Accounting: An Integrated Approach (Note: This is an application of Pattern 1: Within-table numeric calculation.) 2. Report/Analysis: Create a table that shows the total vested contribution for each employee.
LO 6.2, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Knowledge, AICPA AC: Technology and Tools
EX 6.11 1. Information model: There are different ways to create the information model that can be used to determine and analyze the Blue Ballroom’s cash flows. Next, we illustrate a three-step solution created with Power BI's DAX language. Step 1: Determine the total incoming payments. Inflow = SUM(‘CashReceipt’[Amount]) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 2: Determine the total outgoing payments. Outflow = SUM(‘CashDisbursement’[Amount]) (Note: This is an application of Pattern 5:Single-column aggregation.) Step 3: Determine the current balance. CurrentBalalance = ‘Cash’[StartingBalance] + [Inflow] – [Outflow] (Note: This is an application of Pattern 4: Across-Table Calculation.) 2. Report/Analysis: Create a table and add the Bankname, StartingBalance, Inflow, Outflow, and CustomerBalance fields.
LO 6.2,6.3 BT: AP, Difficulty: Hard, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach
EX 6.12 1. Information Model: There are many different measures that can be created, and students’ answers will therefore vary. Five examples of measures and one calculated column that can be used as building blocks are defined here. Calculated Column TimeVariance = Ride.EstimatedTime – Ride.ActualTime (Note: This is an application of Pattern 1: Within-table numeric calculation.) Measure 1 AverageRating = Average(Ride.Rating) (Note: This is an application of Pattern 5: Single-column aggregation.) Measure 2 AverageTimeVariance = Average(Ride.TimeVariance) (Note: This is an application of Pattern 5: Single-column aggregation.) Measure 3. AverageTip = Average(Ride.Tip) (Note: This is an application of Pattern 5: Single-column aggregation.) Measure 4. TotalTips = Sum(Ride.Tip) (Note: This is an application of Pattern 5: Single-column aggregation.) Measure 5. NumberOfRides = Count(Ride.RideID) (Note: This is an application of Pattern 5: Single-column aggregation.) 2. Reports/Analyses: Many reports can be generated, and students’ answers will therefore vary. Three example reports are shown. Report 1: Comparison of Driver Performance Based on Ratings Given By Customers.
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Data and Analytics in Accounting: An Integrated Approach
Report 2: Comparison of Driver Performance Based on Average Time Variance.
Report 3: Comparison of Driver Performance Based on Total Tips.
LO 6.2,6.3 BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach EX 6.13 1. Information Model: There are many different calculated columns and measures that can be created, and students’ answers will therefore vary. Next, one calculated column and one measure are defined. Calculated Column (Sales Table). Amount = Sales.Quantity * Items.ItemPrice (Note: This is an application of Pattern 4: Across-table aggregation.) Measure TotalAmount = SUM(Sales.Amount) (Note: This is an application of Pattern 5: Single-column aggregation.) 2. Reports/Analyses: Many reports can be generated and students’ answers will therefore vary. Following are two example reports. Report 1: Total Revenue Generated by Product Type
Report 2: Total Revenue Generated by Item Category
LO 6.2,6.3 BT: AP, Difficulty: Easy, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 6.14 1. Information Model: There are different ways to create the information model that can be used to analyze employee performance at Giuseppina’s. We illustrate a three-step solution created with Power BI's DAX language. Step 1: Determine the total amount received. TotalAmount = SUM(Deliveries.Amount) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 2: Determine the total tip amount received.
27
Data and Analytics in Accounting: An Integrated Approach TotalTipAmount = SUM(Deliveries.Tip) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 3: Determine the average tip percentage received. Tip% = TotalTipAmount / TotalAmount (Note: This is an application of Pattern 5:Measure hierarchy) 2. Reports/Analyses: Report 1: Drivers Ranked By Total Tip Amount Received
Report 2: Drivers Ranked By Tip Percentage
LO 6.2,6.3 BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach
SOLUTIONS TO PROFESSIONAL APPLICATION CASE To achieve the provided solutions, please have your students complete the assignments with the data as provided. PAC 6.1 Financial Accounting: 1. Information Model: There are different ways to create the information model that can be used to analyze D*Tunes gross profit. Next, we illustrate a multi-step solution created with Power BI’s DAX language. Step 1: Determine an instructor’s category. Category = IF NumberOfNationalAwards > 0 THEN “Champion” ELSE IF NumberOfHoursTaught > 250 AND NumberOfAwards >= 3 THEN “Advanced” ELSE NumberOfHoursTaught > 250 THEN “Intermediate” ELSE “Apprentice” (Note: This is an application of Pattern 4: Across-table calculation.) Step 2: Determine an instructor’s rate. Rate = IF Category = “Apprentice” THEN 45 ELSE IF Category = “Intermediate” THEN 65 ELSE IF Category = “Advanced” THEN 85 ELSE 110 (Note: This is an application of Pattern 3: Within-table classification.) Step 3. Determine the cost of a lesson. Cost = IF Class = “PT” THEN 250 ELSE Instructuors.Rate (Note: “PT” refers to a “Party” class. This is an application of Pattern 4: Across-table calculation.) Step 4: Determine the total cost for all classes. TotalCost = SUM(Cost) (Note: This is an application of Pattern 5: Single-column aggregation.) Step 5: Determine prices to be paid by students for participating in a class. Price = IF Lessons.Class = “PR” THEN Instructors.Rate + 30 ELSE IF Lessons.Class = “PT” THEN 25 ELSE IF Lessons.Class = “IN” THEN 0 ELSE 40 (Note: This is an application of Pattern 4: Across-table calculation.) Step 6: Determine prices to be paid by students for participating in a class.
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Data and Analytics in Accounting: An Integrated Approach Determine the total revenue for all classes. TotalRevenue = Sum(Price) (Note: This is an application of Pattern 5: Single-column aggregation Step 7: Determine Total Gross Profit. GrossProfit = [TotalRevenue] – [TotalCost] (Note: This is an application of Pattern 7: Measure hierarchy.)
2. Report/Analysis:
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Data and Analytics in Accounting: An Integrated Approach
PAC 6.2 Auditing: 1.Information Model: Determine the total cost of initiation lessons. TotalCostOfInitiationLessons = SUM(Lessons.Cost) WHERE LessonsClass = “IN”. (Note: This is an application of Pattern 6: Filtered aggregation.) Report/Analysis:
2.Information Model: Determine the Number of Initiation Lessons. NumberOfInitiationLessons= COUNT(Lessons) WHERE Class = “IN”. (Note: This is an application of Pattern 6: Filtered aggregation) Report/Analysis:
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Data and Analytics in Accounting: An Integrated Approach
3. Information Model: The current information model can be used. No further extensions are needed. Report/Analysis:
4. Information Model: Determine the Conversion Rate Step 1: Determine the number of non-initiation lessons. NumberOfNonInitiationLessons= COUNT(Lessons) WHERE Class <> “IN”. Step 2: Determine whether a student has been converted. Conversion = IF(NumberOfInitiationLessons >= 1 AND NumberOfNonInitiationLessons = 0) THEN "NO" ELSE (IF(NumberOfInitiationLessons >= 1 AND NumberOfNonInitiationLessons >= 1) THEN "YES" ELSE "NA" ) Step 3: Determine the conversion rate. ConversionRate = COUNT(Students) WHERE Conversion = “Yes” / COUNT(Students) WHERE Conversion = “NA” Report/Analysis:
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Data and Analytics in Accounting: An Integrated Approach PAC 6.3 Managerial Accounting: 1. Information Model: Step 1: Determine the “break-even” number of students for each lesson. Breakeven Number of Students = Roundup((Lessons[Cost] + 150) / 40) (Note: This is an application of Pattern 1: Within-table numeric calculation.) Step 2: Determine which lessons did not have enough students to reach the break-even point. Discrepancy = IF(
No Students THEN (0 – Breakeven Number of Students) ELSE (Number of Students – Breakeven Number of Students)
(Note: This is an application of Pattern 1: Within-table numeric calculation.) 2. Report/Analysis: Show all lessons with a negative value for discrepancy (< 0). Exclude non-group lessons: Intro (IN), Private (PR), and Party (PT).
PAC 6.4 Tax Accounting: Information Model: Step 1: Determine Monthly Wages. Monthly Wage = Annual Wage / 12 Step 2: Determine Cumulative Wages. Cumulative Wage = Previous Month’s Cumulative Wage + Monthly Wage Step 3: Determine FICA Tax. FICA Tax =
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Data and Analytics in Accounting: An Integrated Approach IF Cumulative Wage < Social Security Maximum THEN Monthly Wage * FICA% ELSE IF (Cumulative Wage – Social Security Maximum) > Monthly Wage THEN ((Monthly Wage – (Cumulative Wage – Social Security Max))*Social Security %) + (Monthly Wage * Medicare %) ELSE Monthly Wage * Medicare % LO 6.1-6.3 BT: AN, Difficulty: Hard TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools
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Data and Analytics in Accounting: An Integrated Approach
CHAPTER 7 Analysis: Data Exploration
Learning Objectives: LO 7.1 Describe the process of data exploration. LO 7.2 Explore foundational data relationships through visualizations. LO 7.3 Explore data by integrating foundational relationships.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 20.C LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
21.A LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
22.B LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
23.A LO 7.1, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
24.D LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
25.E LO 7.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
26.C LO 7.2, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
27.E LO 7.2, BT: AN, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
28.C
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LO 7.2, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
29.F LO 7.2, BT: AP, Difficulty: Medium, TOT: 3 min., AACSB: Knowledge, AICPA AC: Technology and Tools
30.E LO 7.2, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
31.C LO 7.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
32.E LO 7.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
33. A LO 7.3, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
34.D LO 7.3, BT: AP, Difficulty: Medium, TOT: 4 min., AACSB: Knowledge, AICPA AC: Technology and Tools
35.B LO 7.3, BT: AN, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools
36.C LO 7.3, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
Data and Analytics in Accounting: An Integrated Approach
ANSWERS TO REVIEW QUESTIONS 1. The goal of data exploration is to find new insights that will inform decision-making. LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
2. • • • •
Identify the questions that need to be explored. Identify the data relationships. Explore the data relationships. Generate insights while exploring the data relationships.
LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
3. An insight is an observation generated from data that might significantly affect a business’ decision-making. LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
4. Charts visualize data relationships, which makes it easier to generate insights. LO 7.1, BT: K, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
5. A PivotTable can quickly rearrange data to help answer important business questions. LO 7.1, BT: K, Difficulty: Easy TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
6. The key data exploration techniques supported by PivotTables are: • • •
Defining data relationships Filtering Dragging and dropping
LO 7.1, BT: K, Difficulty: Easy TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
7. The values area specifies the number or numbers to be analyzed. LO 7.1, BT: K, Difficulty: Easy TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
8. Filters let us determine what data should be considered for analysis and explore a subset of data, such as location-specific sales, seasonal or quarterly sales, or high-risk projects. LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
9. A nominal variable defines what is being compared and a numeric variable determines how the comparison is done. An example would be the number of customer complaints (numeric variable) by product category (nominal variable). LO 7.2, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
10. Two insights that can be generated from exploring a distribution relationship are: • •
If there are any outliers—e.g., a year with exceptionally high earnings. If the earnings have been consistent or if there have been strong fluctuations.
LO 7.2, BT: AP, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
11. Student answers will vary. Seven different pieces of information that a box-and-whisker chart provides about a distribution are: 7-36
Data and Analytics in Accounting: An Integrated Approach • • • • • • •
Minimum Maximum First quartile. Third quartile. Interquartile range. Median. Mean
LO 7.2, BT: K, Difficulty: Easy TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
12. The values of the nominal variable can be used to create multiple distributions for comparison purposes. LO 7.2, BT: K, Difficulty: Easy TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
13. They explore a deviation relationship comparing actual salaries with industry averages. They will learn whether faculty are underpaid or overpaid. Depending on the results of the analysis, they might change their performance expectations. LO 7.2, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
14. 1. The pie chart represents a part-to-whole relationship. The full pie represents the total expense amount, the whole. The slices represent the relative importance of the different types of expenses, the parts. 2. • •
The biggest expense is housing. More than 75% of the expenses come from three categories: housing, food, and children.
LO 7.2, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
15. 1. Scatterplot 2. Correlation 3. • •
There is moderate negative correlation between years of experience and number of surgical errors. There are a few outliers—e.g., a surgeon with extensive experience (more than 25 years) with a high number of surgical errors.
LO 7.2, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
16. • •
Color: For example, a province or state with a high number of sales transactions is colored red, while a province or state with a low number of sales transactions is colored green. Size: Addresses of customers are represented by bubbles. The size of a bubble is determined by the total dollar amount ordered by a customer.
LO 7.2, BT: AP, Difficulty: Easy, TOT: 3 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
17. 7-37
Data and Analytics in Accounting: An Integrated Approach • • • •
The numeric variable is the total medal count for the last five summer Olympics. It is the number being analyzed. The time unit, year (2008, 2012, 2016, 2020, 2024), determines the medal count per year, i.e., per Olympics. The numeric and time unit variables combined enable trend analysis. The nominal variable is country. Combined with the numeric variable, a country’s total medal count and its share in the overall medal count can be determined. Combined, the numeric, time unit, and country variables, can be used to analyze how each country’s share has changed over time, across Olympics.
LO 7.3, BT: AP, Difficulty: Difficult, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
18. Data relationship 1: A nominal comparison visualized by the column chart. It allows comparing the frequency of (customer) complaints and identifying which complaints are more common.
Data relationship 2: A ranking visualized by the sorted column chart. Complaints are sorted in descending order based on their frequency. The most common issue reported is lack of friendliness. Data relationship 3: Part-to-whole visualized by the cumulative line chart. It shows how much (percentage) issues contribute to the overall number of complaints. More than 80% of the complaints result from two issues: lack of friendliness and long wait times. LO 7.3, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
19. Data relationship 1: A tree map represents a part-to-whole relationship. The tree map shows the relative importance, as a percentage, of each type of asset in an investment portfolio. For example, 41.37% of the portfolio consists of stocks. Data relationship 2: Tree maps rank the different parts in descending order. In the provided visual, the asset with the biggest share (stocks) is followed by the asset with the second biggest share (bonds), and so on. LO 7.3, BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
20. Student answers will vary. The following are examples. • • • •
What is the total sales amount by region during the 2021-2025 period? What are the sales trends for all regions combined (overall sales trend) during the 2021-2025 period? What is the sales by region during a specific year or combination of years? What is the sales trend for a specific region or group of regions during the 2021-2025 period?
LO 7.3, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
SOLUTIONS TO BRIEF EXERCISES BE 7.1 Statement
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Data and Analytics in Accounting: An Integrated Approach
1. An observation that might significantly affect a business’ decisionmaking.
ANS: c. Insight
2. Input of data exploration.
ANS: d. Analytical database
3. Output of data exploration.
ANS: c. Insight
4. The discovery process looking to learning something new and previously unknown from data.
ANS: g. Data exploration
LO 7.1, BT: K, Difficulty: Easy, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.2 Filters that enable to select the data relevant for analysis. LO 7.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.3 This problem can be solved with a PivotTable that shows the summer (filters) sales amounts (values) for the different items (rows) as a percentage of the total summer sales.
LO 7.1, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.4 This problem can be solved with a PivotTable that compares the reliability percentages for each battery (car) model across vendors. The most reliable vendor for the Fortis battery is Champion, while Diehard is the most reliable vendor for the Perfic battery.
LO 7.1, BT: AP, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.5
1.
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Data Exploration
Data Relationship
A strong growth in Wallmark’s number of employees has resulted in strong growth in office expenses
ANS: a. Correlation
Data and Analytics in Accounting: An Integrated Approach
2.
Last year Wallmark’s labor costs systematically ANS: b. Deviation exceeded their estimates. This might have been caused by the premiums Wallmark paid related to the pandemic.
3.
Last month the average price Wallmark paid for a button ANS: c. Distribution battery, a key element in musical greeting cards, was $7. However, prices for the exact same product ranged from $3 to $18.
4.
Batteries represent 20% of the cost of making a standard musical greeting card.
ANS: d. Part-to-whole
LO 7.2, BT: AP, Difficulty: Easy, TOT: 6 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.6 1. The chart explores a deviation relationship: how a salesperson’s actual sales (length of orange bar) differ from their budgeted sales (length of blue bar). 2. Responses will vary. Example insight: All senior salespeople exceeded their budgeted sales, while salespeople with less seniority fall short. This might be an indication that the budgeted numbers need to be revised in alignment with seniority. LO 7.2, BT: AP, Difficulty: Easy TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
BE 7.7 1. A deviation relationship is being explored. 2. There are different visuals that can be considered for such exploration. For example, a stacked bar or column chart with a target line would work. Here we opted for a Dial Gauge, one for each product line. The red threshold shows last year’s unit sales. The orange threshold shows the targeted unit sale. The pointer shows the units sold this year.
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Data and Analytics in Accounting: An Integrated Approach
3. The gauges provide the following insights: • Sales of printers is much lower than last year and thus the +10% target was not reached. • Sales of computers is higher than last year, but the +10% target was not reached. • This year’s sales of phones and TVs exceed the +10% target. LO 7.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.8 The report shows a deviation relationship. The top part of the report compares daily sales with the breakeven point (2,000 loaves of bread). For example, on Wednesday the breakeven point was not reached. It also shows a time series. Both charts in the report show how units manufactured, units sold, unit spoiled, costs, revenues, and profits differ across weekdays. LO 7.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.9 Student answers will vary. Examples follow. 1. The top part of the report uses a line chart exploring a time series data relationship. The chart represents a horizontal or trend analysis, displaying how revenues and accounts receivable changed across years. CITREX was able to increase its revenues while keeping its A/R relatively stable. 2. The lower part of the report also uses a line chart and explores changes in the A/R turnover ratio. In addition, a deviation relationship is explored by including the target line. The increasing value of the ratio indicates a shorter turnover. This was also indicated by the widening gap between revenues and accounts receivable in the top part of the report. CITREX met their A/R turnover ratio target in 2024 and 2025. However, the target was barely met in 2025.
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Data and Analytics in Accounting: An Integrated Approach
LO 7.2-7.3, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.10 Student answers will vary. Examples follow. The top part of the report uses a line chart exploring a time series data relationship. The chart represents a horizontal or trend analysis, displaying how the accounts change over time. While there is a downward trend for revenues and gross profits, there is an upward trend for net profit. This implies that Poisson has been successful in reducing their expenses other than COGS. The lower part of the report uses a line chart and integrates part-to-whole and time series data relationships. Vertical analysis evaluates a financial item (account) as a percentage of a base amount, revenue in this case. The ratio represents a part-whole relationship—for each $1 in revenues (sales), how much cents (percentage) of net profit are generated. The line chart shows an upward trend in the net profit margin.
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Data and Analytics in Accounting: An Integrated Approach
LO 7.2-7.3, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.11 1. The pie chart represents a part-to-whole relationship. Each pie slice signifies the relative importance of each investment type to the overall Napigem portfolio. The (stacked) bar chart represents nominal comparison / ranking relationships. In the illustration, “stocks” has been selected in the pie chart and the bar chart shows the different stocks in Napigem’s portfolio and their importance (nominal comparison) in descending order (ranking) of value. 2. The completed exploration structure looks as follows:
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Data and Analytics in Accounting: An Integrated Approach
LO 7.3, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
BE 7.12 1. The clustered bar chart (A) represents a nominal comparison relationship. How do countries compare to each other in terms of actual sales? The clustered bar chart (A) also represents a deviation relationship. How do actual sales compare to budgeted sales? The (filled) map (C) represents a geospatial relationship. Countries with a blue color met the budget expectations while countries with a red color failed to do so. 2.
LO 7.3, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
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Data and Analytics in Accounting: An Integrated Approach
SOLUTIONS TO EXERCISES (Student answers will vary.) EX 7.1 Both questions can be answered using PivotTables. 1. The first PivotTable summarizes the number of the forms that need to be filed per state and in total.
2. The second PivotTable shows that the suggested policy is not currently properly followed. In PA, more than 60% of the employees are assigned to an office located in a state different from their residency state. This complicates tax filings.
LO 7.1, BT: AP, Difficulty: Easy, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
EX 7.2 The Benford group’s salaries are strongly skewed towards the right. This is indicated by the mean (189,386) being substantially larger than the median (80,026). There is little variation among the lower salaries.
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Data and Analytics in Accounting: An Integrated Approach
LO 7.2, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools.
EX 7.3 1. Question 1: Was the growth target met for the overall sales? Question 2: Which product lines met the target growth? 2. A deviation relationship. We can compare growth in units, from 2024 to 2025, with the targeted growth of 5%. 3. There are numerous ways to visualize and therefore explore deviation relationships. First, growth must be calculated and then compared with the targeted growth rate (5%). This comparison could be done with a table that contains the actual and targeted growth rates. For the first question, we use a gauge that compares the overall 2025 sales, the orange color, and the number in blue, with the budgeted overall 2025 sales, the red line. Further, a card is used to show the overall growth percentage which can be compared with the budgeted percentage.
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For the second question, a column chart provides similar information for the different product lines. A column presents the growth rate for a product line. Product lines represented by green bars met the growth expectations. Product lines represented by red bars did not.
4. The overall 2025 sales target of 72,198 was met. Shrek sold 73,736 bikes in 2025. The overall growth percentage is 7.24% and higher than 5%. Four product lines met the expectations: Adventure, Fitness, Cyclocross, and Touring. The others did not. LO 7.2, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools.
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Data and Analytics in Accounting: An Integrated Approach
EX 7.4 1. The column chart ranks weekdays based on sales. It shows that revenues are higher during the weekend, especially Saturday, compared to weekdays.
2. The 100% stacked column chart compares the sales share of each product (part-to-whole) across weekdays. While meat generates the highest sales on all weekdays, deli sales present a large share on Sundays, chicken sales on Tuesday, and fish sales on Friday.
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Data and Analytics in Accounting: An Integrated Approach
LO 7.2-7.3, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
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Data and Analytics in Accounting: An Integrated Approach
EX 7.5 1. Are sales affected by temperatures? For example, should Ice Cow have more employees during hot days? 2. Correlation relationship: Do sales (revenues) go up when temperatures go up? 3. • Is the direction of the correlation positive or negative? It is positive (upwards), but nonlinear. Sales go up when temperature goes up. However, sales seem to go down again when it is very hot (90 degrees and higher). • Is the degree of correlation strong or weak (scattered)? It is fairly strong. • Are there any unexpected data points or outliers? July 4th had the highest sales, although it was not hot. July 14th might have been a rainy day. LO 7.2, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
EX 7.6 1. Should the number of candy trucks (stores) in the different cities be adjusted? What might be the best city to add candy trucks? 2. Deviation relationship: The 2021 number of trucks is used as benchmark. Time-series relationship: Change in number of trucks during the 2021-2025 period. 3. Compared to 2021, determine whether there is an increase or decrease in number of candy trucks and how strong the growth/decline, and thus deviation, has been. Among others, the chart shows that there has been a sharp decline in number of trucks in Los Angeles and this might indicate low demand and/or too much competition. The trend for San Francisco is just the opposite. LO 7.2-8.2, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools.
EX 7.7 1. The owner is exploring the salespeople’s performance. The visualization can explore several questions: • • • • •
What is the total number of cars sold by each salesperson (67, 64, 75)? How do salespeople compare based on number of cars sold? How much does each car type contributes to a salesperson’s overall sales? Have salespeople met their overall target? Have salespeople met the targets for the different car types.
2. Nominal comparison: Comparison of salespeople based on the number of cars sold. Deviation: Comparison, per salesperson, between the actual number of cars sold and the targeted number of cars sold. Part-to-whole: Shows how the different car types contribute to a salesperson’s sales. 3. Nominal comparison: Overall, Shanice had the best performance—she sold 75 cars. Deviation: Two salespeople did not meet their overall targets. However, the discrepancies are within the 10% range. 7-50
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Part-to-whole: Carlos had a strong performance for selling sedans but a poor performance for sports cars and SUVs. Part-to-whole, Deviation: None of the salespeople met the target for sports cars. Part-to-whole, Nominal comparison: Carlos is the strongest in selling sedans, Arun in selling sports cars, and Shanice in SUVs. LO 7.2-7.3, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools.
EX 7.8 1. Maggie is exploring, during the first quarter, how daily sales vary across two restaurants. Questions include: • • •
What is the average daily income for each restaurant? What is the variation in her daily income for each restaurant? Does the variation differ between the two restaurants?
2. Distribution relationship. 3. The average daily income ($) is about the same for both stores. The amounts for Dumpling are more consistent. For Wok, the amounts are typically lower, but there are several days where the amounts are much higher. LO 7.2, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools.
EX 7.9 1. They are exploring which hotel they should stay in. Questions they are interested in include: • • •
Which hotel is preferrable based on distance? Which hotel is preferrable based on rate? Which hotel chain is preferrable: Hilton (orange) versus Marriott (green)?
2. Geospatial, deviation, nominal comparison. 3. The analysis is based on rate (bubble size) and distance from the seminar location. Further, decisions can be based on preference of the hotel chain: Hilton or Marriott. A key insight generated by the chart is that some of the cheaper hotels, Marriot and Hilton, are close to the seminar location. LO 7.2-7.3, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools.
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EX 7.10 (A) 1. The structure in (A) enables the exploration of part-to-whole relationships. The total quantity (values) is broken down by warehouse (rows) and then by product type (columns). 2. Several questions can be answered by this exploration structure: • What is the total number of units in stock? • What is the total number of units stored in each warehouse? • What is the total number of units available per product? • What is the total number of units available per product in each warehouse? 3. A pivot table can be used to answer all four of these questions.
A clustered column chart is especially useful to compare the inventory structure—mix of products and their quantities—across warehouses.
4. The number of units available for a product differs sharply across warehouses.
(B) 1. The structure in (B) enables the exploration of a nominal comparison relationship. Warehouses (rows) can be compared based on the number of units in stock (values). Using the filter, this comparison can be done for a single product or for a combination of products. 2. Several questions can be answered by this exploration structure: • How many units, quantity on hand, are stored in each warehouse? • What warehouse has the highest number of units? • What warehouse has the lowest number of units? • What warehouse has the highest number of “M1750” items.
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3. A column chart can be used for nominal comparison purposes and the warehouses can be ranked. A “Product” slicer can be used for filtering purposes.
4. New York currently has a larger inventory then the other warehouse locations and has a large inventory for three of the items.
(C) 1. The structure in (C) enables the exploration of a nominal comparison relationship. Products (rows) can be compared based on the number of units in stock (values). The filter can be used to select the product types that have reached the reorder point. 2. Several questions can be answered by this exploration structure: • How many units, quantity on hand, are available for each product? • What product has the highest quantity on hand? • What product has the lowest quantity on hand? • What products have reached the reorder point? 3. A column chart can be used for nominal comparison purposes. A horizontal line can be added that shows the reorder point.
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4. M2450 and M3750 have reached the order point. There is substantial surplus inventory for M2950. LO 7.2,7.3, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools.
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EX 7.11 Insight 1: Laureen’s revenues are showing an upward trend. We used a line chart to explore a time series data relationship, which is how revenues changed during the 2021-2025 period.
Insight 2: Softer-Ice’s revenues are growing faster than Laureen’s, while YuMeeS revenues are slowing down. We used a line chart to explore a time series data relationship, which is a comparison of trends for the three ice cream stores for the 2021-2025 period.
Insight 3: Laureen’s market share is slightly decreasing. Softer-Ice is gaining what Yumee is losing. We used a 100% stacked column chart to explore a composite trends data relationship, which is how have market shares (part-to-whole) changed during the 2021-2025 period (time series).
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LO 7.2-7.3, BT: AP, Difficulty: Medium, TOT: 15 min., AACSB: Analytic, AICPA AC: Technology and Tools.
EX 7.12 1. A nominal comparison of the tax groups, determined by the size of the total assets, based on the tax rate. The latter is calculated by dividing Total Income Tax After Credits by Income Subject to Tax. 2. A bar chart can be used to explore the nominal comparison relationship.
3. The tax rate, and thus the average income tax, goes down for corporations with a large asset total. 7-56
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EX 7.13 1. The pie chart (a part-to-whole relationship) in the upper left corner of the report shows that less than 10% (8.71%) of the total money was requested by small corporations. 2. The pie chart (a part-to-whole relationship) in the right upper corner of the report shows that 20% of the budget was given to small corporations. By putting a budget in place, small corporations receive a bigger part of the pie. 3. Additional information provided in the report is what percentage of the requested money each group received—Small: 61%, Not-Small: 23%
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EX 7.14 1.
2. It shows that almost 90% of the complaints are caused by “Unfriendly Staff” and “Unexpected Fees”. LO 7.3, BT: AP, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Technology and Tools.
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EX 7.15 1. The following is a possible report for the given exploration structure:
2. • • •
The Store and Product slicers are filters, not visualizations of data relationships. The line chart represents a time series: How have revenues changed during the 2021-2025 period for both stores? The 100% stacked column chart helps explore changes (time series) in the contribution of each of the different product categories (part-to-whole) to revenue (composite trend).
3. Example questions that can be explored with this dashboard: • How do the revenue trends for the two stores compare?
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SOLUTIONS FOR PROFESSIONAL APPLICATION CASE (Students Answers Will Vary) PAC 7.1. Auditing: 1. The column chart shows that a substantial number of orders has delivery issues. Red indicates issues. Green indicates no issues. Blue indicates open orders.
The pie chart (part-to-whole) further indicates that almost half of the orders have issues.
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2. The following report can be used to assess the risk level of each supplier. The top part of the report shows, per supplier, the number of open orders (blue), deliveries without issues (green), and deliveries with issues (red). The lower part of the report let’s us explore the nature of the issues. For example, of the eleven deliveries by MEMOA with issues (red color), six of them are canceled deliveries, while five were disruptive in nature. One of the insights that can be collected from this chart is that, given NoTable’s policy of selecting suppliers based on price, often orders go to suppliers who have a poor delivery record--MEMOA being a great example.
Risk labels can be assigned to suppliers using an algorithm such as the following: • • •
Safe: at most 10% of the orders have issues (green) Risky: more than 10% but less than 50% of the orders have issues (white) High-risk: more than 50% of the orders have issues (red).
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Note that open orders are excluded from the ratio calculations.
3. This table can also be used for tiering purposes. Taking price into consideration, consider Tier 1 vendors (green) first. PAC 7.2. Managerial Accounting: 1. Percentages can be used to indicate the magnitude of the variances (deviations). The top part of the following illustration shows that, overall, labor and raw material variances are all positive. A positive variance means that the actual cost is lower than the estimated cost. This is desirable for NoTable given that their prices are based on the estimated cost. 2. The lower part of the following figure shows the variances for each of the five designers. A clustered column chart is used to break down the overall variance into labor and raw material variances. An insight is that the magnitude and direction of the variances strongly vary among designers. This suggests that a review of the estimation process might be necessary to improve their accuracy.
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PAC 7.3 Financial Accounting: 1. The analysis shows that NoTable’s credit policy does not work, which might result in cash flow issues. Early, on time, and late payments are compared based on both number of transactions and dollar amount. The top part of the following visualization presents a nominal comparison, while the lower part presents a part-to-whole relationship. More than 40% of the transactions are paid late.
2. The following bar charts show that, both in terms of number of transactions and amount, NoTable has serious collection issues. Many of the transactions that haven’t been paid yet are overdue and these transactions represent a substantial amount of money.
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3. The stacked bar chart compares how much money each customer owes to NoTable (nominal comparison). In addition, it shows how much of the accounts receivable is outstanding (<= 30 days), and how much is overdue (> 30 days) (part-to-whole). It is noticeable (insight) that a large chunk of the credit overdue is given to two customers: Carl Rooks and Petrov Petrovski. This is an indication of accounts receivable concentration and a substantial credit risk. You could recommend that, at least for now, there should be no additional sales to these two customers.
PAC 7.4 Tax Accounting: 1. The top part of the following illustration shows that NoTable should have collected more than sixty-three thousand dollars in sales tax from its customers. 2. The bottom of the illustration shows how much tax NoTable should have collected and remitted to the different states its customers live in.
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CHAPTER 8 Interpreting Data Analysis Results Learning Objectives: LO 8.1: Compare data analysis interpretation and data exploration. LO 8.2: Apply critical thinking to data analysis interpretation. LO 8.3: Determine if the data analysis results answer the question and align with the objective of the analysis. LO 8.4: Evaluate the validity and reliability of descriptive and diagnostic data analysis results. LO 8.5 Evaluate the validity and reliability of predictive and prescriptive data analysis results.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. A
12. D
LO 8.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
LO 8.3, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA BC: Strategic Perspective
2. B
13. C
LO 8.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation.
LO 8.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
3. D
14. A
LO 8.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
LO 8.4, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
4. A LO 8.1, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA BC: Strategic Perspective
5. B LO 8.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
6. D LO 8.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
7. C LO 8.2, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
8. A LO 8.2, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
9. C LO 8.2, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA BC: Strategic Perspective
10. C LO 8.3, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA BC: Strategic Perspective
15. C LO 8.4, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
16. B LO 8.4, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
17. B LO 8.4, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
18. C LO 8.4, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
19. D LO 8.5, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
20. A LO 8.5, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
21. C LO 8.5, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
11. A
21. B
LO 8.3, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
LO 8.5, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
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22. D
24. A
LO 8.5, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
LO 8.5, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
ANSWERS TO REVIEW QUESTIONS
1. Data exploration is about understanding data, whereas data analysis interpretation involves understanding the analysis you prepared using the data. Data analysis interpretation is the process of evaluating an analysis to understand the meaning of that analysis. LO 81, BT: C, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
2. Data analysis interpretation involves reviewing an analysis to determine if (1) it makes sense, and (2) that the analysis results are valid and reliable. LO 8.1, BT: K, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
3. Identifying the stakeholders helps us to interpret the analysis from the stakeholder’s perspective. If we don’t understand the stakeholders, we may fail to understand the impact of the interpretation on those affected by any decisions based on the analysis. LO 8.2, BT: C, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Risk Assessment, Analysis, and Management
4. 1. Internal: management, employees. External: stockholders, analysts, vendors, creditors, federal, state, and local tax authorities. 2. To convey to stakeholders the change in sales figures by quarter for the years 2016, 2017, 2018, 2019, and 2020. 3. • How to calculate percent change • The retail industry • Revenue cycle • Revenue recognition 4. Potential data risks: Completeness, accuracy, timeliness, internal controls over data storage. Potential analysis risks: Was the correct method and data used? Does the analysis answer the question/objective? 5. Second quarter 2020 had a large increase in sales compared to other years. Year-over year sales have been flat since 2016. LO 8.2, BT: A, Difficulty: Hard, TOT: 8 min., AACSB: Analytic, AICPA AC: Risk Assessment, Analysis, and Management
5. It is important to consider if there are alternative explanations of the results of an analysis or if there are alternative ways to conduct the analysis that might be more appropriate. Thinking about alternative ways to view the analysis helps to determine whether the most appropriate analysis
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was used. If we are sure we have chosen the best alternatives, then we can have more confidence in our interpretation of the results. LO 8.2, BT: A, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Risk Assessment, Analysis, and Management
6. An analysis makes sense if it has a clear meaning. An analysis should be evaluated to confirm it answers the intended question, aligns with the objective, and that the results are reasonable given the question or objective. LO 8.3, BT: K, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Measurement, Analysis, and Interpretation.
7. Are the data used in the analysis reasonable given the question or objective of the analysis? Is the analysis method reasonable given the question or objective of the analysis? LO 8.3, BT: K, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Measurement, Analysis, and Interpretation.
8. Are the results of the analysis reasonable given what we know about the subject being analyzed? Are the implications of the analysis reasonable given what we know about the subject being analyzed? Does the analysis address the needs or concerns of the stakeholders? LO 8.3, BT: K, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Measurement, Analysis, and Interpretation.
9. Reliability means that the measures used in the analysis are accurate and consistent and that the data used are dependable and trustworthy. Validity means the data analysis measures what it is supposed to measure and represents reality. LO 8.4, BT: K, Difficulty: Easy, TOT: 5 min., AACSB: Knowledge, AICPA AC: Measurement, Analysis, and Interpretation.
10. An outlier is an observation that is much different than the other observations but is still a legitimate observation if we expect that it could happen again. An anomaly is also an observation that deviates from what is normal or expected, but it is a mistake or an unusual occurrence that we would not expect to happen again. LO 8.4, BT: C, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Measurement, Analysis, and Interpretation.
11. The validity of a regression model is evaluated by reviewing the model variables to confirm they make sense and that the model is a realistic representation of the phenomenon we are interested in predicting. In other words, does the model measure what it is supposed to measure, and does it represent reality? LO 8.5, BT: C, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Measurement, Analysis, and Interpretation
12. The adjusted R-square is one of the regression statistics used to evaluate the reliability of the regression model. The closer to 1, the better the fit of the regression line to the data. We interpret the adjusted R-square of the percentage of the dependent variable that is explained by the independent variables in the model. LO 8.5, BT: C, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Measurement, Analysis, and Interpretation
13. The independent variables in a regression model are the variables believed to influence changes in the dependent variable. When the regression is performed, the intercept and coefficients of the independent variables are estimated. This represents the equation of the line 68
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that best fits the data. Evaluate whether the independent variables are reliable by examining the p-values for each variable. If the p-value is 0.05 or lower, we can say that the corresponding independent variable is significant and should remain in the final model. LO 8.5, BT: C, Difficulty: Medium, TOT: 5 min., AACSB: Analytic, AICPA AC: Measurement, Analysis, and Interpretation
SOLUTIONS TO BRIEF EXERCISES BE 8-1. data, analysis LO 8.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Risk Assessment, Analysis, and Management
BE 8-2. valid, reliable LO 8.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Risk Assessment, Analysis, and Management
BE 8-3. 1. The question is which departments have the highest spending. The analysis does answer that question. Public works -Utilities has the highest spending, as shown in the bar chart. 2. Yes, the preparer indicates that they used all the transactions for expenditures during the respective period, which is what the question asked. 3. The preparer confirmed that they prepared the analysis using all the transaction data for the year 2025 for every department. They also confirmed that the total agreed to the appropriate line item for expenditures in the general ledger, and that there were no intercompany transactions to eliminate. LO 8.1, BT: AP, Difficulty: Hard, TOT: 8 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
BE 8-4. 1. Knowledge that would be important: • • • • • •
Accounting knowledge: Break-even point measures. Accounting knowledge: The formula for the break-even point. Accounting knowledge: The differences between fixed costs and variable costs. Other knowledge: Changes in the company’s sales price between 2023, 2024, and 2025 for each scooter model. Other knowledge: Changes in the company’s strategy for fixed costs or variable costs among the years 2023, 2024, and 2025. Other knowledge: Number of all models of scooters produced and sold (to be confident that all models are included in the graph).
2. The stakeholders for this analysis are primarily internal. The manager and superiors are decision-makers who determine sales price and production strategy. 3. The purpose of the analysis is to determine the break-even point for each model of scooters sold by the company. The break-even point is the quantity of units before a profit will be
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generated (the point at which there is no loss and no profit), which was the basis of the request.
The
graph provides information for decision-makers to answer the question. LO 8.2, BT: AP, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
BE 8-5. Question
Type of Risk Addressed
1. Did the preparer have any potential biases that could have influenced the preparation of the analysis?
ANS: C. Potential bias risk
2. Are the data used in the analysis the most recent available? 3. Were all the necessary and appropriate data included in the analysis? 4. Were appropriate internal controls in place to ensure the data used was correct? 5. Was the correct method used to perform the analysis? 6. Is the analysis missing relevant data?
ANS: A. Potential data risks
7. Did the analysis meet the original objective and answer the question?
ANS: B. Potential analysis risk
ANS: C. Potential bias risk ANS: A. Potential data risks ANS: B. Potential analysis risk ANS: A. Potential data risks
LO 8.2, BT: K, Difficulty: Easy, TOT: 8 min., AACSB: Knowledge, AICPA AC: Measurement Analysis and Interpretation
BE 8-6. Statement 1. The person performing the analysis wants to prove a predetermined assumption. 2. The person performing the analysis selected the data subjectively. 3. The person interpreting the analysis wants to prove a predetermined assumption. 4. The person interpreting the analysis focuses on results that support the existing assumption. 5. The person interpreting the analysis considers only a sample of the data rather than the entire population.
Type of Bias ANS: A Confirmation bias ANS: B. Selection bias ANS: A Confirmation bias ANS: A Confirmation bias ANS: B. Selection bias
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6. The person interpreting the analysis ignores aspects of the analysis that contradicts the existing assumption.
ANS: A Confirmation bias
LO 8.3, BT: C, Difficulty: Medium, TOT: 10 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
BE 8-7. 1. No. The visualization does not address the question because it depicts the relationship between customer satisfaction and guest complaints, which is not Roberto’s objective. 2. No. While the analysis provides a visualization of the relationship between customer satisfaction score and guest complaints, the internal audit department would need to perform a correlation analysis to conclude a statistical relationship between the two variables. 3. Yes. The analysis depicts a visualization between the two variables and is therefore sufficient for a presentation. LO 8.3, BT: AP, Difficulty: Hard, TOT: 10 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
BE 8-8. A large difference between mean and median is a good indication that there are extreme values in the data set. Specifically, there may be a larger quantity of smaller sales transactions and several large sales transactions that are impacting the calculation of the mean and median. LO 8.4, BT: C, Difficulty: Medium, TOT: 6 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
BE 8-9. Question
Answer
1. What does an inverse relationship mean between two variables?
ANS. B. A negative correlation coefficient
2. How would you interpret the correlation coefficient for annual customer complaints and customer satisfaction score?
ANS. D. There is a strong negative correlation between the variables
3. How would you interpret the correlation coefficient for minutes spent at the property and customer satisfaction score?
ANS. C. There is a strong positive correlation between the variables
4. How would you interpret the correlation coefficient for number of employees and customer satisfaction score?
ANS. E. There is a moderate positive correlation between the variables
LO 8.4, BT: C, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
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BE 8-10. 1. Independent variables: Beds, outpatient visits, births 2. Dependent variable: Total hospital expenses 3. R- square 4. 76.1% LO 8.5, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
BE 8-11. 1. Knowledge needed: •
Assumptions used in the model, such as the increase/decrease in sales and variable costs percentages. • What are included as variable costs? • Potential price changes in the sale of models. 2. Yes, assuming we know the sales tax percentage. We just need to apply it to the calculated figures in the what-if analysis. LO 8.5, BT: AP, Difficulty: Hard, TOT: 8 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
SOLUTIONS TO EXERCISES EX 8-1. Student answers will vary, but they should work through the five questions that help us determine if an analysis makes sense and if the results are valid and reliable. Step 1: Determine if the analysis makes sense. 1. Does the analysis answer the intended question and align with the original objective of the analysis? • The objective of the analysis is to evaluate business expenses for the city departments. This analysis provides an overview of the total spending for the top six departments. • Yes, the analysis makes sense for the objective of identifying total spending by the department for the top six departments. 2. Were the correct data and appropriate analysis used to perform the analysis? • To determine this, confirm the data is from the most current year or time period, and whether it agrees to the totals in the financial records. • The analysis is descriptive which is appropriate. The bar chart reveals which departments have the highest business expenses. 3. Are the results reasonable? • Not knowing what spending has been in the past makes it difficult to determine if the results are reasonable. It appears the fire department has
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Step 2: Verify the results valid and reliable. 4. Does it measure what it is supposed to measure? • The analysis is measuring business expense spending, so it appears to be valid. 5. Are the results accurate? • Confirm the correct data was used to determine if the analysis is accurate. LO 8.1, BT: AN, Difficulty: Hard, TOT: 15 min., AACSB: Analytic, AICPA AC: Measurement Analysis and Interpretation
EX 8-2. Student answers will vary, but they should work through the five questions that help us determine if the analysis makes sense and if the results are valid and reliable. Step 1: Determine if the analysis make sense. 1. Does the analysis answer the intended question align with the original objective of the analysis? • The original objective was to determine if contracts are being rewarded without the appropriate bidding process. • This analysis shows the vendors with the highest total amount of spending and the number of transactions. An energy company has the most transactions, but the least amount of spending. This is reasonable since energy bills likely come to each department on a monthly basis. 2. Were the correct data and appropriate analysis used to perform the analysis? • To determine this, confirm the data is from the most current year or period and whether it agrees to the totals in the financial records. 3. Are the results reasonable? • Not knowing which transactions would require a bidding process makes it impossible to say if the results are reasonable.
Step 2: Verify the results are valid and reliable. 4. Does it measure what it is supposed to measure? Identifying the top vendors is a good way to start this analysis, but we don’t know from this analysis if they followed the bidding process. 5. Are the results accurate? • We don’t know from this analysis if the proper bidding process was followed. If these are the top ten vendors, then we have a starting point to ask for more information regarding bidding. LO 8.1, BT: AN, Difficulty: Hard, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-3. Student answers will vary depending on the selected analysis.
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1. Internal: management, employees. External: stockholders, analysts, vendors, creditors, federal, state, and local tax authorities. 2. Answers will vary depending on the analysis chosen 3. What are alternative explanations for the percent increases/decreases in sales? Are there other analyses that might better explain the percent changes in sales? 4. Potential data risks: completeness, accuracy, timeliness, internal controls. Potential analysis risks: Was the correct method and data used? Does the analysis answer the question/purpose? 5. Answers will vary depending on the analysis chosen, but should include accounting specific knowledge, industry knowledge, and knowledge about the measurements in the visualization. 6. Have we reviewed analyses like this in the past? Can we use that experience to interpret this analysis? How can we use what we learn from this analysis to better interpret similar analyses in the future? LO 8.2, BT: AP, Difficulty: Hard, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-4. 1. Internal: management, employees. External: stockholders, vendors 2. To understand accounts receivable aging. 3. What are alternative explanations for the balances in accounts receivable? Are there other analyses that might better explain the accounts receivable outstanding balances? 4. Potential data risks: completeness, accuracy, timeliness, internal controls. Potential analysis risks: Were the correct methods and data used? Does the analysis answer the question/objective? 5. Knowledge needed by both the preparer and interpreter: • Accounts receivable • Aging report for accounts receivable • Revenue cycle • Cash collection cycle 6. Have we reviewed accounts receivable balance analyses in the past? Can we use that experience to interpret this analysis? How can we use what we learn from this analysis to better interpret account receivable aging analyses in the future? LO 8.2, BT: S, Difficulty: Hard, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-5. 1. Internal: management, employees. External: stockholders, analysts, vendors, creditors, 2. To compare Walmart cash flows to competitor cash flows. 3. Are there alternative explanations for the differences between the company’s cash flows? Are there other analyses that might better compare Walmart’s cashflows to competitors? 4. Potential data risks: Completeness, accuracy, timeliness, internal controls. Potential analysis risks: Was the correct method and data used? Does the analysis answer the question/objective? 5. Knowledge needed by both the preparer and interpreter: • Cash flow analyses
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• Retail industry 6. Have we reviewed cash flow analyses in the past? Can we use that experience to interpret this analysis? How can we use what we learn from this analysis to better interpret cash flow analyses in the future? LO 8.2, BT: S, Difficulty: Hard, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-6. 1. Profit margin is a good way to identify top customers because it allows us to see which customers provide the best profit margin. However, we do not know which customers we sell more to. A customer with a lower profit margin but a higher amount of sales may provide more profit to us than a customer with a higher profit margin but only a few sales. 2. Like the answer to question #1, profit margin is a good measure, but total sales and total profit would be better measures to identify the top five customers. We should look at top customers by more than one dimension. 3. The results show several years, so it is difficult to determine which customers are the top customers consistently. Ziggi’s has the highest profit margin in 2023, but it has the lowest in 2021. 4. The implications are that top customers are determined only on profit margin. However, top customers should include more than one measure. 5.
The analysis provides a portion of the information, but more analysis is needed.
LO 8.3, BT: S, Difficulty: Hard, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-7. 1. The objective of the analysis is to identify if there is any unusual journal entry activity. The data used are the total amounts of journal entries by the employee and the total number. The data appears to be reasonable. 2. The analysis method is a comparison of the average amount of journal entries, the total dollar amount of journal entries, and the total number of journal entries by the employee. The analysis allows us to see if there are any employees that have unusually high amounts. The method appears to be reasonable. 3. We would need more information to determine if the results are reasonable, but based on the data it seems that one employee has the highest total dollar amount of journal entries, but has only posted 10 entries. We should examine this employee further. 4. Yes, the implications are reasonable. The highest number of journal entries are prepared automatically by the accounting system. 5. Yes, the analysis helps identify an unusual amount of journal entries and amounts for an employee. LO 8.3, BT: AN, Difficulty: Hard, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-8.
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1. The data used in the analysis are total sales per month for each model from 2022 to 2025. Given that we are trying to determine if the Celeritas model should be eliminated, it is reasonable to want a sense of sales trends. It is also reasonable to want a forecast of sales for the next year, and the analysis includes that information. 2. The objective of the analysis is to provide insight into current and projected future sales for the Celeritas model. It is reasonable to want to see how the other models are performing to gain an understanding of total sales for Super Scooters. 3. We know that the market demand is increasing for electric-powered scooters. The analysis appears reasonable given that trends for electric-powered scooters are increasing (Lazer and Captain) and sales trends for the gas-powered scooter (Celeritas) are flat. 4. The implication of the analysis is that the Celeritas and the Kicks model are not performing as well as the Lazer and Captain. This is reasonable knowing that the market for electric-powered scooters is better than gas-powered scooters. 5.
The stakeholder’s concerns relate to the decision to eliminate the Celeritas model. While the analysis does address this issue, it is limited to only an analysis of sales. Determining whether to keep or drop a product should include an analysis of costs as well as sales.
LO 8.3, BT: AN, Difficulty: Hard, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-9. 1. The interpretation is that the Captain is the most popular model and the Lazer is the least popular. However, this is sales dollars, so if one is priced higher than the other, then it is impossible to tell which one has the highest sales volume. 2. Yes, we would like to see more analysis. It is not possible to know if this is one year, one month, etc. Also, it only shows sales dollars. 3. We would like to see sales volume since more popular models will have a higher sales volume. It would also be helpful to see comparisons from year to year. LO 8.3, BT: AN, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-10. 1. Yes, the analysis shows a comparison of sales each year by region. The darker the shade, the higher the sales. The visuals shows that the West has the highest sale every year except for 2023, where they had a slight decline. After 2023, the West region had increasing sales each year. 2. Yes. Performance should be evaluated by more than just sales. 3. They should show contribution margin by region for the years. LO 8.3, BT: AN, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-11.
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1. There is a strong positive correlation (0.782) between outpatient visits and total expense. An increase in outpatient visits is strongly correlated to an increase in total expense. 2. There is a strong positive correlation (0.777) between the number of beds and total expenses. The more beds a hospital has, the higher the total expense. 3. There is a moderate correlation (0.667) between the number of beds and births. The more beds a hospital has the higher the number of births. LO 8.4, BT: AN, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-12. 1. a. The mean represents the average profit of all the hotel locations. Average profit for Denton Hospitality hotels is $547,691.05 b. The median profit represents the middle value in the data set. If the profit by hotel was listed from lowest to highest, the middle value is $553,868.00. c. The standard deviation of profit for Denton Hospitality hotels is $309,049.66. The standard deviation represents how spread out the data points are from the mean of $547,691.05. The standard deviation is more than half the amount of the mean, so it is an indication that there is a lot of dispersion of profit among the hotels. 2. Yes, the scatterplot does support the descriptive statistics. The data points are very dispersed, as indicated by the standard deviation. However, many of the data points are around the mean amount of $547,691.05. LO 8.4, BT: E, Difficulty: Hard, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-13. 1. The pivot table shows the total sales for each country made either by sales personnel or online. In Canada the amounts are nearly equal, with online slightly higher. In Mexico more sales are made offline than online, and in the United States more sales are made online than by sales personnel. 2. One Stop Shop has too many sales made by sales personnel to simply cut that type of sales channel. Additional analyses could include sales channel sales by vendor and profit margin by sales channel. LO 8.4, BT: AN, Difficulty: Medium, TOT: 10 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-14. 1. Yes, the analysis does show trends over time. It seems that there are higher sales in the second quarter of the year.
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2. The pattern is not very clear when viewing it by quarter. Also, there could be different patterns for different countries, regions, or products. Additional analysis by month and by country should be performed. LO 8.4, BT: S, Difficulty: Hard, TOT: 15 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-15. 1. The variance amount of 388,434.46 is the sum of all the squared deviations divided by the number of observations. Variance is not easily interpreted because it is not in the same unit of measurement as sales (it is a squared amount). On the other hand, the standard deviation is the square root of the variance number, which essentially converts it back to the same units as the mean—in this case sales dollars. In this example the mean amount of a sale is $229.86. The standard deviation is $623.25. So, the average distance between an observation (in this case a sale amount) and the mean is $623.25. Standard deviation can indicate the data points relationship to the mean: •
A low standard deviation indicates that the data points tend to be very close to the mean.
•
A high standard deviation indicates that the data points are spread out over a large range of values.
2. Considering that the standard deviation of $623.25 is about 2.7 times higher than the mean, it does seem to be a high deviation. The scatterplot supports this, as there are many sales amounts that are much higher than the mean. 3. Students can use Microsoft Excel to calculate Descriptive Statistics using the Data Analysis tool in the Data menu. The scatterplot can also be recreated in Microsoft Excel by highlighting the data and using the Insert Menu to choose a ScatterPlot graph. LO 8.4, BT: S, Difficulty: Hard, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-16. 1. The adjusted r-square of 0.657 indicates that number of rooms, hours worked, front desk, hours worked, GM, and house worked, housekeeping can explain 66% of total expenses. 2. The model is significant. The F-statistic is well below 0.05, meaning that the model is better than no model at all. 3. Coefficients: • Intercept: This number represents where the regression line will cross the y-axis. It is a fixed amount in the equation. • Number of rooms: Total expenses will increase by $2,670.21 for each room in the hotel. • Hours Worked, Front Desk: For each hour of front desk work, expenses will increase by $29.30. • Hours Worked, GM: Total expenses increase by $52.01 for each hour a general manager works. • Hours Worked, Housekeeping: Each hour of housekeeping adds $13.41 to total expenses. 4. The variables in the model make sense. We would expect all the variables that are included in the model to contribute to total expense.
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5. Since we are only explaining 66 % of total expenses, there are other factors that must explain the remaining 34% of expenses. Consider adding: maintenance expenses, electricity, gas, water, and supplies. 6. The model would be: ($131,136.43) + $2,670.21 (150) + $29.30 (9,200) + $52.01 (1,500) + $13.41 (12,100) = $779,381.1 LO 8.5, BT: E, Difficulty: Hard, TOT: 25 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-17. Students should first recreate the what-if analysis. 1. The objective of the analysis is to better predict net revenue for the next year. 2. The model does not help predict revenue. Rather, it provides a prescriptive look at what net revenue might be based on changes in assumptions for variable costs. 3.
The measures are accurate and consistent.
LO 8.5, BT: S, Difficulty: Medium, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-18. 1. Break-even units needed:
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2. Number of tents U.S. Outdoor Adventures must sell to make a profit of $100,000:
3. U.S. Outdoor Adventures would like to know how many units they need to sell to breakeven or to make a $100,000 profit. The Goal Seek analysis is a valid model because it calculates the break-even amount by going through scenarios until it reaches a profit of $0. The same is true for the calculation of units needed to generate a profit of $100,000.
The measures used in the goal seek are accurate and consistent, so the analysis is reliable. LO 8.5, BT: S, Difficulty: Medium, TOT: 25 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-19. 1. The objective of the analysis is to predict sales for 2026. 2. The model uses a trendline to determine an equation for revenue, which will allow management to predict sales for 2026. 3. A trendline can be helpful for understanding sales predictions; however, it is a limited method for prediction. The r-square is 0.0487, indicating the trendline model only explains about 5% of sales. LO 8.5, BT: S, Difficulty: Medium, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
EX 8-20. 1. Regression statistics interpretation: • The adjusted r-square of 0.391 indicates that the discount, discount dollar amount, and whether the sales were in the west, east, or central region can explain 39% of total sales.
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•
2. 3.
4. 5. 6.
The standard error of $486.13 represents the variability of total sales to predicted total sales based on the model. A small standard error indicates the data are clustered around the regression line. Generally, a standard error that is less than the standard deviation is considered small. The model is significant. The F-statistic is below 0.05, meaning the model is better than no model at all. Model coefficients: • Intercept: This number represents where the regression line will cross the y-axis. It is a fixed amount in the equation. • Discount Percentage: For every additional 1% discount, sales decreased by $438.73. • Discount $: For each additional dollar of discount, sales are increased by $2.42 • Region West: If the sale is in the west region, it will be $0.12 higher. • Region East: If the sale is in the east region, it will be $17.73 lower • Region Central: If the sale is in the central region, it will be $18.59 higher. The variables in the model make sense. It is reasonable to believe that the amount of discount and where the sale takes place could influence the sale amount. Since only 39 % of total sales are explained, there are other factors necessary to explain the remaining 61% of sales. Consider adding: season, product type. $221.75 -$438.73 (20%) + $0.12 (1) + $2.42 (100) = $376.12
LO 8.5, BT: S, Difficulty: Hard, TOT: 25 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
SOLUTIONS TO PROBLEMS
PR 8-1.
1. a. Stakeholders include the customers, shipping partners, and employees. b. The purpose is to determine if there is a relationship between shipping costs and order priority and if U.S. Outdoor adventures should charge customers for shipping. c. We could perform a correlation analysis between shipping mode and shipping priority. We would expect that faster shipping modes would be related to higher priority sales. d. There is always the possibility that we do not have all the relevant data or that there is inaccurate data. There is the risk that if the company starts charging customers for shipping, they will lose business. e. It is necessary to understand how shipping costs are recorded, how to summarize the data in a way that will allow comparisons between years, and how to calculate the correlation between shipping method, shipping priority, and costs. f. Consider prior completed analyses that might be similar to this analysis. Consider our own experiences with shopping online and paying for shipping costs. 2. Yes, we are concerned with the shipping method, shipping costs, and shipping priority. That is the
data used in the analysis.
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3. It is reasonable to use descriptive analysis, but it is necessary to do more than simply summarize the total number of shipments. 4. The results show that shipping costs are rising, which is what U.S. Outdoor Adventures expects.
However, the summary of total number of shipments by shipping mode does not provide any insight as to which shipping modes are increasing or decreasing. 5. The implication of the shipping mode analysis is that there are many high-priority orders shipping in
the slowest mode (standard class). This seems odd and should be investigated further. In addition, there are medium priority orders that are shipping next day. That seems unnecessary. 6. The concern is that shipping costs are rising, and the analysis is only the first step in addressing this
concern.
7. Solutions to this problem will vary based on the student’s perspective and there may be different analyses. Possible solutions include: a.
This column chart shows the trend over 2023 – 2025 for each type of shipping mode. This allows showing which shipping modes not only have the highest volume, but also how the volume has changed over time.
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This column chart also shows trends in average shipping costs by shipping mode. b. Interpretation: Although the total orders by shipping mode are increasing, the second chart shows that the average cost of shipping is decreasing for all modes of shipping except the same day. U.S. Outdoor Adventures should consider limiting same-day shipping to only orders that are critical. LO 8.1, 8.2, 8.3, 8.4, BT: E, Difficulty: Hard, TOT: 35 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
PR 8-2. 1. a. Stakeholders include the hospital management, employees, and patients. b. The analysis was prepared to help All Care budget for expenses. c. The model appears to be very strong, so it looks like it is the best alternative. However, correlation analyses could also be performed to be sure there are no multicollinearity issues. d. There is always a risk that the data is incomplete or inaccurate. Another risk is excluding variables that should be in the regression model. There is also the risk that the output of the regression is interpreted incorrectly. e. It is important to understand how to prepare budgets, what expenses hospitals typically incur, and how to perform and interpret regression analysis. f. Apply knowledge of how to perform and interpret regression from other projects to this project. 2. a. The adjusted r-square of 0.9643 indicates that the admissions, census, outpatient visits, births, payroll expense, personnel, and whether the hospital has births or not can explain 96% of total expenses.
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b. The model is significant. The F-statistic is below 0.05, meaning that the model is better than no model at all. c. Coefficients: Intercept: $4,191.08. This number represents where the regression line will cross the y-axis. It is a fixed amount in the equation. Admissions: For each additional admission of a patient, expenses will increase by $6.01 Census: For each additional person in the hospital, expenses decrease by $57.72 Outpatient visits: For each outpatient visit, expenses decrease by $0.02 Births: Each birth reduces expenses by $9.98. Payroll expense: For every $1 of payroll expense increase, total expenses increase by $1.80. Personnel: Each additional employee increases total expenses by $30.94 Births or Not: This is either a 1 if the hospital has a maternity ward or 0 if not. If the hospital has a maternity ward, expenses are $5932.87 lower than hospitals without a maternity ward. d. The variables all seem to be logically related to total expenses. However, some of the coefficients do not make sense. Births and having a maternity ward lower expenses, which does not seem to make sense. The coefficient for the census is also negative, which seems odd. Perform a correlation analysis of the variables to be there are no multicollinearity issues. For example, Personnel and Payroll expense are likely correlated and only one should be in the model. The same is true for Births and Births or Not. e. Since we are explaining 96% of total expenses, the model seems to be very good. The variables with negative coefficients should be examined further. 3. Regression model that predicts total expenses using the variables: Admissions, Outpatient Visits, Births, and Personnel:
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a. Although the adjusted r-square is lower at 0.9192, it is still very high. The model explains 92% of total expenses. The correlated variables of personnel and payroll expense are eliminated by only having Personnel in the model. The same is true for Births and Births or Not. Overall, it is a better model because the coefficients make more sense. b. Interpretation: Intercept: $14,129.79. This number represents where the regression line will cross the y-axis. It is a fixed amount in the equation. Admissions: For each additional admission of a patient, expenses will increase by $5.67. Outpatient visits: For each outpatient visit expenses decrease by $0.08 Births: Each birth reduces expenses by $5.21. Personnel: Each additional employee increases total expenses by $132.15. LO 8.1, 8.2, 8.3, 8.5, BT: E, Difficulty: Hard, TOT: 40 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
SOLUTIONS TO PROFESSIONAL APPLICATION CASE Answers may vary. These are suggested solutions. 1. Auditing:
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1. Evidence that the Tableau output that the Tableau output is valid is that the visualization’s data fields match those provided in the data is reliable is that both the 2024 and 2025 total sales agree to the client-provided income statement (rounded). Evidence dictionary. 2. The frequency distribution table shows that the sales mix remains largely the same from 2024 to 2025; therefore, we would expect to see costs in a similar mix. 3. This information increases the risk of material misstatement as it relates to the collectability of the
accounts receivable outstanding for this customer. If a dominant customer fails to pay their balance, it could impact Ortho’s ability to pay their own bills. Using the 2025 sales data, we can calculate the effect of this dominant customer on total sales:
Note that the dominant customer contributes 21% of Ortho’s total sales. Losing this customer will significantly affect Ortho’s sales revenue. 4. Item # 1 is considered an anomaly as it is a negative dollar amount for sales. A negative dollar amount
for sales may indicate a sales return or it may indicate an error in processing a sales transaction or payment.
Item # 2 is a normal sale and is not considered an anomaly. This sale can be included as part of the normal detail testing for sales in substantive testing. Item # 3 is an anomaly as it represents a larger than normal sale amount during the period. This could represent a large sale to a customer.
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3) anomaly
Sales Transaction Total
40,000
2) normal sale
20,000 (20,000) (40,000) (60,000)
1)
(80,000)
2025 5. Analyses and interpretations may vary based on the student’s emphasis in the analysis. Suggested analyses may include:
Important items of note: •
•
There are null sales in 2024 which are contra sales (negative) and not related to a product description. These should be included in the discussion with management. Using the Tableau software, we may consider “viewing data” to understand the transactions that makeup with the negative amount in null. Metal on Metal (Titanium) sales exceeds that of other sales in both 2024 and 2025.
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Important items of note: • •
In 2025, NY is the site with the largest sales followed by Texas. This differs from the prior year where the largest quantity of sales occurred in Texas followed by California. Null items do not appear in this analysis, suggesting that the transactions are adequately coded for site location but not for product description. Further analysis is necessary to understand what data is lacking for the null items identified in the sales by product description analysis.
2. Managerial Accounting: 1. The line chart and the correlation analysis show a positive correlation between purchases and sales.
However, there is not enough information to answer the question about whether the individual sites are purchasing too many raw materials. 2. Analyses of purchases compared to sales should be performed for each site because they are responsible for their own raw material purchasing. In addition, the following analyses are needed: •
A comparison of sales and purchases by site
•
An analysis of the materials needed for production compared to units sold.
•
An analysis of purchases by vendor to see if there are any anomalies.
•
An analysis of purchases by employee to see if there are any anomalies.
3. Student analyses may vary based on their examination of the data. A possible solution includes:
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The illustration above shows that the Illinois site had a very large increase from 2024 to 2025 in purchases. The purchases of $140.7 million are much higher than all the other sites and well above the median purchase amount. This analysis compared to the one below of sales by site, shows that purchases are high in Illinois, but sales remained flat.
3. Financial Accounting:
1. The key stakeholders are investors because they will use this analysis to make their investment decisions and management for use in the review of the footnote disclosure 2. To create the ASC 606 reporting requirement for geographic sales, knowledge of the requirements in the standard and confidence that the analysis complied with those requirements is necessary. 3. No, this analysis is not sufficient for segment reporting. More knowledge should be acquired regarding t the requirements of segment reporting and how segment information should be reported. We may consider performing additional analyses to gain further insight regarding the various sales figures. 4. Student’s analyses may vary based on their on analysis emphasis. A proposed solution may include:
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4. Tax Accounting :
1. The Ortho, Inc. – Sales Tax Analysis chart illustrates total purchases by the five Ortho Inc. productions sites as compared to the total sales tax paid. The visualization assumes that all purchases are taxable but we cannot conclude that based only on the visualization. The visualization also assumes that all purchases were made in the state in which the production site is located. 2. The analysis is not sufficient because it does not provide a comparison of sales tax paid It also does not provide for the effective sales tax rate charged. We also collect sales tax on sales and that aspect is ignored in this analysis. Additional analysis is necessary as to what the effective tax rate charged is. We also need to know the taxable purchases versus the non-taxable purchases, because this can throw off the effective sales tax rate calculation. It would be best to present the findings by year because showing tax rate changes that can occur from year-to-year can help identify any increases in taxes paid. 3. Student responses to this question may vary. One example may include: When analyzing the Ortho, Inc.’s data sets by year, it was clear that the average sales tax rate charged on purchases was 7.0% across all locations, which does not align with the sales tax rates applicable to those locations based on the provided chart. Of note, sales tax should not
be charged on purchases of inventory that are meant to be resold. In these scenarios, a resale certificate should be provided to the supplier for the supplier to not charge sales tax. Sales tax should not be paid on inventory purchases because Ortho Inc. is using these materials to create products for resale. If the company pulls certain items from inventory for its own use, then use tax should be self-assessed in those instances. Orthoshould investigate why it has been paying sales tax on its inventory purchases. The following illustration was created with the intention of illustrating the relationship between total purchases and total sale tax paid on purchases. Total purchases and total sales tax are directly positively correlated to one another. When purchases increase, so do sales taxes paid because they are applied as a percentage of the purchase price.
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LO 8.1, 8.2, 8.3, 8.4, 8.5, BT: E, Difficulty: Hard, TOT: 40 min., AACSB: Data Analytics, AICPA AC: Measurement Analysis and Interpretation
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CHAPTER 9 COMMUNICATING DATA ANALYSIS RESULTS Learning Objectives: LO 9.1: Explain how a data story communicates data analysis results. LO 9.2: Summarize the steps for creating effective data visualizations. LO 9.3: Describe the characteristics of an effective data visualization. LO 9.4: Recognize misleading data visualizations. LO 9.5: Create an interactive data visualization presentation.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. C LO 9.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.1
2. A LO 9.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.1
3. A LO 9.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.1
4. C LO 9.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.1, Accounting Discipline: Financial Accounting
LO 9.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.2
10.B LO 9.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.2
11.C LO 9.2, BT: AP, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.2
12.D LO 9.3, BT: C, Difficulty: Easy, TOT: 2 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.3
13.B LO 9.3, BT: C, Difficulty: Easy, TOT: 2 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.3
5. A
14.D
LO 9.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.1
LO 9.3, BT: C, Difficulty: Easy, TOT: 2 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.2
6. B LO 9.1, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.1
7. B LO 9.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.2
8. B LO 9.2, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.2
9. A
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15.C LO 9.4, BT: AP, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.4
16.C LO 9.4, BT: AP, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.4
17.D LO 9.4, BT: AP, Difficulty: Medium, TOT: 3 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.4
18.A LO 9.5, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.5
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19.C LO 9.5, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.5
20.A LO 9.5, BT: K, Difficulty: Easy, TOT: 2 min, AACSB: Knowledge, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.5
ANSWERS TO REVIEW QUESTIONS 1. Effective communication of data analysis results can be achieved by following these suggestions: • Understanding the audience: What do they know about the topic? Are they internal or external to the organization? • Focus on the message: Do not focus solely on the numbers. Show the relationship between the numbers and the message you are trying to convey. • Put it in context: Consider the context of the analysis (information or persuasion) and the context of the data. • Strive for clarity: Clearly explain the data and results. • Tell a story: Follow storytelling best practices to engage the audience. LO 9.1, BT: K, Difficulty: Easy, TOT: 8 min, AACSB: Knowledge, AICPA BB: Strategic/Critical Thinking, Section 9.1
2. Putting the data in context means putting it in perspective. First, there is the context of the overall purpose of the analysis. For example, is the purpose of the data analysis to inform the audience or to persuade the reader? Then, there is the context of the individual analysis. For example, is the analysis focused on the company, or on just one department? The audience should be provided with the information they need to understand the context of the analysis. LO 9.1, BT: K, Difficulty: Easy, TOT: 8 min, AACSB: Knowledge, AICPA BB: Strategic/Critical Thinking, Section 9.1
3. To tell this data story, follow Freytag’s pyramid: • Introduce the problem: Labor expenses are highest in one region. • Explore the problem more deeply: Show the overall labor increase and then break it down to the employee level. • Share the main finding: Show which category of employees or specific employees are being paid unusually high amounts. • Share the solution: Show possible reasons for the higher wages or recommendations for employees that should be investigated further. • Offer a conclusion: Suggest controls that could uncover wage variances sooner. LO 9.1, BT: AP, Difficulty: Medium, TOT: 12 min, AACSB: Knowledge, AICPA BB: Strategic/Critical Thinking, Section 9.1, Accounting Discipline: Managerial Accounting, Financial Accounting.
4. The human mind is made to absorb stories. Stories create both physical and emotional responses. Research has shown that facts are up to 22% more memorable when told in a story. To make the maximum impact on the audience with data analysis, communicate the message by telling a story with the data. LO 9.1, BT: C, Difficulty: Medium, TOT: 12 min, AACSB: Knowledge, AICPA BB: Strategic/Critical Thinking, Section 9.1
5. It is important to verify that the data in any data analysis communication are accurate, complete, consistent, fresh, and timely. If the data do not have those attributes, we risk communicating incorrect information. In addition, communication using incorrect data could lead to poor business decisions. LO 2, BT: C, Difficulty: Easy, TOT: 8 min, AACSB: Knowledge, AICPA BB: Strategic/Critical Thinking, Section 9.2
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6. Because a novice audience will be unfamiliar with the topic of the data analysis, the communication of the analysis results will require more details and background information than an executive audience would need. An executive audience will be interested in only the most important highlights and insights. LO 2, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.2
7. A managerial audience will know the background information about the data analysis and will be interested in actionable results. Therefore, a managerial audience will be interested in recommendations for action based on the results of the analysis. An expert audience has deep knowledge of the topic and is interested in digging deeper into the analysis. LO 2, BT: AP Difficulty: Medium, TOT: 10 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.2
8. A dashboard can display more than one analysis and can quickly summarize results. Managers can use filters in a dashboard to customize the visualizations to their needs. This provides quick, actionable information for the manager. LO 2, BT: C, Difficulty: Easy, TOT: 8 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.2
9. The objective of a data visualization might be to show composition, relationships, distribution, trends, or comparisons. Depending on the objective of the analysis, different visualizations may be appropriate. For example, if the goal is to show composition, use a visual that shows how part of the data compares to the whole, such as a stacked bar chart. The appropriate visual for showing relationships will allow the audience to see how the data are related, such as with a scatterplot. Scatterplots can also be used if the objective is to show how data are distributed. Trends can be shown using line charts or column charts. To show comparison of items, bar or column charts should be used. To show comparison over time, a line or column chart would be appropriate. LO 2, BT: C, Difficulty: Easy, TOT: 8 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.2
10. The law of continuity states that people tend to perceive any line as continuing in its established direction and objects that are aligned with each other as a single path or shape. An effective visualization will arrange visual objects in a line to simplify groupings and comparison. For example, ordering a bar chart in ascending or descending order will help the viewer more clearly see comparisons. LO 3, BT: AP, Difficulty: Easy, TOT: 5 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.3
11. The law of similarity states that items that are like each other tend to be perceived as a unified group. Items similar in color, shape, size, or locations evoke the perception that they belong to the same group. It is important when preparing a visualization to keep this in mind. When showing comparisons, we need to be sure we group the items together that we want to compare. LO 3, BT: AP, Difficulty: Easy, TOT: 5 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.3
12. The law of proximity states that people will perceive visual elements related to how close they are to one another. This tendency helps people make sense of large data sets quickly. For example, if we use a scatterplot or a bubble chart, we can identify similar groupings more quickly than by only examining the numbers. LO 3, BT: AP, Difficulty: Easy, TOT: 5 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.3
13. The law of focal point states that a viewer will be more attentive to whatever stands out visually. To focus the audience on a specific aspect of a visualization, make that point stand out from the rest. For example, use a different color for the item. LO 3, BT: AP, Difficulty: Easy, TOT: 5 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.3
14. Preattentive attributes are visual properties we notice without realizing it. Size, color, and position are preattentive attributes in visualizations that can direct the audience’s attention. LO 3, BT: K, Difficulty: Easy, TOT: 8 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.3
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15. The y-axis can be manipulated by increasing or decreasing the range of values on the scale. For example, to downplay variability in a line chart, we can make the y-axis scale range wider. To show more variability, make the range smaller. LO 4, BT: AP, Difficulty: Medium, TOT: 12 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.4
16. Going against conventions refers to creating a visualization that goes against long-held conventions or associations. For example, in accounting the color red is associated with losses, so do not use red to show profit. LO 4, BT: AP, Difficulty: Medium, TOT: 12 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.4
17. Omitting the baseline in a visualization can mislead the audience by making changes look more dramatic than if a zero-base line was used. An example is the chapter comparison between Illustration 9.41 and 9.42. When the base line was set to $94 million, the increase in welfare received looked more dramatic than the graph with a baseline of $0. LO 4, BT: C, Difficulty: Easy, TOT: 8 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.4
18. Static visualizations focus on a specific data story and the viewer cannot go beyond a single view to explore the data. An interactive data visualization is designed to allow users to explore, manipulate, and interact with the data. They help connect the presenter with the audience by drilling down into the data based on the audience’s needs. This allows the presenter to quickly answer any questions from the audience. LO 5, BT: AN, Difficulty: Medium, TOT: 10 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.5
19. Interactive visualizations allow the viewer to explore and manipulate the visualization. In a live presentation, the presenter can connect with the audience by drilling down into the data based on the audience’s needs. The presenter can also quickly answer questions from the audience. LO 5, BT: AP, Difficulty: Medium, TOT: 12 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.5
20. There are seven best practices for presenting to a live audience: (1) Make sure visualizations are visible to the audience. (2) Focus on the points the data illustrates by explaining the meaning of the data. (3) Share one major point on each chart to avoid overwhelming the audience. (4) Label the chart components clearly. (5) Visually highlight the “a-ha” moment or the insight/discovery in the story. (6) Slide titles should reinforce the data’s point. (7) Look at the audience when presenting, and do not read from the slides. LO 5, BT: K, Difficulty: Easy, TOT: 14 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.5
SOLUTIONS TO BRIEF EXERCISES BE 9.1
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1. Knowing how much background information the audience will need in the communication of the risk analysis will ensure they understand the message. 2. Communication should focus on conclusions about risks for each of the investments analyzed. 3. Put the results in a context the audience can relate to. For example, compare the investments to each other in terms of risk, or compare the investments being considered to other investments already held. 4. The narrative of the communication should clearly communicate the results of the analysis and visualizations used in the presentation should be effective. 5. Communicate the results of the analyses as a story the audience can relate to. Start with why the analyses were performed and how each investment risk was analyzed. Share the main findings regarding which investment is least/most risky, include recommendations, and end with next steps for the audience. LO 1, BT: C, Difficulty: Easy, TOT: 11 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.1
BE 9.2 1. The audience is the tax director, who will likely not need much background information. Instead, focus on deeper insights. 2. Stay focused on insights from the analysis, and do not spend time discussing every analysis performed to achieve those insights. The presentation should focus on explaining the sales tax collected and how it relates to the amount of sales for the company. This will help keep the audience engaged. 3. Specify the context in which the analysis was conducted. Was the analysis for the entire company, or was it broken down by division? Can we compare this year’s analysis to last year’s analysis? In this presentation, the context would likely be by state because sales tax rates differ by state and locality. 4. The audience will understand the background already, but ensure that all the analyses/visualizations are effective and easy to understand. 5. People remember more facts from stories, so it is important to prepare the communication in the form of a data story. LO 1, BT: K, Difficulty: Easy, TOT: 9 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.1, Accounting Discipline: Tax
BE 9.3 Freytag’s Pyramid
Data Story Equivalent
P-card Transactions Analysis
Exposition
Introduce the problem or issue
Briefly discuss the background information relevant to the analysis. Keep it interesting to engage the reader’s attention. “Are there a material amount of p-card transaction control violations?”
Rising Action
The subject of the analysis is explored at a deeper level.
In this part of the story, show the overall analysis of pcard transactions and dig down to the examples of violations of controls.
Climax
The main finding or insight is shared. It is the “aha moment “of the story.
After building a case in the previous section, announce the employee(s) suspected of p-card violations. This may include collusion between employees or individual employee violations.
Falling Action
Share your solution.
Here, provide more details and recommendations as to specific transactions that should be investigated further.
Resolution
Conclude the story and provide next steps.
Make suggestions for additional internal controls to avoid future p-card violations.
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LO 1, BT: AP, Difficulty: Medium, TOT: 7 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.1, Accounting Discipline: Audit
BE 9.4 The audience must know how to evaluate potential investments, including the risk rating definitions. Provide the audience with details about the potential investments and an explanation of the types of analyses performed and the factors that contributed to the risk rating. If the audience is not familiar with the types of analyses that were performed, then explain them. LO 1, BT: AP, Difficulty: Easy TOT: 3 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.1, Accounting Discipline: Financial
BE 9.5 Purpose
Visualization
1. Show the distribution of prices for a specific product.
ANS: d. Scatterplot
2. Show the composition of total expenses.
ANS: c. Stacked bar chart
3. Show the relationship between temperatures and soup sales at a restaurant.
ANS: d. Scatterplot
4. Show sales trends over time.
ANS: b. Line chart
5. Show the distribution of sales by country.
ANS: e. Bubble chart
6. Show sales comparisons by year.
ANS: a. Bar chart
LO 2, BT: AP, Difficulty: Medium, TOT 4 min, AACSB: Analytic, AICPA BB: Strategic/Critical Thinking, Section 9.2
BE 9.6
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Animal Control Department Overtime - 2025 $80,000 $70,000 $60,000 $50,000 $40,000 $30,000 $20,000 $10,000 to be r No ve m be r De ce m be r
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$0
LO 3, BT: C, Difficulty: Medium, TOT: 9 min, AACSB: Analytic, PC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.3
BE 9.7
Gross Sales by City - 2024 -2025 $14,000 $12,000 $10,000 $8,000
2024
$6,000
2025
$4,000 $2,000 $0
Brookfield
Lafayette
Loveland
Denver
LO 3, BT: AP, Difficulty: Medium, TOT: 8 min, AACSB: Analytic, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.3
BE 9.8 1. Problems: • Not all the departments are visible. • There is no chart title. • The wrong type of visualization was used. • There are no axis labels to identify the measures. Corrections: • • •
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Use a column chart to show comparison of departments and total per year. Add a title: Employee Reimbursements by Department: 2022 – 2025 Change the y-axis so it is formatted as currency with no decimal places.
Dzuranin Approach, 1e •
Data Analytics and Accounting: An Integrated Chapter 8
Remove the grid lines.
2.
LO 2,3, BT: AN, Difficulty: Hard, TOT: 12 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies. Sections 9.2, 9.3
BE 9.9 1. Corrections/Improvements: •
Avoid using a black/dark background in case the viewer has astigmatism. The white numbers and text won’t look blurry. Title should indicate which year or years the data represents. Are January, November, and December missing? If not, they should be included, or the graph will be misleading.
• • 2. • • •
Viewers with astigmatism will have difficulty seeing the white numbers. Without indicating the year or years represented in the visualization, viewers won’t understand what the data is representing or be able to use it for decision-making. The viewer must guess as to why there are some months missing from the data. They will not find the visualization reliable.
LO 3, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Analytic, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.3
BE 9.10 1. The base line for the y-axis does not start at zero. This makes the changes over the years look more dramatic than they are. 2. The visualization is missing a title. The x-axis should just have the years under each column. Once that change is made, eliminate the colors and the legend for years. Eliminate the grid lines because they are not needed. Add data labels in millions above each bar. 3.
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LO 3, BT: AP, Difficulty: Medium, TOT: 12 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.3
BE 9.11 1. The y-axis has been manipulated to make the variation in overtime look less severe. Also, the title of the visual is too vague, and the months should be presented as abbreviations rather than numbers. 2. In the following visual, the title and months have been corrected and the y-axis has been adjusted so the line for overtime takes up ¾ of the visualization space.
Fire Department Monthly Overtime: 2025 $9,000,000 $8,000,000 $7,000,000 $6,000,000 $5,000,000 Total
$4,000,000 $3,000,000 $2,000,000 $1,000,000 $0
Jan Feb Mar Apr May Jun
Jul Aug Sept Oct Nov Dec
LO 3, BT: AP, Difficulty: Medium, TOT: 7 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.3
BE 9.12 Student answers will vary. Students can use Excel, Power BI or Tableau for this question. The visualization in this solution was created with Tableau. 1. Because we are comparing changes, pie charts are not the best option. It is difficult to see the extent of change in a pie chart. Also, for comparisons it is more appropriate to use bar, column, or line charts. 2. The following is a better choice for a visualization:
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This visual is a better choice because we are comparing years and categories. It makes it easier to see changes from 2024 to 2025, as well as differences between the product categories LO 4, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.4
BE 9.13 Student answers will vary. Students can use Excel, Power BI, or Tableau for this question. The visualization in this solution is in Tableau. 1. To make this visual interactive, add filters for the different sales channels to see profitability for each type of sales channel. Add a filter to also see the profitability of each sales channel by brand. 2. The following visualization has been adjusted to be interactive. The filters for sales channels and brand are shown below the visual.
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LO 5, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.4
BE 9.14 1. Because the audience are experts, it is not necessary to provide basic background information. Instead, provide an interactive presentation that will allow us to dig deeper into the analysis. An improved scatterplot will have a more descriptive title, the profit numbers should be currency, and the two locations with losses should be labeled. Labeling the locations that are operating at a loss will highlight the most important aspect of the visualization. It will also allow us to look closer at other data related to those locations. 2. The following scatterplot is an example:
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LO 2,5, BT: AP, Difficulty: Hard, TOT: 16 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.2, 9.5
BE 9. 15 The best way to make this visual interactive is to add filters for the details that we would like to drill down to. Add a filter for Category, Sub-Category and Region. Following is an example of an interactive dashboard in Tableau that can be used to customize the visualization.
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In Excel, the data can be made interactive by adding slicers to the PivotTable for Category, Region, and Sub Category LO 5, BT: AN, Difficulty: Medium, TOT: 8 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.5
SOLUTIONS TO EXERCISES EX 9.1
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1. The following visual was created in Excel as a dual axis chart. First a PivotTable was created, then a PivotChart. Model Total Sales Average of Profit Margin Captain $ 12,324,133 46.4% Celeritas $ 4,792,338 38.9% Kicks $ 1,108,183 44.1% Lazer $ 8,922,753 29.1% Grand Total $ 27,147,407 39.3% The profit margin was calculated by creating a calculated field:
Once the PivotTable is created, a PivotChart can be generated. The following chart is a Combo chart where the dual axis chosen is average of profit margin.
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2. These analyses would be more engaging to the audience if they were interactive. To make this visualization interactive, add filters. If using Excel, add slicers to the PivotChart that will allow the user to slice the data by year and by location:
LO 1,2,3,5, BT: AN, Difficulty: Medium, TOT: 30 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Sections 9.1-9.5, Accounting Discipline: Managerial Accounting
EX 9.2 1. Solutions will vary depend on the software used. The following examples use Excel PivotTables and PivotCharts to summarize the expenses. Meals:
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Meal Expenses by Category: FY 2025 $11,884
BAKERIES
$32,848
CATERERS
$218,893
FAST FOOD RESTAURANTS
$319,000
EATING PLACES RESTAURANTS $0
Entertainment:
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$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
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Entertainment Spending By Category -$235
DISNEY RESORTS $325
AQUARIUMS SEAQUA
$597
MASSAGE PARLORS $4,804
TOURIST ATTRACTION $16,095
MOTION PICTURE THE
$17,604
AMUSEMENT RECREAT $23,866
-$5,000
$0
AMUSEMENT PARKS C
$5,000 $10,000 $15,000 $20,000 $25,000 $30,000
2. To make these visualizations interactive, add slicers to the PivotTable and Chart. This allows us to see how much an individual employee has spent. McGuire had $22,972 of spending in the Amusement, Parks, Circuses, and Carnivals’ category.
LO 2,3,5, BT: AN, Difficulty: Medium, TOT: 15 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.2, 9.3, 9.5, Accounting Discipline: Tax
EX 9.3 Student answers will vary. 1. A suggested solution would be to prepare visualizations that show the following analyses: • Total variable costs by model • Average variable costs by model • Separate visuals for labor, materials, and overhead by model. The analyses can appear in a dashboard:
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2. • • • •
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Novice audiences need background information about the company and the models. The communication should also include details about the labor, materials, and overhead expenses. Expert Audiences do not need background details but will want the ability to drill down to the year, model, and location. Managerial audiences want actionable results, so this dashboard will allow them to see which models have higher expenses and if they are increasing. Executive audiences are interested in the most important insights. For example, are there any locations and models that have unusually low or high variable costs? The following visualization uses a box plot of variable costs per year for the Captain model. The annotation calls out an unusually low amount for 2025.
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LO 2,3, BT: AN, Difficulty: Medium, TOT: 16 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Sections 9.2, 9.3, Accounting Discipline: Managerial Accounting
EX 9.4 Student answers will vary. 1. Suggested sales trends and change in sales visualizations:
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Changes by location:
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Changes by model:
2. The line chart illustrates the pattern of monthly sales for each year. This visual allows the auditor to see if the current year pattern follows prior year patterns. The highlight table for the percent change by location provides the auditor with the increases or decreases in sales by location so that locations with large increases are decreases can be easily identified. The third highlight table does the same, but by model. 3. • Novice: This audience will need more background information about the company and what the visualizations illustrate. • Expert: This audience will want to dig deeper into the unusual trends or large increase/decreases in sales. • Managerial: This audience would like to see actionable information, such as the why there was decrease in sales in 2025 and what the recommendations are to address the decrease
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Executive: This audience wants the most important insights, so consider showing the locations and regions with decreases from 2025.
Excel Solution
1. Percent difference: • •
Right click on the amount for Boston, 2025. Chose Show Values As and then % Difference From
•
In the input box for Show Values As, select Year as the Base Field, and (Previous) as the Base Item:
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Now you will have the following PivotTable:
Use conditional formatting to create the highlight table:
LO 1,2,3, BT: AN, Difficulty: Medium, TOT: 18 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Sections 9.1, 9.2, 9.3, Accounting Discipline: Audit
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EX 9.5 1.
Total Overtime by Department: 2025 $16,229,522
DEPARTMENT OF STREETS AND SANITATION
$20,806,388
DEPARTMENT OF AVIATION
$26,551,512
DEPARTMENT OF WATER MANAGEMENT
$85,821,081
CHICAGO FIRE DEPARTMENT
$139,560,317
CHICAGO POLICE DEPARTMENT
2.
Police Department Monthly Overtime: 2025 $30,000,000 $25,000,000 $20,000,000 $15,000,000 $10,000,000 $5,000,000
3.
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Police Department Five Highest Overtime Totals: 2025 $9,000,000 $8,000,000
$7,830,667
$7,000,000 $6,000,000 $5,000,000 $4,000,000 $3,000,000 $1,411,685
$2,000,000 $1,000,000 $0
$203,241 POLICE OFFICER
SERGEANT
$192,610
$180,260
POLICE EILAND, DAVID LIEUTENANT TECHNICIAN
Note that it is typical to not specifically identify undercover officers. A title used in the name field is an indication of uncover employees. LO 3, BT: AN, Difficulty: Medium, TOT: 18 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.3, Accounting Discipline: Financial Accounting
EX 9.6 The profit analysis can be made interactive by using slicers and selecting Region, Sub-Category, and Years. Following is an example using the Central Region and Chairs:
This can be made interactive by using slicers and selecting Category, Sub-Category, Years, and State. Here is an example of sales comparison of 2024 and 2025 sales of camp stoves in Colorado.
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LO 5, BT: AN, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.5, Accounting Discipline: Financial Accounting.
EX 9.7
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1. Solutions to this exercise will vary, however students must consider the following best practices outlined in the text: • The target audience for the visualizations are the company’s executives. Therefore, the visualizations should include only the most important insights. • The visualizations’ purpose is to show trends in gross sales dollars for the models over a period of time. Therefore, the recommended visualizations are line graphs or bar charts. • Preattentive attributes of the visualization should be considered, and the visualization should be a single color with a gross sales dollar amount label. • The best-selling model is placed to the left, while the lower performing models are placed to the right of the graph.
2.
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Data Analytics and Accounting: An Integrated Chapter 8 Gross Sales by Model The executive team is interested in understanding the sales trend in gross sales dollars by scooter model for the last three years. To explore gross sales by model, I created a bar chart that depicts gross sale by model for each of the three historical years. As you can see, the gross sales for the Captain and Lazer models outperformed gross sales of Celeritas and Kicks models, which is depicted in the bar chart Gross Sales by Model. We also notice that Kicks and Celeritas have lower sales volume than Lazer and Captain models. However, the Lazer model has a higher sales volume than that of the Captain model in 2024 and 2025. Closer examination of the sales volume mix of each of the models reveals that sales mix has changed between 2023, 2024 and 2025. The Lazer model is growing in popularity, as the number of units sold has increased. Gross sales have increased from 2023 to 2025. In addition, reviewing the pie charts reveals that the sales product mix has changed between 2023 and 2025. Super Scooters is selling more Lazer and Captain models, and there is a reduction in the sale of Celeritas and Kicks models.
Climax
Falling Action
Resolution
Because the executive team indicated they wanted to allocate more marketing dollars to the high-performing models, I recommend that marketing dollars be allocated to the Lazer and Captain models. Gross sales and sales volume are important indicators of a product’s performance, however additional analysis may be considered regarding the geographic location of sales. Certain models may be more desirable in certain geographic areas. It is important to expand our analysis to consider the profitability of each model when considering other allocation methods for marketing dollars. Contribution margin is an indicator of profitability for each model.
LO 1,2,3, BT: AN, Difficulty: Medium, TOT:20 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 9.1, 9.2, 9.3, Accounting Discipline: Managerial Accounting
EX 9.8 Student answers will vary. Students should consider best practices when creating visualizations and the objective of identifying models and part numbers that have warranty issues. Students will need to understand the data, such as that the Spoke part has a significantly lower sales and cost price but a higher volume of sales. Therefore, the visualizations for this model should be separate from the visualizations of the Hub and Rim parts.
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LO 2,3 BT: AN, Difficulty: Medium, TOT: 20 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 9.2, 9.3, Accounting Discipline: Financial Accounting
EX 9.9
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1.Tableau Solution
1. Excel Solution
Gross Sales
Gross Sales Trends by Location : 2023 - 2025 $2,000,000 $1,800,000 $1,600,000 $1,400,000 $1,200,000 $1,000,000 $800,000 $600,000 $400,000 $200,000 $0
2023 2024 2025 Boston
Charlotte
Chicago
Dallas
Miami
Location
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Phoenix
Salt Lake City
Seattle
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2. Excel Solution
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$1,000,000 $900,000 $800,000 $700,000 $600,000 $500,000 $400,000 $300,000 $200,000 $100,000 $0
2023 2024 2025
B Ca osto pt n ai Ch n ica go M Sa lt iam La i ke C B i Ce ost ty le on rit a Ch s ica go Sa Mia lt m La i ke Ci Bo ty Ki ston ck Ch s ica go Sa Mia lt m La i ke C Bo ity s La to ze n Ch r ica go M Sa lt iam La i ke Ci ty
Gross Sales
Gross Sales Trends by Location : 2023 - 2025
Location
3. Freytag’s Pyramid Exposition
Data Story Equivalent
P-card transactions Analysis
Introduce the problem or issue
The objective of the analysis of Super Scooters’ gross sales by location and model over time is to examine any modifications in trends that may relate to a risk of material misstatement.
Rising Action
The subject of the analysis is explored at a deeper level.
Both line graphs show that overall sales have increased from 2023 to 2025. However, not all models have seen an increase in gross sales. The Captain model has increasing sales in every location, whereas the Kicks model has decreasing sales in all locations except Salt Lake City and Seattle. We also see a decrease in the Celeritas model from 2024 to 2025
Climax
The main finding or insight is shared. It is the “aha moment “of the story.
Falling Action
Share your solution.
Resolution
Conclude the story and provide next steps.
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Several locations have dramatic increases in sales. For example, Boston had a dramatic increase in sales of all models between 2024 and 2025. The Phoenix location’s annual sales are slightly less than prior year. Based on these two analyses, sales in the Boston and Phoenix locations should be considered as risks of material misstatement related to revenue. In addition, as we consider the occurrence assertion related to revenue by model, more analysis should be performed on the Kicks and Captain models. Because auditing authoritative guidance requires the audit team consider revenue as a risk of material misstatement, it is important to specifically identify the
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LO 1, 2, 3 BT: AN, Difficulty: Medium, TOT: 20 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 9.1. 9.2, 9.3Accounting Discipline: Audit
EX 9.10 Student solutions will vary but they should consider the target audience for the story and documentation of Freytag’s pyramid. Freytag’s Pyramid Exposition
Rising Action
Climax
Falling Action
Resolution
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Perspective of Investor Call
Perspective of Managerial Decisions
Welcome to the investor relations call to discuss geographic sales for 2024compared to 2025. This year’s performance exceeded expectations. We had increased sales in several regions, which we expected, and this contributed to overall sales increase for the company this year.
We examined geographic sales for the year compared to last year to understand our sales performance in certain key regions.
The largest increase in sales occurred in North America. The increases in sales could be attributed to our dedication to fulfilling customer orders in this region. We also saw increases in South America and the European Union, which we also expected and are part of our overall sales and growth strategy.
The region we need to pay attention to is Australia. That region saw a decrease in sales of $282,799 compared to prior year.
Our continued dedication to serving international markets will provide us opportunity to grow sales in the future.
We need to perform incremental analysis at the model level to determine a cause for these decreased sales. We might also consider examining the sales trend data in this region to understand if the timing of
Specifically, we examined sales in North America, European Union, South America, and Australia. Overall sales increased from 2024 to 2025. We saw higher sales in North America, South America, and the European Union.
North America sales increased the most at 85.07%, and South America sales increased 50.06%. The European Union’s sales increase was only 27.35%, whereas Australia decreased 62.48%.
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Data Analytics and Accounting: An Integrated Chapter 8 sales changed since prior year.
LO 1, 2 BT: AN, Difficulty: Medium, TOT: 35 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.1, 9.2, Accounting Discipline: Financial Reporting, Managerial Accounting
EX 9.11 Student answers will vary, but they should be guided by best practices for creating an effective visualization and avoiding misleading data visualizations. 1. Gross Sales by Year Comments: • • •
A stacked bar graph could be used to show variations in sales, but the x-axis should be the years and the stacked colors should be the models. Alternatively, a column chart may be more appropriate to show differences between the years and model sales. The data should be sorted from highest to lowest from left to right to communicate the models and years sales and differences between annual sales. Label the columns and remove the gridlines in the back of the visualization
Sales by Model, Region, and Year Comments: • •
The visualization used is a tree map, which should be used when precise comparisons are not important. In this instance, precision of the differences in sales and model are important. There are no labels in the tree boxes, so understanding the differences between model and year are challenging to determine.
2. Student visualizations created to assess the extent of testing by region and model will vary but should follow the best practices for creating visualizations and avoiding misleading visualizations. A proposed solution is as follows:
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LO 1, 2 BT: AN, Difficulty: Medium, TOT: 20 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Sections 9.1, 9.2, Accounting Discipline: Auditing
EX 9.12
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2.
3.
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LO 3 BT: AN, Difficulty: Medium, TOT: 38 min, AACSB: Data Analytics, AICPA FC: Leverage Technology to Develop and Enhance Functional Competencies, Section 9.3, Accounting Discipline: Financial Accounting
EX 9.13 Product costs:
Shipping costs:
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LO 5, BT: AP, Difficulty: Medium, TOT: 20 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.5, Accounting Discipline: Financial Accounting.
EX 9.14 1.
Because HEH is a distributor, they may have sales transactions on a Saturday. Therefore, it would be appropriate to filter out sales transactions and include only those transactions that may be outside the normal course of business.
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2.
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3.
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LO 2,3, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Sections 9.2, 9.3Accounting Discipline: Audit
EX 9.15 1. Sales Login by Employee. The chart indicates that Kayne, Ann, and Marcus logged into more than one POS location during the date of testing.
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2. Employees should logout when not using the POS. The chart indicates that Ann, Kayne, Marcus, and Sheila failed to logout of the POS after conducting a transaction
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3. Corporate and back-office employees should not login to the POS system. The chart shows that two employees from Corporate Accounting, Marcus and Yvonne, logged into the store POS on the date of testing.
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LO 2,3, BT: AP, Difficulty: Medium, TOT: 15 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Sections 9.2, 9.3, Accounting Discipline: Accounting Information Systems
EX 9.16 1. Invoice Quantity by Vendor Current Visualization The vendor names are cutoff at the bottom of the visualization, which makes it hard for the reader to know the full name of the vendor. The visualization is cluttered and includes gridlines that are not necessary. There are too many colors used in the visualization. The vendors are listed in alphabetical order. There is no indication what the reader should focus on. The axis label does not start at zero, which is confusing to the reader. The visualization does not highlight any findings of the analysis.
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Recommendation Use a bar chart so that the names fit.
Remove gridlines or make them more transparent. Use only one color. Sort vendors from highest to lowest. Start the axis at zero or use data labels at the end of the bars. Sort the data to highlight the vendors with the highest total quantity.
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The items purchased vary in quantity based on the type of item. The vendors who are providing a higher quantity of items are not the same vendors who provide a higher dollar amount of items. An analysis that includes the product purchased may be important to fully understand vendor purchasing.
Simplifying both visuals will make comparisons easier.
Invoice Cost by Vendor Current Visualization The vendor names are cutoff at the bottom of the visualization, which makes it hard for the reader to know the full name of the vendor. The visualization is cluttered and includes unnecessary gridlines. There are too many colors used in the visualization. The vendors are listed in alphabetical order. There is no indication of what the reader should focus on. The visualization does not highlight any findings of the analysis.
Recommendation Use a bar chart.
Remove or make lines more transparent. Use only one color. Sort from highest to lowest. Sorting will highlight the highest vendors.
2. The following solution was prepared in Excel. Note that by using a bar chart instead of a column chart we can fit the whole name of the vendor. We reduced the clutter by removing the bottom axis and adding data labels to each bar. The data is sorted from highest to lowest so that we can quickly see which vendors buy the highest number of items from us. In the visual, we can quickly see that Safety First and SGT Knots but the most items.
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The second visualization, Invoice Cost by Vendor, has been improved by reducing clutter, using only one color, adding data labels instead of an x-axis and sorting the data from highest to lowest. Here we can quickly see that Master Craft has purchased the most from us followed by Boston Whaler. Interestingly, these vendors buy fewer items, as we can see in the first visual, but the items they do buy are at higher prices.
Invoice Total by Vendor $178,925.84
MasterCraft $67,343.00
Boston Whaler $28,546.60
Bertram $3,892.05
Wakesurf, Inc. Vendor Name HighPerformanceSports
$3,727.44
SGT Knots
$2,993.40
Saftey First
$1,962.00
Wow Sports, Inc.
$1,161.95
Total Invoice Cost
LO 2,4, BT: AP, Difficulty: Medium, TOT: 15 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Sections 9.2, 9.4, Accounting Discipline: Financial Accounting
EX 9.17 1. Current Visualization
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The visualization has too many colors. They are not adding to the meaning of the graph because the locations are directly labeled
Use only one color for the sales that are appropriately approved and a separate color to identify sales that are not approved.
The reader must review the entire visualization to determine which location has the most sales that were not approved.
Consider filtering the chart to show only those sales above $10,000 that are not approved by location.
Because the columns are labeled directly, the gridlines are unnecessary.
Remove the gridlines.
The title of the visualization is Sales Approval Control, which does not describe the actual content of the visualization.
Consider a title such as “Sales above $10,000 with no approval” to adequately inform the audience.
The data set includes data from 2024 and 2025, however the control is only tested for 2025, which means that the graph should only include sales from 2025.
Consider adding a filter to limit the sales to the year for testing only, which is 2025.
2.
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LO 4, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.4, Accounting Discipline: Accounting Information Systems
EX 9.18 Current Visualization
Recommendations
The purpose of the visualization is to educate a novice audience about variable expenses. While a stacked bar chart can show relationships among variables, the reader of this visualization does not know what the stacked colors mean, and therefore may not understand the relationship between the colors
Consider labeling the colors on the visualization to highlight models or years.
The graph is not free from distractions.
Remove the gridlines and other non-data ink.
The data are not labeled and there is not a separate legend.
Label the data items.
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The data are not intentionally ordered.
Order the columns based on the trends in the variable expenses.
LO 4, BT: AP, Difficulty: Medium, TOT: 10 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.4, Accounting Discipline: Managerial Accounting
EX 9-19. Student answers will vary. A proposed solution includes:
LO 5, BT: S, Difficulty: Hard, TOT: 10 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.5, Accounting Discipline: Financial Accounting
EX 9.20. 1. The line graph should show all the years and not just 2022 and 2025. By selectively choosing the years, a decrease from 2024 to 2025 is not noticeable. Also, the y-axis should start with a zero-base line. Pie charts are not the appropriate visuals to show comparisons from year to year. There are also too many slices, so it is difficult to interpret them. 2.
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LO 2,3,4 BT: AP, Difficulty: Medium, TOT: 15 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Section 9.2, 9.3, 9.4, Accounting Discipline: Audit
SOLUTIONS TO PROBLEMS PR 9.1. The following PivotTable can be used in an interactive presentation that would allow the presenter to view the data at the Area, Location, and State Level:
LO 1,3,5 BT: S, Difficulty: Hard, TOT: 35 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Accounting Discipline: Financial Accounting
PR 9.2 The dashboard created allows management to see how the company is performing compared to targets.
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The filters at the top of the dashboard allow managers to customize their view. For example, if they want to see only Area 1, but choosing Area 1 in the Area filter the dashboard changes the visualizations so that they reflect only Area 1.
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LO 1,2,3,5 BT: AP, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Accounting Discipline: Managerial Accounting
PR 9.3 Plot of Revenue and Room Rentals: 1. Current year compared to prior year.
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2. Analysis for unusual observations:
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3. Students’ stories will vary. LO 1,2,3,5 BT: AP, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Accounting Discipline: Auditing
SOLUTION TO PROFESSIONAL APPLICATION CASE 9-151
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PAC 9.1 Accounting Information Systems: Student answers will vary.
LO 1,2,3,4,5 BT: AN, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Accounting Discipline: Accounting Information Systems
PAC 9.2 Auditing: See the file PAC 9.2 Auditing for the data story. What follows are individual visualizations that are included in the data story. The first visualization confirms the balances of salary expense and benefit expense on the trial balance.
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The second visualization confirms the headcount by job title.
The third visualization is an outlier analysis for Librarian salaries by job title. Outliers are annotated in the visualization.
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LO 1,2,3,4,5 BT: AN, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Accounting Discipline: Audit
PAC 9.3 Financial Accounting: Student answers will vary, but they should prepare analyses that show how revenue and expenses have changed in both composition of the total and over the years. Following are some suggested solutions. This visual shows a comparison of revenue, expenses, and net inflows from 2015 - 2025
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The next visual shows changes in the composition of revenue 2015 – 2025.
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A stacked bar chart can also be used.
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The same type of visuals used to show revenue composition can be used to show expense composition.
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Finally, a visual can be prepared to show the percent change in revenue and expense categories from prior year.
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LO 1,2,3,4,5 BT: AN, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Accounting Discipline: Financial Accounting
PAC 9.4. Managerial Accounting: Student answers will vary.
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LO 1,2,3,4,5 BT: AN, Difficulty: Hard, TOT: 45 min, AACSB: Data Analytics, FC: Leverage Technology to Develop and enhance Functional Competencies, Accounting Discipline: Managerial Accounting
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CHAPTER 10 RECENT DATA AND ANALYSES DEVELOPMENTS IN ACCOUNTING Learning Objectives: LO 10.1 Describe data trends impacting the accounting profession. LO 10.2 Explain how technology developments are impacting data analysis in accounting. LO 10.3 Demonstrate how data and technology developments are adding value to professional practice.
ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. A Students may want to discuss this further. They could argue that there is high data volume, but we do not know how many sales units or customers are involved (might be low) – they may select volume because the question has the word volume in it. Second, they may argue data value, but the hypothesis of the objective question, not the results, is asking if there is data value, meaning if there is a relationship between units sold and text comments.
LO 10.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
LO 10.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
LO 10.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
2. A Because the students read the question about the millions of transactions, they may want to see data volume in the alternative answers, and it is not there to make them think more deeply. They may want to choose velocity, but accounts payable typically has a delay between the purchase order, the receipt, the invoice, and the payment, so there may not be high velocity here. Auditors want to find evidence of the ending A/P balance value, which means that they want to verify data with high veracity. LO 10.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
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4. C LO 10.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
5. D LO 10.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
6. B LO 10.1, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
7. A LO 10.2, BT: K, Difficulty: Easy, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools.
8. A 9. C LO 10.2, BT: K, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
10. B LO 10.2, BT: K, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
11. C LO 10.2, BT: C, Difficulty: Medium, TOT: 2 min., AACSB: Knowledge, AICPA AC: Technology and Tools
12. A LO 10.2, BT: C, Difficulty: Medium, TOT: 3 min., AACSB: Analytic, AICPA AC: Technology and Tools
13. A LO 10.3, BT: AP, Difficulty: Medium, TOT: 2 min., AACSB: Analytic, AICPA AC Technology and Tools
14. D LO 10.3, BT: AP, Difficulty: Medium, TOT: 3 min., AACSB: Analytic, AICPA AC: Technology and Tools
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15. B
16. A
LO 10.3, BT: AP, Difficulty: Medium, TOT: 3 min., AACSB: Analytic, AICPA AC: Technology and Tools
LO 10.3, BT: AP, Difficulty: Medium, TOT: 3 min., AACSB: Analytic, AICPA AC: Technology and Tools
ANSWERS TO REVIEW QUESTIONS 1. Data variety refers to the diversity of data structures and measurement scales in the data we want to analyze. Examples of structured data include account codes, dates, currency values of transaction amounts or other data that can be entered easily into tables with rows and columns. Examples of unstructured data include GPS weather data, text, contracts, equipment sensor data, graphic files, social media posts and other data that does not fit easily into tables with rows and columns of fixed length. LO 10.1, BT: C, Difficulty: Medium, TOT: 6 min., AACSB: Comprehension, AICPA AC: Technology and Tools
2. Professionals are increasingly including unstructured data in their analyses due to the availability of that data, new technology developments that analyze unstructured data, and because of the value unstructured data can add to business intelligence. Example: accountants may incorporate vendor quality comments from social media data with purchasing data to refine approved vendors lists. LO 10.1, BT: AP, Difficulty: Medium, TOT: 7 min., AACSB: Analytic, AICPA AC: Technology and Tools
3. Data veracity refers to the reliability of data or the data’s integrity. Data veracity is important to data analysis because professionals make decisions and judgements based on the results of data analyses, which means that they are relying on the completeness and accuracy of the data. Performing an analysis with data that is incomplete, inaccurate, or otherwise lacks integrity can hinder the quality of the judgments made based on the analyses. LO 10.1, BT: AP, Difficulty: Medium, TOT: 7 min., AACSB: Analytic, AICPA AC: Technology and Tools
4. Data velocity refers to the speed in which new data points, from internal or external sources, are generated. Examples of internal high velocity data: • • • •
Smart sensing and controlling devices for production machinery performance. Business process logs for measuring throughput performance or identifying bottlenecks. Emails for contract related communications and confidential data violations. Texts for project progress, culture, and internal control bypassing.
Examples of external high velocity data: • • • •
Industry analyst tweets and blogs describing changes in company valuation. Social media postings regarding human resources, marketing, and investor relations. Location, weather and geographic map data for sales forecasting and transport safety. Textual media data regarding economic outlook, legislative progress, and regulatory activity.
LO 10.1, BT: K, Difficulty: Medium, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
5. Growing data volume creates the need for additional storage space and better data organization resources. There are significant incremental costs from necessary investments to store, process, manage, and transmit large amounts of data. There are also privacy and security 9-163
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concerns with increased costs related to keeping data secure and controlling for data risks. To address these challenges, organizations are often turning to cloud data services, which offer secure and scalable online data storage. LO 10.1, BT: K, Difficulty: Medium, TOT: 5 min., AACSB: Knowledge, AICPA AC: Technology and Tools
6. Accountants measure data value as the additional or incremental benefits or usefulness the data have for the objective of the analysis. It is important to use data with high data value in an analysis because collecting, purchasing, storing, processing, and analyzing data is expensive and accountants’ reputations are a function of the value their results provide to stakeholders. Identifying, choosing, and using high data value data options increases efficiency and effectiveness of data and analysis strategies. LO 10.1, BT: C, Difficulty: Medium, TOT: 5 min., AACSB: Comprehension, AICPA AC: Technology and Tools
7. The three types of data clouds include private clouds, public clouds, and hybrid clouds. Private clouds are restricted access data storage centers created for the data of one organization or a closed set of companies. Public clouds securely store data from multiple companies using virtual server data separators, and new companies can enter the public cloud easily. Hybrid clouds offer both private and public cloud data storage as dictated by the security and use characteristics of the data involved. LO 10.1, BT: K, Difficulty: Easy, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
8. The four components to the data analysis value creation chain are data analysis, new intelligence, strategy shifts, and value creation. The data analysis value creation chain begins by performing data analyses to gain new intelligence insights so that professionals can recommend strategies that are more likely to create new value for their clients and organizations. Student responses to the requirement to identify an example will vary. One proposed solution is: Managerial accountants may perform analyses to identify the optimal quantity of goods to produce or the optimal mix of products to produce. Their analysis leads to new intelligence regarding how to shift production capacity usage and marketing efforts, both of which add to the value created from incremental purchasing intelligence, inventory management, sales increases, and net income. LO 10.2, 10.3 BT: K, Difficulty: Hard, TOT: 8 min., AACSB: Analytic, AICPA AC: Technology and Tools
9. The goal of process mining is to evaluate the effectiveness, efficiency, and control of business processes. Accounting professionals engage in process mining to diagnose problems or improve internal processes. An example is measuring the throughput time and accuracy of each revenue cycle step of sales orders from capture until fulfillment. LO 10.2, BT: K, Difficulty: Medium, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
10. Continuous auditing has several benefits. First, it allows auditors to embed automated routines to test all or most of an organization’s transactions during the year. Second, by analyzing the population of transactions, the auditor’s year-end work is reduced, the auditors have more time to focus on the areas of highest risk, and the clients can often publish their audited financial statements earlier. Analyzing a population, rather than small random samples, also adds value because general ledger balance issues and errors can be more easily evaluated 9-164
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and corrected when they occur, increasing the integrity and reliability of internal data. Detecting patterns in the data can also trigger other insights, such as new tax considerations. LO 10.2, BT: K, Difficulty: Medium, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
11. The benefits of blockchain technologies include a single, shared transaction record between two or more parties on the shared ledger. In addition, the transaction is secure, the facts of the transaction cannot be altered without difficulty, and all parties are alerted of any changes. One example of blockchain use in accounting is smart contracts. Smart contracts for leases and loans are applications of blockchain technologies that provide many GAAP benefits, as well as automatic scheduled payment collections or payments. LO 10.2, BT: K, Difficulty: Easy, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
12. Tax professionals use cognitive technologies for many purposes, such as tax law compliance testing and for the evaluation of multi-jurisdictional sales and use taxes. These technologies can provide more accurate and defendable tax liability predictions and tax treatment classifications in complex multi-entity and multi-jurisdictional contexts, such as in international transfer pricing. LO 10.3, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA AC: Technology and Tools
13. Many AIS performance objectives can be analyzed using new data and analysis developments. Examples include: • • •
•
Cloud data storage needs are better predicted as data volume increases. Internal continuous auditing and process mining are helpful to evaluate system and internal control performance, as well as system security breaches. Cognitive technologies help predict and classify AIS bottlenecks and performance issues. Together with robotic process automation, systems accountants can spend more time interpreting performance information to add value to their organizations and clients. Examples of robotic process automation include automated running of system updates, automatic performance reporting for department managers, automatic production of daily reports including the date, time, and IP address of all unauthorized access attempts, or automated reports of the processing time for different processes such as order to shipment time in the revenue cycle, or invoice to payment time in the expenditure cycle.
LO 10.3, BT: K, Difficulty: Medium, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
14. Managerial accountants’ decisions are improved when they consider data veracity because it can motivate them to reduce the impact of dirty data on their results, improving the reliability of their decision-making. Dirty data, such as corrupt, incomplete, inaccurate, and inappropriate data formats, can create invalid analyses results. LO 10.3, BT: K, Difficulty: Medium, TOT: 6 min., AACSB: Knowledge, AICPA AC: Technology and Tools
15. Examples of how new data and analyses technologies are adding value to professionals in financial accounting include: • •
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Routine process automation is reducing the iterative processes in revenue, expenditure, payroll, production, and financial reporting processes. Process mining serves to evaluate the efficiencies, effectiveness, and controls in each process cycle.
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Cognitive technologies are providing efficiencies and more accuracy in both predicting financial outcomes as well as classifying analysis results, such as level of credit risk. Textual content is analyzed for stakeholder sentiments, regulatory implications, and earnings expectation communications. Visualizations organized into storytelling are more effectively educating and informing stakeholders.
LO 10.3, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA AC: Technology and Tools
SOLUTIONS TO BRIEF EXERCISES BE 10.1 There are several factors that an organization might consider when deciding between cloud data services: •
•
•
Privacy and confidentiality requirements of the organization, including the regulations associated with the type of data stored. For example, if the organization is storing PII (personally identifiable information) that is related to individuals’ health, then the HIPAA regulations must be considered as part of the privacy and confidentiality of the cloud data service. How much data the organization plans to store and how vulnerable the organization may be if the data is subject to unauthorized access. For example, should the organization store all its data in a public cloud, or should the more vulnerable data be stored in a secured private cloud? How often the organization will interact with the data. For example, will the data be stored in the cloud as back up or will the data be used for daily, weekly, or monthly transactions?
LO 10.1, BT: C, Difficulty: Medium, TOT: 8 min., AACSB: Comprehension, AICPA AC: Technology and Tools
BE 10.2 Data veracity refers to the reliability of the data used in the analysis. In this case, using only a portion of the year’s data to support the annual sales tax compliance for each sales tax jurisdiction is not complete enough to support the objective of the analysis. There is not enough information provided to determine if the eight months of data is accurate data, or if there were transactions in the remaining four months that were different, and not in tax compliance. It is likely that U.S. Outdoor Adventures management may form inaccurate conclusions from this analysis because the data set is incomplete. LO 10.1, BT: C, Difficulty: Medium, TOT: 8 min., AACSB: Comprehension, AICPA AC: Technology and Tools
BE 10.3 1. variety 2. veracity 3. velocity 4. volume
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LO 10.1, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 10.4 Statement
Term
1. When the client’s weekly payroll journal entry value was outside of the defined terms of normal authorized activity, the audit manager reviewed an automatically generated report 2. The financial accountant obtained an electronic file of the organization’s press releases and related investor comments. The accountant analyzes the pessimistic and optimistic words to understand sentiment around the company’s annual performance. 3. The operations accounting manager reviewed an event log that documented how much time it takes to complete operational steps in sales, shipping, and accounts receivable’s sending of the invoices to the customers. 4. The tax accountant analyzed their client’s online blog discussions to assess risk of tax evasion.
ANS: e
ANS: f
ANS: d
ANS: f
LO 10.2, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 10.5
Data Analysis
New Intelligence
1. b.
2. d.
Strategy Shifts
3. a
Value Creation 4. c
LO 10.2, BT: AP, Difficulty: Easy, TOT: 5 min., AACSB: Analytic, AICPA AC: Technology and Tools
BE 10.6 Statement
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Practice Area
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1. Steven engaged in data mining as part of the risk assessment process to determine if there were concerning patterns in his client’s sales cycle data. 2. Chi used process mining to examine systems delays and errors. Her analysis results help identify new policies to optimize the system functionality. 3. Wynn used textual analysis tools to better classify his company’s multi-jurisdictional transactions for compliance with new tax laws. 4. Henry employs smart contracts for all new leases made by his company. The smart contracts will improve GAAP lease classifications and revenue recognition. 5. Jamal automated his process of calculating depreciation expense for his company’s fixed assets by creating an Excel Macro. He runs this macro at the end of each month to create his adjusting entry to record depreciation expense. 6. John used textual analysis software to identify sentiment in his client’s press releases. His goal is to identify additional risks of material misstatements in his client’s financial statements.
ANS: b
ANS: a
ANS: e
ANS: c
ANS: c
ANS: b
LO 10.2, 10.3, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 10.7 Statement 1. The managerial accountant analyzes the comments made by customers when they return a product to identify where improvements can be made to return policy and procedures. 2. An audit staff member examines sales returns data for the objective of discovering patterns among product type or delivery timing. 3. To reduce AP processing time costs, the accounts payable (AP) manager wants the system to electronically match vendor invoices with their associated purchase orders and receiving reports instead the
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Technology ANS: f
ANS: a
ANS: c
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matching process currently performed by the AP clerk. 4. An internal auditor examines patterns in employee reimbursements related to travel to identify unusual expenses.
ANS: a
LO 10.2, 10.3, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
BE 10.8 If the restaurant informs its customers that it collects, securely stores, and analyzes this data only for its own business intelligence, strategy shifts and value creation, then there are no ethical implications. If the restaurant informs the customers that it may strip all personally identifying data fields, aggregate the data into categories of ordering behavior, and sell those aggregated insights to third parties, then there are no issues with selling the aggregated insights. However, if the customers are not informed, or if the data are not stored securely, or if personally identifiable data are shared with third parties, then there are ethical, if not legal, implications for the restaurant. LO 10.2, 10.3, BT: AP, Difficulty: Medium, TOT: 8 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
SOLUTIONS TO EXERCISES EX 10.1 Student solutions to this exercise will vary. However, a well-written memo that identifies data privacy, customizability, and interactivity factors will include the following: •
• •
A private cloud provides data storage for a single organization or for an agreed upon fixed set of organizations. Since the company captures personally identifiable data regulated by HIPAA, a private cloud is best for high privacy needs. A public cloud does not have the same level of privacy and adaptability as a private cloud and our organization’s data will be stored alongside of other company’s data. Since we are required to comply with HIPPA requirements, we would need the cloud-provider to provide documentation regarding how data are maintained separately from that of other companies. A private cloud is more customizable and adaptable to the needs of the company. Since the company is growing and offering increased procedures and services, the company many need to modify the data that is stored or reported in the future. A private cloud can support high data interactivity from doctors and patients who may need to access their data in real-time.
Regardless of the type of cloud-based service provider we engage, the company is still responsible for data veracity considerations with respect to the integrity of the stored data’s accuracy and completeness. LO 10.1. BT: AP, Difficulty: Medium, TOT: 35 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 10.2 This EX 10.2 solution is in Excel, and alternative technology solutions are provided in PowerBI and Tableau. EX 10.7 is this same exercise requesting the students to complete it using Alteryx, so this solution manual has this exercise solution in Alteryx under EX 10.7. 9-169
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Between these two exercises, instructors could potentially have students complete this comparative income statement analysis in all four technologies, or any subset thereof. 1. To generate the comparative income statement for these two products, the student should follow the steps outlined in the How To 10.1: Create a Comparative Income Statement in Excel, which are outlined next. Step 1: Open the EX 10.2 data set and familiarize yourself with the data field columns that have been extracted from the company’s AIS. The Excel spreadsheet contains retrieved data on the Monthly Sales & Costs Data tab and a data dictionary, which defines each data field, on the Data Dictionary tab. The data retrieved are summarized monthly totals for units sold, price, and cost information for the 2025 year for each of two products: SUP Model SX and SUP Model FW. Data Provided
Data Dictionary Field Label Year Month Product SumUnitsSold Price VCunit TraceableFCunit CommonFCunit TotalRevenues TotalDirectVC TotalAppliedTraceableFC TotalAppliedCommon_FC
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Field Description Transaction year Transaction month Product (SUP Model SX or SUP Model FW) Total sum of the units sold during the period Sales price per unit Variable cost per unit Fixed cost traceable to each unit Fixed cost that supports the production of both SUP Models Calculated data field (SumUnitsSold x Price) Calculated data field (SumUnitsSold x VCunit) Calculated data field (SumUnitsSold x TraceableFCunit) Calculated data field (SumUnitsSold x CommonFCunit)
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Step 2: The file has the information to create comparative contribution margin income statements for 2025 for the two products. The steps outlined in How To 10.1 are as follows:
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•
Go to the Data tab and select Sort. A pop-up window will appear. Verify the default My data has headers has been checked. Select Product in the first drop down list to ensure the data set is sorted properly.
•
Insert two rows between SUP Model FW and SUP Model SX data to help separate the data.
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•
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Enter a sum formula in D14 and D28 for total units sold for each product. D14 = total SUP Model FW and D28 = total SUP Model SX. In rows 14 and 28, enter similar sums for columns I, J, K and L.
Step 3: In the spreadsheet’s cell P3, widen the column and enter the title as “2025 Income Statement,” and then in O 5-13, enter the column and row formats and text as follows, remembering to put the single quote before the “=” so that Excel understands that this is a text entry and not a formula:
Sales Revenues less Variable Costs ‘= Contribution Margin less Avoidable Fixed Expenses ‘= Segment Margin less Unavoidable Common Fixed Expenses 9-172
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‘= Net Income Step 4: Using the following formulas, create the income statement to compare the revenues and costs for SUP Model FW and SUP Model SX N
O
7 8 9 10 11 12 13
Sales Revenues less Variable Costs =Contribution Margin less: Traceable Fixed Costs =Segment Margin less: Common Fixed Costs =Net Income
P 2025 Income Statement SUP model FW =+I14 =+J14 =P7-P8 =+K14 =P9-P10 =+L14 =P11-P12
Q
SUP model SX =+I28 =+J28 =Q7-Q8 =+K28 =Q9-Q10 =+L28 =Q11-Q12
R
S
Both Product Lines =P7+Q7 =P8+Q8 =P9+Q9 =P10+Q10 =P11+Q11 =P12+Q12 =P13+Q13
T
SUP Model SX only =+Q7 =+Q8 =+Q9 =+Q10 =+Q11 =+R12 =T11-T12
The final comparative income statement should be formatted and look as follows: 2025 Income Statement
7 Sales Revenues 8 less Variable Costs 9 =Contribution Margin 10 less: Traceable Fixed Costs 11 =Segment Margin 12 less: Common Fixed Costs 13 =Net Income
SUP model FW SUP model SX $ 2,777,360 $ 3,595,295 $ 2,221,888 $ 2,161,500 $ 555,472 $ 1,433,795 $ 521,920 $ 1,080,750 $ 33,552 $ 353,045 $ 123,024 $ 237,765 $ (89,472) $ 115,280
Both Product Lines $ 6,372,655 $ 4,383,388 $ 1,989,267 $ 1,602,670 $ 386,597 $ 360,789 $ 25,808
SUP Model SX only $ 3,595,295 $ 2,161,500 $ 1,433,795 $ 1,080,750 $ 353,045 $ 360,789 $ (7,744)
Save the file per your instructor’s file naming guidance.
2. No, it would not help the company’s profitability as measured by the overall net income. Eliminating the SUP Model FW, which by itself has a net loss of $89K, would further decrease the company’s overall profitability because of the unavoidable common fixed expenses that the company incurs regardless of whether they produce the SUP Model FW or not. These unavoidable common fixed expenses would then be applied to the SUP Model SX and make the company operate at a net loss (- $ 7,744) when only producing the one SX product. 3. Data value depends not only on whether the data is a good measure of the underlying phenomena but also whether it will inform the objective of the analysis. Students’ answers regarding alternatives may vary and could include: • •
Considering whether market share for the net-loss producing product is significant and if it contributes to overall company success. Considering whether this product is an entry brand product that encourages consumers to develop brand loyalty.
LO 10.1, BT: AP, Difficulty: Medium, TOT: 30 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
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EX 10.3 The answers to this question will vary based on the student’s knowledge base from their auditing courses. Suggested correct responses are highlighted in red. March 15, 20XX Memo for the Audit Files RE: Continuous Audit of Revenue Data & Professional Due Care Considerations This memo documents the engagement team’s procedures to provide reasonable assurance regarding the client’s revenue-cycle transactions and to document the engagement team’s procedures to ensure that the client’s data is kept confidential. First, we discuss the audit procedures and second, we discuss professional due care considerations. Continuous Audit of Revenue Procedures The client has provided us access to all sales transaction data in real-time by allowing the audit firm to connect its proprietary analytics software to the company’s relational database. As revenue transactions are recorded in the client’s database, the auditor’s software automatically tests and captures the audit data. We performed audit procedures on this data throughout the year, including the following descriptive statistics and diagnostic tests performed on the revenue: 1. Possible ANS: calculation of mean, median, and total sales revenues; invoice counts; maximum and minimum invoice totals; and the standard deviation, skewness, and kurtosis for each month of data. We also created visualizations, such as line graphs for sales over the audit year and bar graphs and pie charts of sales by product category and sales by customer. These visualizations will inform us regarding outlier transactions in which we should perform incremental procedures. Based on these descriptive and diagnostic tests, and comparing our results to past years, abnormal transactions or anomaly transactions were identified and further investigated by collecting and reviewing their related source documents, such as contracts, invoices, and customer payments. Professional Due Care Considerations Since sales and collection transaction have a high data volume of personally identifiable data, great care must be taken to ensure that the client’s data is kept private. For example: 2. Possible ANS: we will maintain appropriate physical access controls over the hardware and strong authorized access software to safely extract the raw data from the client’s database. We will also restrict access of the client’s files to those members of the engagement team who actively work on the audit of the sales cycle. Any sensitive data stored on our computers will be stored with encryption. Finally, none of the personally identifiable data will be shared with any other party within or external to our audit firm. LO 10.1, BT: AP, Difficulty: Hard, TOT: 30 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
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EX 10.4 1. The quantity of products sold have high data volume, especially if the company sells these products online. For this data to have high data value, it must also have high data veracity, so the data’s completeness, accuracy, and correct recording can be relied upon. 2. The number of memberships sold each month can have high data volume, especially if the gyms allow walk-in visits and monthly as well as annual memberships. This data has high data value for understanding the member-base for the gyms and to determine if the gym membership can support its cost structure. 3. The percentage of members who book appointments with nutrition or personal training professionals has high data value, as these tend to be higher contribution margin services. Data veracity may depend on if the number of booked appointments matches the number of appointments performed. 4. Member surveys, feedback, and online website comments and social media posting data have high velocity and variety, ranging from ordinal data for star ratings to categorical text data comments groupings to ratio counts of positive comments. LO 10.1, BT: AP, Difficulty: Medium, TOT: 30 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 10.5 Robotic Process Automation (RPA) can be programmed to automatically check the match of the vendor, the purchase order number, and quantity of products across all three documents each night. A test of the quantity of products ordered and received across these documents can also be set up to match within a tolerable difference. If these tests are passed, then the invoice can be approved for recording the accounts payable liability, authorizing its subsequent payment at the due date. If the quantity, vendor name, or price differ between these items, then the RPA can be programmed to initiate an exception report each day, which the AP clerk can use to investigate the unmatched items first thing each morning, freeing up the clerk to perform other tasks the rest of each day. LO 10.2, BT: AP, Difficulty: Medium, TOT: 30 min., AACSB: Analytic, AICPA AC: Technology and Tools
EX 10.6 1. An incomplete screenshot of the updated Excel file:
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A B 1 Comment date Customer food rating key words 2 3 2/ 9/ 25 adequate / / 4 2 19 25 adequate 5 3/ 1/ 25 adequate 6 3/ 18/ 25 adequate 7 1/ 12/ 25 average 8 1/ 24/ 25 average 8 2/ 5/ 25 average 9 2/ 15/ 25 average 10 2/ 25/ 25 average / / 11 3 7 25 average 12 3/ 14/ 25 average 13 3/ 24/ 25 average 14 3/ 31/ 25 average 15 1/ 3/ 25 awful 16 1/ 15/ 25 awful 17 1/ 27/ 25 awful
C Customer service rating
D Food Rating Group 2. Average 2. Average 2. Average 2. Average 2. Average 2. Average 2. Average 2. Average 2. Average 2. Average 2. Average 2. Average 2. Average 3. Poor 3. Poor 3. Poor
not remarkable not remarkable not remarkable helpful nice adequate helpful friendly friendly average OK OK OK good OK friendly
2. Food Rating Frequency Distribution Table 1. Good 2. Average 3. Poor Total Number of Ratings
Number of Responses 25 32 32 89
Grading note: Advanced students may want to use the frequency table function in Excel. Also, the count of each of the categories may vary based on how a student classifies each term in the customer food rating column. This is true for question parts 3, 4, and 5. In this solution, “Good” food ratings were coded as those with the following words: delicious, exceptional, good, great, and yummy. “Average” ratings were coded as those with the words: Ok, average, adequate, and not remarkable. The remainder were coded as “Poor” ratings. 3. A B 2 Count of Food Rating Group Column Labels 3 Row Labels <1/2/2025 4 1. Good 5 2. Average 6 3. Poor 7 (blank) 8 Grand Total
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C
D
E
F
Jan
Feb Mar Grand Total 9 16 25 6 11 15 32 24 8 32 30
28
31
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4. 30 25 20 Average
15
Good 10 Poor 5 0
Jan
Feb
Mar
5. Based on the line graph in question part 4, students can conclude that the customer food ratings have changed over time such that poor ratings decreased between January and February and ratings classified as “Good” have increased between February and March. A note of caution: This trend analysis does not prove causality. Students should not conclude that the new kitchen management policies are causing the change in the customer food ratings over time. LO 10.1, BT: AP, Difficulty: hard, TOT: 30 min., AACSB: Comprehension, AICPA AC: Technology and Tools
EX 10.7 The WileyPlus solution provided is in Alteryx. Alternative technology solutions are provided in Excel, PowerBI and Tableau under Exercise 10.2, as EX 10.7 is this same exercise. Between these two exercises, instructors could potentially have students complete this comparative income statement analysis in all four technologies, or any subset thereof. 1. Creation of Income Statement: Step 1: Open Alteryx and select Favorites tab at the top of the screen. This set of tool icons contains most of the tools necessary for this exercise. Step 2: Add the Input Data icon to the workflow by dragging the icon into the workflow window. Import the Excel data from the worksheet “Monthly Sales and Costs Data” from the stored EX 10-7 data set file location. NOTE: the excel file should not be open in another window. Run the input. Step 3: Add the Sort tool icon to the workflow by dragging the icon into the workflow window to the right of the prior Input Data icon. Sort on [Product] column in ascending order. Run the sort. 9-177
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Step 4: Add the Formula icon to the workflow. Add the following new columns by choosing the menu option Select Column and typing the following formulas for each new column. Select data type = Double for all new calculated data fields. Add annotation of choice. Run the formulas. New Column
Formula for the new column
RevenuesFW if [Product]="SUP Model FW" then [TotalRevenues] else 0 endif RevenuesSX if [Product]="SUP Model SX" then [TotalRevenues] else 0 endif VCforFW if [Product]="SUP Model FW" then [TotalDirectVC] else 0 endif VCforSX if [Product]="SUP Model SX" then [TotalDirectVC] else 0 endif TraceFCforFW if [Product]="SUP Model FW" then [TotalAppliedTraceableFC] else 0 endif TraceFCforSX if [Product]="SUP Model SX" then [TotalAppliedTraceableFC] else 0 endif CommonFCforFW if [Product]="SUP Model FW" then [TotalAppliedCommonFC] else 0 endif CommonFCforSX if [Product]="SUP Model SX" then [TotalAppliedCommonFC] else 0 endif
Step 5: Add the Summarize icon to the workflow. Select all the numeric fields listed next. Select action (Sum) and the field will populate automatically in the field below the action menu. Add annotation of choice. Run the SUM. Field RevenuesFW RevenuesSX TotalRevenues VCforFW VCforSX TraceFCforFW
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Action Sum Sum Sum Sum Sum Sum
Output Field Name Sum_RevenuesFW Sum_RevenuesSX Sum_TotalRevenues Sum_VCforFW Sum_VCforSX Sum_TraceFCforFW
Dzuranin Approach, 1e TraceFCforSX CommonFCforFW CommonFCforSX
Data Analytics and Accounting: An Integrated Chapter 8 Sum Sum Sum
Sum_TraceFCforSX Sum_CommonFCforFW Sum_CommonFCforSX
Step 6: Add the Formula icon to the workflow. Create the new column shown next with the corresponding formula. Add data types = Double. Add annotation of choice. Run the formulas. New Column TotalRevenues CostofGoodsSoldFW CostofGoodsSoldSX TotalCostofGoodsSold GrossProfitFW GrossProfitSX TotalGrossProfit TotalCommonFC NetIncomeforFW NetIncomeforSX TotalNetIncome
Formula for the new column [Sum_RevenuesFW]+[Sum_RevenuesSX] [Sum_VCforFW]+[Sum_TraceFCforFW] [Sum_VCforSX]+[Sum_TraceFCforSX] [CostofGoodsSoldFW]+[CostofGoodsSoldSX] [Sum_RevenuesFW]-[CostofGoodsSoldFW] [Sum_RevenuesSX]-[CostofGoodsSoldSX] [GrossProfitFW]+[GrossProfitSX] [SUM_CommonFCforFW]+[SUM_CommonFCforSX] [GrossProfitFW]-[SUM_CommonFCforFW] [GrossProfitSX]-[SUM_CommonFCforSX] [NetIncomeforFW]+[NetIncomeforSX]
Step 7: Add the Arrange icon to the workflow (look under the Transform menu). In the Key Fields window, make sure all the key fields have been de-selected, as we do not want those fields as columns in our table. Add output fields by clicking the down arrow to the right of the Column in the Output Fields header line. After entering your desired output fields (Descriptions are only needed for the first column, SUP Model FW), choose your annotation. Run the Arrange. 9-179
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In the description column, which appears to the left of the SUP Model FW column, type each of the following descriptions: • • • • •
Sales Revenues less Cost of Goods Sold Gross Profit less Selling and Administration Expenses Net Income
Then add two more columns. one for SUP Model SX and Both Products as depicted next. Remember to check if the correct field is listed for each description in each column, as these may need to be rearranged.
Click Run. The final output should appear as follows:
Step 8: Export the results to an Excel file, name the sheet “2025 Income Statements,” and name the file per course instructions.
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Sales Revenues less Variable Costs =Contribution Margin less: Traceable Fixed Costs =Segment Margin less: Common Fixed Costs =Net Income
SUP model FW SUP model SX $ 2,777,360 $ 3,595,295 $ 2,221,888 $ 2,161,500 $ 555,472 $ 1,433,795 $ 521,920 $ 1,080,750 $ 33,552 $ 353,045 $ 123,024 $ 237,765 $ (89,472) $ 115,280
Both Product Lines $ 6,372,655 $ 4,383,388 $ 1,989,267 $ 1,602,670 $ 386,597 $ 360,789 $ 25,808
SUP Model SX only $ 3,595,295 $ 2,161,500 $ 1,433,795 $ 1,080,750 $ 353,045 $ 360,789 $ (7,744)
2. These results indicate that SUP model FW should not be dropped as it is contributing a positive segment margin which helps cover the common fixed costs which would continue even when SUP model FW is dropped. If some of the model FW products were in 2025 ending inventory, then some of the fixed overhead would be trapped in Inventory on the Balance Sheet, which would make the income statement look more profitable in 2025. LO 10.2, BT: AP, Difficulty: Hard, TOT: 40 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 10.8 Students’ solutions must calculate ratios for each of the six divisions in the One Stop Shop company. This is a summary of their results (which will be on different sheets for every division): Students can start on the Floor Cleaning worksheet, moving their cursor to the first non-shaded cell in the ratios table, cell C20. Under the View option on the top menu, select the pull-down menu for the last icon on the right, Macros. Select “Record a macro”, and name it “2025 and 2026 Financial Ratios. Starting in cell C20, enter the formula for each of the ratios, remembering that the macro is recording each keystroke: The formulas for 2025 C20: =C9/((B15+C15)/2) C21: =C9/C6 C22: =C6/((B15+C15)/2) C23: =C14/C16 Then repeat the formulas for 2026, using column D data (and column C data when averaging is needed in the denominator). Then select the C20:D21 cells, and format them as percentages. Select the C22:D23 cells and format as a a number with two decimal points. Go to View menu, Select Macros, and Stop recording. Next, go to each subsequent sheet, and select cell C20. Go to View menu, select Macros, and run the 2025 and 2026 Financial Ratios macro. Here are the results:
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An easy extension of this exercise is to have the students copy all results to one worksheet and make a visualization of all divisions’ performance for each of the ratios. LO 10.2, BT: AP, Difficulty: Hard, TOT: 30 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 10.9 The stakeholders impacted by using blockchain technologies are the company and its supply chain partners. One benefit of using blockchain to record these transactions is that blockchain creates an unchangeable record of each transaction, reducing future possible disputes about the timing and the value of transactions. Another benefit of using a blockchain is the possibility to implement smart contracts for long term purchase agreements, which can increase efficiencies in the negotiation of contracts, the examination of contracts for GAAP revenue recognition compliance, and increase the timeliness of payments. These benefits need to be compared to the cost associated with implementing a blockchain technology and the possibility of sharing these costs with the supply chain partner vendors should also be considered. LO 10.2, BT: AP, Difficulty: Hard, TOT: 30 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 10.10 Process mining technologies are the best tools to analyze the effectiveness, efficiency, and authorization of P-card transaction processes. The first step would be to transform the P-card event log data into data formats and measures that can be compared and analyzed. The second step would be to build models and explore the results further to determine which P-card policies are not being followed by which P-card users. LO 10.2 10.3, BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 10.11 An effective tool for this stock market closing price data retrieval task is robotic process automation (RPA). Since the task is performed daily, creating an RPA routine for this task would eliminate the need to perform this task manually every day, freeing up time to perform greater valuecreating analysis, interpretation, and communications for stakeholders. LO 10.2 10.3, BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
EX 10.12 1. Step 1: Using the EX 10-12 data set, select the entire data set and sort the customer key words by ascending alphabetical order to group like comments together, or sort the customer product ratings as they should be highly correlated with the customer text comments.
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Product Rat ing Jan 3 Jan 3 Feb 3 Feb 3 Feb 4 Mar 3 Mar 3 Mar 3 Mar 3 Mar 3
Comment s - Key words average average average average average average average average average average
Classificat ion 2 2 2 2 2 2 2 2 2 2
Step 2: In the classification column, assign a group to the key word comments. Students can choose from 3= High Quality, 2=Neutral, and 1=Low Quality. Note that the visualization and the conclusion reached in the case may depend on how a student classifies each of the terms. There are no ambiguous text comments, so there should be wide agreement across students. Then transform the date column into months from the Home menu. In the Number section of the top menu, select Custom formatting, then type “mmm” in the type (no quotes).
2. The next step is to select Insert from the top menu, and select Pivot Chart, Pivot Table and Pivot Chart. With the cursor anywhere in the pivot table, select the following Pivot table fields: Select Classification for the Columns, Select Date for the rows, and keep the Months, delete any other Date variable listed in the Rows box. Select Classification again for the Values box and change the value settings to Count rather than Sum. The pivot table results:
Count of Classification Row Labels Jan Feb Mar Apr May 9-183
Column Labels 1 18 12 22 15 4
Grand 3 Total
2 12 7 8 18 10 9 5 9 11
30 27 50 29 24
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Jun Grand Total
2 73
10 15 65 49
27 187
3. Graph results of the trends over time. Select the Pivot Chart, and right click, selecting Change chart type. Change to line graph. The results:
Customer Satisfaction Ratings 25 20 1
15
2 10
3
5 0
Jan
Feb
Mar
Apr
May
Jun
45 40 35 30
1
25
2 3
20
4
15
5
10 5 0
Jan
Feb
Mar
Apr
May
Jun
To create the second visualization based on product ratings, where 1 is the lowest and 5 is the highest rating, follow the same process as above in the Pivot Table fields, replacing Product Rating for Classification in the columns and values boxes. The result:
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16 14 12
1
10
2
8
3
6
4
4
5
2 0
Jan
Feb
Mar
Apr
May
Jun
4. The customer comment visualization: since 1 = Low quality, and 3 = high quality, provides evidence that warranty expense accrual could be decreased. The product ratings visualization: since 1=Low quality and 5 = high quality, this graph shows that product ratings are increasing from Jan to June, providing more evidence that the warranty expense accrual could be decreased. Further audit work would be recommended, which could include: • • •
Analysis of returned product between year-end and the date of the audit Analysis of warranty claims filed between year-end and the date of the audit. Further discussion with the client regarding product quality, such as the correlation between product quality, customer satisfaction, and the warranty expense estimations.
An internal control used often for coding integrity is to have two people code the data independently, and then resolve any coding disagreements. LO 10.2 10.3, BT: AP, Difficulty: Medium, TOT: 20 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
SOLUTIONS TO PROFESSIONAL APPLICATION CASE PAC 10.1. Accounting Information Systems: The final workflow prepared by the student should be like the following:
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The Alteryx output will result in two Excel files: 1) Exceptions.xlsx: The spreadsheet should contain four invoices that cannot be paid because their quantity is different than the receiving report quantity. Those invoices are presented in the following excerpt:
2) OkaytoPay.xlsx: The spreadsheet contains 23 Invoices which meet the criteria to pay. An excerpt of that solution file is next:
_Right_Left _Right_PODate InvoiceNOPONo VendorID VendorName ItemID ItemDescription InvoiceQtyInvoiceItemCost InvoiceTotal Left_Right_Left PONo VendorID VendorNamePOItemID POItemDescription POItemCost POItemQty 1326 6005 1783 Bertram TM127402 Trolling Motor 12 238.55 2862.6 6005 1783 Bertram 3/ 1/ 2024 TM127402 Trolling Motor238.55 12 6287 6006 1258 MasterCraft TM120489 Trolling Motor 12 223.52 2682.24 6006 1258 MasterCraft 3/ 2/ 2025 TM120489 Trolling Motor223.52 12 4614 6007 1153 Saftey FirstLV2942 Life Vest - Women's 15 M-L 15.85 237.75 6007 1153 Saftey First 3/ 3/ 2025 LV2942 Life Vest - Women's 15.85 M-L 15 4225 6009 1552 Wow Sports, 11443629 Inc. Wow Sports Macho 10Two Person 45.89 Towable 458.9Tube '20 6009 1552 Wow Sports, Inc 3/.5/ 2025 11443629 Wow Sports Macho 45.89Two Person 10 Towable Tube '20 .89 Combo 5910 6010 1685 HighPerformanceSports H3740 HO Sports Men's Blast 10 Combo 78.89Water788 Ski.9with Horse 6010Shoe Bindings 1685 HighPerformanceSports '20 3/ 6/ 2025 H3740 HO Sports Men's 78Blast 10Water Ski with Horse Shoe B 5391 6011 1784 Boston Whaler B20489 Fish and Ski Boat 1 65845 65845 6011 1784 Boston Whaler3/ 7/ 2025 B20489 Fish and Ski Boat 65845 1 1297 6012 1258 MasterCraft B20490 High Performance Boat 2 87563 175126 6012 1258 MasterCraft 3/ 8/ 2025 B20490 High Performance 87563 Boat 2 .89 2323with .35 Remix .89 2.0 Wakeboard 2552 6014 1788 Wakesurf, 11777612 Inc. Hyperlite Men's State 15 2.0154 Wakeboard 6014 10-14 Bindings 1788 Wakesurf, '21 Inc3./ 10/ 2025 11777612 Hyperlite Men's 154State 15 with Remix 10-14 Bi .87 / 11 . / 2025 11443630 Wow Sports Super 3334 6015 1552 Wow Sports, 11443630 Inc. Wow Sports Super15 Thirller46One-to-Three 703.05Person 6015 Towable 1552 TubeWow '20 Sports, 3Inc 46.87 Thirller One-to-Three 15 Person Towable 4949 6016 1685 HighPerformanceSports H2741 HO Sports Women's 10 Carbon 75.OMNI 58 Slalom 755.8 Water6016 Ski '21 1685 HighPerformanceSports 3/ 12/ 2025 H2741 HO Sports Women's 75.58 Carbon OMNI 10 Slalom Water Ski '21
The POInvQtyDiff.xlsx contains no data as all invoice quantities matched the quantities on the related PO. HOW TO GENERATE THE ALTERYX WORKFLOW Step 1: Remembering that your Excel file cannot be open at the same time, open Alteryx and drag the Input Data icon on the workflow. Connect to the Ch10 PAC data set – the Invoice worksheet. Drag another Input Data icon to the workflow. You will connect the Ch10 PAC Data set – the PurchaseOrders worksheet. Annotate as you please and Run the Input.
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Step 2: Drag the Join icon to the workflow. Drag a line from the Invoice Data Input Icon to the L (top left of the Join Icon) and drag a line from the Purchase Orders Data Input Icon to the R (bottom left of the Join Icon). Join on Purchase Order Numbers. Annotate as you please and run the join. Step 3: Drag the Formula icon to the workflow. Connect the Formula icon to the J on the right side of the Join icon as pictured. Create two formulas, making both fields Double type: • •
InvPOQtyDiff using the formula [InvoiceQty]-[POItemQty] VendorIDDiff using the formula [VendorID]-[Right_VendorID]
Annotate as you please and run the formulas.
Step 4: Add a new join and connect the output of the Formula icon to the Left Join on the left side of the Join icon and connect to the Right Join from the Input Data icon with the Receiving Reports. Join on PONo as shown here: 9-187
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Step 5: Drag the Formula Icon to the workflow and connect to the J on the right side of the Join icon from the previous step. Create two formulas as follows, using Double as the new column type, annotate and run: • •
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InvRecQtyDiff using the formula: [InvoiceQty]-[RecItemQty] VenDiff using the formula: [VendorID]-[Right_VendorID]
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Step 6: Add the Filter icon and create the following custom filter as [InvRecQtyDiff] > (.02*[POItemQty]). Annotate and run the workflow.
Step 7: From the T on the top right of the Filter Icon, connect the Output Data icon and define the sheet as Exceptions. Annotate as you please and run the output. Step 8: Add the Filter icon to the workflow and connect to the F on the bottom right of the Filter icon. Create another custom filter as [InvPOQtyDiff] > (.02*[POItemQty]), annotate as you please, and run the filter. Step 8: Connect two more Output Data Icons to the T (for the Exceptions report) and to the F (for the OK to Pay report) on the right of the filter icon. Annotate as you please and run the outputs.
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Remember that to access the Excel files, go to the location you predefined in the last three Data Outflow process steps. The results from this data set will have zero rows in the exceptions between the Invoice and the PO quantities. There will be four rows in the exceptions between the Invoice and the Receiving Report quantities, and 23 rows representing Invoices that result Okay to Pay. Note: You can easily change the data each semester or between students within a semester to have differing size and number of exceptions between the invoices and the purchase orders, and the invoices and the receiving reports. PAC 10.2 Auditing: Each student memo will vary, but should contain the following key points: •
•
Continuous auditing can be performed by embedding modules in the client’s accounting information system that evaluate the order, receiving, and invoice transactions as they occur. For example, routine transactions can be tested with RPA monthly, or even as they occur. Any transactions that do not pass the test for normal activity can be automatically copied to an exception report, focusing the auditor’s attention on the transactions with the greatest risk. A continuous auditing module could also download all purchase, receipt, and invoice data to create distribution and deviation visualizations that will assist the auditors when looking for anomalies in the vendor, purchase, receiving, and invoice information and to make sure that only inventory that has been received can be recorded as an asset and a liability, as well as authorized for payment.
Example student memo: To: The Audit Files From: (student name) Date: June 11, 2022 Re: Use of Continuous Auditing The purpose of this memo is to document the engagement team’s procedures to provide reasonable assurance regarding the client’s order-to-pay cycle transactions and to document the engagement team’s procedures to ensure that the client’s data is kept confidential. First, we discuss the audit procedures and second, we discuss professional due care considerations. Continuous Audit of Revenue Procedures The client has provided access to all order-to-pay data in real-time by allowing the audit firm to connect its proprietary analytics software to the company’s relational database. As purchase orders are recorded in the client’s database, the auditor’s software automatically tests and captures the audit data. 1. Audit Process We performed the following audit procedures on this data throughout the year: Descriptive statistics and diagnostic tests performed on the purchasing data include utilizing cognitive technologies to classify unusual transactions and evaluate ongoing concerns. Additionally, we used cognitive technologies combined with continuous auditing to perform risk level assessments and test transactions for routine qualities. Continuous auditing allows us to capture and test routine transactions for consistent processing; transactions will be copied to an exception file if they do not pass the tests to be categorized as routine transactions. Through continuous auditing, we were able to reduce the audit sample risk since inferences from limited samples 9-190
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were reduced or eliminated from the process, focus on prioritized risks in the client’s data, and reduce engagement times to increase the value of the client’s financial statement information in the capital markets. 2. Professional Due Care Considerations Since purchase transactions have a high data volume of personally identifiable data, care must be taken to ensure that the client’s data remains private. For example, there are additional audit risks related to client data security and client privacy when using emerging data analytics options. Using internet browsers can increase risks related to private information, since websites can use cookies to trace data retrieved from website pages, password keystrokes, and cursor movements. To mitigate these risks, our team utilizes a VPN (virtual private network) to access private data securely and protect sensitive data from being gathered by outside sources. PAC 10.3 Financial Accounting: Note: there are many ways to solve this problem, including using VLOOKUP and PowerQuery for joining the tables. What follows is the simplest way to perform this task. 1.
Step 1: In the invoices worksheet of the PAC 10.1 data set, create new columns to bring in the data related to the POItemQty and RecItemQty from the other sheets. Then, select all the Invoice data and sort the rows by PONo. Check the sequence of PONo data for any missing PO numbers and note the range of PO’s (6005-6031) by making a new column with the formula =if(G3G2=1,””,”Missing”) in the cell for row 3. Copy this formula through the end of the data rows. Note if any PO numbers are missing. (There are no missing PONos in this data). Step 2: In the PurchaseOrder worksheet, select all the data and sort the rows according to PONo. Check for any missing PO numbers between 6005 and 6031. (There are no missing PONos in this data). Copy the rows of data of POItemQty that correspond with the POs in the Invoices worksheet (PO numbers 6005-6031). The screenshot below shows this new column populated in the invoices worksheet. Step 3: In the ReceivingReport worksheet, select all the data and sort the rows according to PONo. Check for any missing PO numbers between 6005 and 6031. (There are no missing PONos in this data, but there is one PO number error that needs to be corrected, as both Receiving report 5019 and 5020 state that they relate to Purchase Order 6026. When they compare these quantities purchased to the PurchaseOrder data, they will see that Receiving Report 5019 pertains to 6024 rather than 6025, and they will need to do this correction on the Receiving Report before proceeding). Copy the rows of data of RecItemQty that correspond with the POs into the Invoices file (PO numbers 6005-6031). The screenshot below shows this new column populated in the invoices worksheet. Step 4: In the Invoices worksheet, create two new columns to the right of the data copied into the Invoices worksheet. Label the columns as each of the following new variables: • •
InvPOQtyDiff = (cell with the InvoiceQty on that row)-(cell with the POItemQty) InvRecQtyDiff = (cell with the InvoiceQty on that row)-(cell with the RecItemQty)
Then, copy these formulas through the last row of the data in each column. View the results in the screenshot below. Step 5: Create the tolerance test by creating two new columns to the right of the last data column by labeling the columns as follows, and entering the following formulas, and then copy through to the end of the data rows: •
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PODiff Tolerance = if(ABS(InvPOQtyDiff cell)> (.02*POItemQty), “Exception”,”OK”)
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RecDiff Tolerance = if(ABS(InvRecQtyDiff cell)> (.02*POItemQty), “Exception”,”OK”)
Here is an example of the results with the first formula shown in the formula bar.
The results will show four invoices which need to be researched as exceptions and should not be authorized as “OKay to Pay.” Alternative: If you want your student to perform this exercise with VLOOKUP: At the Invoices tab of the Excel file, create columns to perform a VLOOKUP to pull the following variables into the invoices tab: • • • •
VendorID (from receiving) ITEMID (from receiving) RecItemQty Recdate
NOTE: there are several invoices with “N/A” – this means that the items have not been received and these should not be included on the “Okay to Pay” list. The difference and the 2% tolerance test will have to be performed with this new data, so the students would have to perform the steps 4 and 5 from above, and the results will be the same as above. 2. The benefits of performing this analysis with Excel eliminates the need for the manual matching of the hardcopy invoice data to the PO and receiving data, creating efficiencies in the process of approving vendor invoices and improve data veracity as the AP clerk might just “eyeball” the differences rather than calculate the percentage difference when determining the 2% tolerance threshold. PAC 10.4 Managerial Accounting: Solutions may vary based on the graphs prepared by the students and the items interpreted as important metrics for consideration and inclusion in the dashboard.
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The following is one of many options in Excel to create the dashboard showing (1) Number of purchases by vendor in a vertical bar graph (2) Total Purchases by vendor in a tree map (3) Total Purchases by inventory item in a tree map Step 1: Open the data set provided for PAC 10 Adventure Sports. In the Invoices worksheet, select the data rows and columns, including the column names, and insert a table, naming it “Invoices”. Insert a new worksheet, naming it “Dashboard” Step 2: For the (1) bar chart, go to the Insert menu, and insert a Pivot Table and Pivot Chart on the Dashboard worksheet starting in Cell A1. Select Invoice Count for the Values and Vendor name for the rows. Format chart as desired.
Step 3: For the Purchases by Vendor tree map, From the Invoices table, insert a pivot table and pivot chart on the Dashboard worksheet, starting in cell A18. Select Invoice Total for the values, and Vendor Name for the rows. Then copy the pivot table data rows (not the header or the total rows), as tree maps cannot be made in Excel from a pivot table. Highlight these copied cells, and insert a tree map chart starting in row 18 to the right of the pivot chart. Format as desired.
Step 4: For the Purchases by Product tree map: From the Invoices table, insert a piot table and pivot chart on the Dashboard worksheet starting in row A 39. Select Invoice total for the values and ItemDescription for the rows. Then copy the pivot table data rows (not the header or the total rows) as tree maps cannot be made in Excel from a pivot table. Highlight these 9-193
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copied cells, and insert a tree map chart starting in row 18 to the right of the pivot chart. Format as desired.
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PAC 10.5 Tax Accounting: This process is a routine process that is the same every month. Due to that consistency, and the consistency of the integrity of information located on tax authorities’ websites, this process is appropriate for robotic process automation. Technologies such as UiPath can be used to “record” the steps of going to each tax authority website, copying the relevant information, and pasting it into an Excel spreadsheet. The monthly income value can then be copied from the appropriate report file, and the tax accrual calculations can be performed automatically. LO 10.1, 10.2,10.3, BT: AP, Difficulty: Hard, TOT: 120 min., AACSB: Data Analytics, AICPA AC: Technology and Tools
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