Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Solution Manual For
Data Analytics for Accounting, 3rd Edition Richardson
Chapter 1-9
1
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Table of Contents
Chapter 1: Data Analytics for Accounting and Identifying the Questions
Chapter 2: Mastering the Data
Chapter 3: Performing the Test Plan and Analyzing the Results
Chapter 4: Communicating Results and Visualizations
Chapter 5: The Modern Accounting Environment
Chapter 6: Audit Data Analytics
Chapter 7: Managerial Analytics
Chapter 8: Financial Statement Analytics
Chapter 9: Tax Analytics
Chapter 10: Project Chapter (Basic)
Chapter 11: Project Chapter (Advanced): Analyzing Dillard’s Data to Predict Sales Returns
Appendix A: Basic Statistics Tutorial
Appendix B: Excel (Formatting, Sorting, Filtering, and PivotTables)
Appendix C: Accessing the Excel Data Analysis Toolpak
Appendix D: SQL Part 1
Appendix E: SQL Part 2
Appendix F: Power Query in Excel and Power BI
Appendix G: Power BI Desktop
Appendix H: Tableau Prep Builder
Appendix I: Tableau Desktop
Appendix J: Data Dictionaries
2
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Solutions Manual – Chapter 1
Solutions to Multiple Choice Questions
1. (LO 1-1) Big Data is often described by the four Vs, or
a. volume, velocity, veracity, and variability.
b. volume, velocity, veracity, and variety.
c. volume, volatility, veracity, and variability.
d. variability, velocity, veracity, and variety.
Answer: b
2. LO 1-4) Which data approach attempts to assign each unit in a population into a small set of
classes (or groups) where the unit best fits?
a. Regression
b. Similarity matching
c. Co-occurrence grouping
d. Classification
Answer: d
3. (LO 1-4) Which data approach attempts to identify similar individuals based on data known
about them?
a. Classification
b. Regression
c. Similarity matching
d. Data reduction
Answer: c
4. (LO 1-4) Which data approach attempts to predict connections between two data items?
a. Profiling
b. Classification
c. Link prediction
d. Regression
3
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Answer: c
5. (LO 1-6) Which of these terms is defined as being a central repository of descriptions for all of
the data attributes of the dataset?
a. Big Data
b. Data warehouse
c. Data dictionary
d. Data Analytics
Answer: c
6. (LO 1-5) Which skills were not emphasized that analytic-minded accountants should have?
a. Developed an analytics mindset
b. Data scrubbing and data preparation
c. Classification of test approaches
d. Statistical data analysis competency
Answer: c
7. (LO 1-5) In which areas were skills not emphasized for analytic-minded accountants?
a. Data quality
b. Descriptive data analysis
c. Data visualization and data reporting
d. Data and systems analysis and design
Answer: d
8. (LO 1-4) The IMPACT cycle includes all except the following steps:
a. perform test plan.
b. visualize the data.
c. master the data.
d. track outcomes.
Answer: b
9. (LO 1-4) The IMPACT cycle specifically includes all except the following steps:
a. data preparation.
b. communicate insights.
c. address and refine results.
d. perform test plan.
4
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Answer: a
10. LO 1-1) By the year 2024, the volume of data created, captured, copied, and consumed
worldwide will be 149 .
a. zettabytes
b. petabytes
c. exabytes
d. yottabytes
Answer: a
Solutions to Discussion and Analysis Questions
1. The accounting function is one of being an information provider. To the extent that data is
available to address accounting questions, be they tax, managerial, audit or financial questions.
With such rich available data, and software tools to prepare and analyze the data, data analytics
will continue to be an important tool for accountants to use.
2. Data analytics is defined as the process of evaluating data with the purpose of drawing
conclusions to address business questions. Indeed, effective Data Analytics provides a way to
search through large structured and unstructured data to identify unknown patterns or
relationships.
A university might learn from the analyzing the demographics of its current set of students in
order to attract its future student recruits. Did they come from cities or high schools that were
close by? Were their parents alumni of the university? Did they score high on certain parts of
the ACT? Were those offered a scholarship more likely to attend, etc.? Was social media
effective in attracting new, potentially stronger students? By analyzing this type of data,
previously unknown patterns will emerge that will make recruiting students more effective.
3. There are many potential answers. For example, Monsanto may use mathematical and
statistical models to plot out the best times to plant both male and female plants and where to
plant them to maximize yield. (https://www.cio.com/article/3221621/analytics/6-data-
analytics-success-stories-an-inside-look.html#tk.cio_rs)
4. There are many potential answers. Data analytics gives both internal and external auditors
additional tools to examine every accounting transaction and assess for compliance with GAAP.
The audit process is changing from a traditional process toward a more automated one, which
will allow audit professionals to focus more on the logic and rationale behind data queries and
less on the gathering of the actual data. No longer will they be simply checking for errors,
material misstatements, fraud, and risk in financial statements or merely be reporting their
findings at the end of the engagement. Instead, audit professionals will now be collecting and
5
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
analyzing the company’s data similar to the way a business analyst would help management
make better business decisions. In this way, data analytics offers value to the audit function.
5. There are many potential answers. For example, data analytics associated with financial
reporting may help accountants determine if any of their inventory obsolete? It may also help
the company benchmark on the financial statements and financial reporting of other similar
companies and understand their accounting practices to help infer their own.
6. Management accountants address the information needs of management. They will often see
what questions management has, find applicable data to address those questions, conduct
analysis of the data, and report the results to management to help them make data-driven
decisions. This is consistent with the data analytics process and the IMPACT model.
7. The IMPACT cycle suggests an order of 1) Identifying the Questions; 2) Mastering the Data; 3)
Performing the test plan; 4) Addressing and refining results; 5) Communicating insights and 6)
Tracking outcomes. The cycle starts with a question and then identifying data and test plan that
might address that question. The results of the data analysis are communicated and tracked
which may lead to additional, possibly more refined questions that then restart the cycle.
8. Data analysis is most effective when a question is identified that needs to be addressed. That
will focus the analysis on which data and which test method might be most effective in
addressing or answering the question.
9. Mastering the data requires one to know what data is available and whether it might be able to
help address the business problem. We need to know everything about the data, including how
to access it, its availability, how reliable it is (if there are errors), and what time periods it covers
to make sure it coincides with the timing of our business problem, etc.
10. Facebook uses link prediction to predict a relationship between two people when it suggests
people that one likely knows due to similar other friends, extended family, high schools, college
or work locations, etc.
11. While sampling is useful, it is still just that, sampling. By looking at all of the transactions and
testing them in a way that will highlight the ones that are the biggest dollar items, or are most
unusual, that will allow auditors to focus on specific items that might be of material significance.
12. There are several correct answers. One data approach might be regression analysis where, given
a balance of total accounts receivable held by a firm, how long it has been outstanding, if they
have paid debts in the past all will help predict the appropriate level of allowance for doubtful
accounts for bad debts.
13. The Debt-to-Income ratio might suggest to LendingClub that the person asking for the loan was
simply asking for too big of a loan and they would have little ability to repay it. The lower the
credit score, the less likely the potential borrower would be able to repay the loan.
14. There are many other potential predictors of whether the LendingClub would pay a loan. Here
are a few possibilities: What other debt do they have? How much is their disposable income? Do
6
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
they have a clean criminal record? Have they had a loan with LendingClub before and did they
repay it? Do they rent or own their house?
Solutions to Problems
Note: Some problems and solutions may be altered in Connect for auto grading purposes.
1. (LO 1-4) Match each specific Data Analytics test to a specific test approach, as part of performing
test plan:
• Classification
• Regression
• Similarity Matching
• Clustering
• Co-occurrence Grouping
• Profiling
• Link Prediction
• Data Reduction
Specific Data Analytics Test Test Approach
1. Predict which firms will go bankrupt and which firms will Classification
not go bankrupt.
2. Use stratified sampling to focus audit effort on transactions Data Reduction
with greatest risk.
3. Work to understand normal behavior, to then be able Profiling
identify abnormal behavior (such as fraud).
4. Look for relationships between related parties that are not Link Prediction
otherwise disclosed.
5. Predict which new customers resemble the company’s best Similarity Matching
customers.
6. Predict the relationship between an investment in Regression
advertising expenditures and subsequent operating
income.
7. Segment all of the company’s customers into groups that Clustering
will allow further specific analysis.
8. The customers who buy product X will be most likely to be Co-occurrence
also interested in product Y. Grouping
7
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
2. (LO 1-4) Match each of the specific Data Analytics tasks to the stage of the IMPACT cycle:
• Identify the Question
• Master the Data
• Perform Test Plan
• Address and Refine Results
• Communicate Insights
• Track Outcomes
Specific Data Analytics Task Stage of IMPACT
Cycle
1. Should we use company-specific data or macro-economic Master the Data
data to address the accounting question?
2. What are appropriate cost drivers for activity-based costing Identify the Question
purposes?
3. Should we consider using regression analysis or clustering Perform the Analysis
analysis to evaluate the data?
4. Should we use tables or graphs to show management what Communicate Insights
we’ve found?
5. Now that we’ve evaluated the data one way, should we Address and Refine
perform another analysis to gain additional insights? Results
6. What type of dashboard should we use to get the latest, Track Outcomes
up-to-date results?
3. (LO 1-5) Match the specific analysis need/characteristic to the appropriate Microsoft Track tool:
• Excel
• Power Query
• Power BI
• Power Automate
Specific Analysis Need/Characteristic Microsoft Track Tool
1. Basic Visualization Excel
2. Robotics Process Automation Power Automate
3. Data joining Power Query
4. Advanced visualization Power BI
5. Works on Windows/Mac/Online platforms Excel
6. Dashboards Power BI
7. Collect data from multiple sources Power Automate
8. Data cleaning Power Query
4. (LO 1-5) Match the specific analysis need/characteristic to the appropriate Tableau Track tool:
• Tableau Prep Builder
• Tableau Desktop
• Tableau Public
8
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Specific Analysis Need/Characteristic Tableau Track Tool
1. Advanced Visualization Tableau Desktop
2. Analyze and share public datasets Tableau Public
3. Data joining Tableau Prep Builder
4. Presentations Tableau Desktop
5. Data transformation Tableau Prep Builder
6. Dashboards Tableau Desktop
7. Data cleaning Tableau Prep Builder
5. (LO 1-6) Here are the predictive attributes and whether they would be applicable to predicting
which loans would be delinquent and which loans will ultimately be fully repaid.
Predictive?
Predictive Attributes
(Yes/No)
1. date (Date when the borrower accepted the offer) No
2. desc (Loan description provided by borrower) No
3. dti (A ratio of debt owed to income earned) Yes
4. grade (LC assigned loan grade) Yes
5. home_ownership (Values include Rent, Own, Mortgage,
Yes
Other)
6. loanAmnt (Amount of the loan) Yes
7. next_pymnt_d (Next scheduled payment date) No
8. term (Number of payments on the loan) No
9. tot_cur_bal (Total current balance of all accounts) Yes
6. (LO 1-6) Navigate to the Connect Additional Student Resources page. Under Chapter 1 Data Files,
download and consider the rejected loans dataset of LendingClub data titled “DAA Chapter 1-1
Data”. Choose among these attributes in the data dictionary, and indicate which are likely to be
predictive of loan rejection, and which are not.
Predictive Attributes Predictive? (Yes/No)
1. Amount Requested Yes
2. Zip Code Yes
3. Loan Title No
4. Debt-To-Income Ratio Yes
5. Application Date No
6. Risk_Score Yes
7. Employment Length Yes
7.
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, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
7A) Multiple Choice: What is the percentage of total loans rejected in the United States that
came from Arkansas?
a. Less than 1%.
b. Between 1% and 2%.
c. More than 2%.
Answer: b
Percentage of total loans rejected that live in Arkansas = 1.219%
7B) Multiple Choice: Is this loan rejection percentage greater than the percent of the U.S.
population that lives in Arkansas (per 2010 census)?
a. Loan rejection percentage is greater than the population.
b. Loan rejection percentage is less than the population.
Answer: a
2,915,918 population in Arkansas divided by USA population of 308,745,538 = 0.9444%
The loan rejection percentage is greater than the percent of the USA population that lives in Arkansas
(per 2010 census), but is reasonably close.
8. (LO 1-6) Download the rejected loans dataset of LendingClub data titled “DAA Chapter 1-1 Data”
from Connect Additional Student Resources and do an Excel PivotTable by state; then figure out the
number of rejected applications for each state.
8A) Put the following states in order of their loan rejection percentage based on the count of rejected
loans (from high [1] to low [11]) of the total rejected loans.
State Rank 1 (High) to 11 (Low)
1. Arkansas (AR) 2
2. Hawaii (HI) 9
3. Kansas (KS) 5
4. New Hampshire
10
(NH)
5. New Mexico (NM) 8
6. Nevada (NV) 1
7. Oklahoma (OK) 3
8. Oregon (OR) 4
9. Rhode Island (RI) 11
10. Utah (UT) 6
11. West Virginia (WV) 7
10
Solution Manual For
Data Analytics for Accounting, 3rd Edition Richardson
Chapter 1-9
1
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Table of Contents
Chapter 1: Data Analytics for Accounting and Identifying the Questions
Chapter 2: Mastering the Data
Chapter 3: Performing the Test Plan and Analyzing the Results
Chapter 4: Communicating Results and Visualizations
Chapter 5: The Modern Accounting Environment
Chapter 6: Audit Data Analytics
Chapter 7: Managerial Analytics
Chapter 8: Financial Statement Analytics
Chapter 9: Tax Analytics
Chapter 10: Project Chapter (Basic)
Chapter 11: Project Chapter (Advanced): Analyzing Dillard’s Data to Predict Sales Returns
Appendix A: Basic Statistics Tutorial
Appendix B: Excel (Formatting, Sorting, Filtering, and PivotTables)
Appendix C: Accessing the Excel Data Analysis Toolpak
Appendix D: SQL Part 1
Appendix E: SQL Part 2
Appendix F: Power Query in Excel and Power BI
Appendix G: Power BI Desktop
Appendix H: Tableau Prep Builder
Appendix I: Tableau Desktop
Appendix J: Data Dictionaries
2
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Solutions Manual – Chapter 1
Solutions to Multiple Choice Questions
1. (LO 1-1) Big Data is often described by the four Vs, or
a. volume, velocity, veracity, and variability.
b. volume, velocity, veracity, and variety.
c. volume, volatility, veracity, and variability.
d. variability, velocity, veracity, and variety.
Answer: b
2. LO 1-4) Which data approach attempts to assign each unit in a population into a small set of
classes (or groups) where the unit best fits?
a. Regression
b. Similarity matching
c. Co-occurrence grouping
d. Classification
Answer: d
3. (LO 1-4) Which data approach attempts to identify similar individuals based on data known
about them?
a. Classification
b. Regression
c. Similarity matching
d. Data reduction
Answer: c
4. (LO 1-4) Which data approach attempts to predict connections between two data items?
a. Profiling
b. Classification
c. Link prediction
d. Regression
3
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Answer: c
5. (LO 1-6) Which of these terms is defined as being a central repository of descriptions for all of
the data attributes of the dataset?
a. Big Data
b. Data warehouse
c. Data dictionary
d. Data Analytics
Answer: c
6. (LO 1-5) Which skills were not emphasized that analytic-minded accountants should have?
a. Developed an analytics mindset
b. Data scrubbing and data preparation
c. Classification of test approaches
d. Statistical data analysis competency
Answer: c
7. (LO 1-5) In which areas were skills not emphasized for analytic-minded accountants?
a. Data quality
b. Descriptive data analysis
c. Data visualization and data reporting
d. Data and systems analysis and design
Answer: d
8. (LO 1-4) The IMPACT cycle includes all except the following steps:
a. perform test plan.
b. visualize the data.
c. master the data.
d. track outcomes.
Answer: b
9. (LO 1-4) The IMPACT cycle specifically includes all except the following steps:
a. data preparation.
b. communicate insights.
c. address and refine results.
d. perform test plan.
4
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Answer: a
10. LO 1-1) By the year 2024, the volume of data created, captured, copied, and consumed
worldwide will be 149 .
a. zettabytes
b. petabytes
c. exabytes
d. yottabytes
Answer: a
Solutions to Discussion and Analysis Questions
1. The accounting function is one of being an information provider. To the extent that data is
available to address accounting questions, be they tax, managerial, audit or financial questions.
With such rich available data, and software tools to prepare and analyze the data, data analytics
will continue to be an important tool for accountants to use.
2. Data analytics is defined as the process of evaluating data with the purpose of drawing
conclusions to address business questions. Indeed, effective Data Analytics provides a way to
search through large structured and unstructured data to identify unknown patterns or
relationships.
A university might learn from the analyzing the demographics of its current set of students in
order to attract its future student recruits. Did they come from cities or high schools that were
close by? Were their parents alumni of the university? Did they score high on certain parts of
the ACT? Were those offered a scholarship more likely to attend, etc.? Was social media
effective in attracting new, potentially stronger students? By analyzing this type of data,
previously unknown patterns will emerge that will make recruiting students more effective.
3. There are many potential answers. For example, Monsanto may use mathematical and
statistical models to plot out the best times to plant both male and female plants and where to
plant them to maximize yield. (https://www.cio.com/article/3221621/analytics/6-data-
analytics-success-stories-an-inside-look.html#tk.cio_rs)
4. There are many potential answers. Data analytics gives both internal and external auditors
additional tools to examine every accounting transaction and assess for compliance with GAAP.
The audit process is changing from a traditional process toward a more automated one, which
will allow audit professionals to focus more on the logic and rationale behind data queries and
less on the gathering of the actual data. No longer will they be simply checking for errors,
material misstatements, fraud, and risk in financial statements or merely be reporting their
findings at the end of the engagement. Instead, audit professionals will now be collecting and
5
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
analyzing the company’s data similar to the way a business analyst would help management
make better business decisions. In this way, data analytics offers value to the audit function.
5. There are many potential answers. For example, data analytics associated with financial
reporting may help accountants determine if any of their inventory obsolete? It may also help
the company benchmark on the financial statements and financial reporting of other similar
companies and understand their accounting practices to help infer their own.
6. Management accountants address the information needs of management. They will often see
what questions management has, find applicable data to address those questions, conduct
analysis of the data, and report the results to management to help them make data-driven
decisions. This is consistent with the data analytics process and the IMPACT model.
7. The IMPACT cycle suggests an order of 1) Identifying the Questions; 2) Mastering the Data; 3)
Performing the test plan; 4) Addressing and refining results; 5) Communicating insights and 6)
Tracking outcomes. The cycle starts with a question and then identifying data and test plan that
might address that question. The results of the data analysis are communicated and tracked
which may lead to additional, possibly more refined questions that then restart the cycle.
8. Data analysis is most effective when a question is identified that needs to be addressed. That
will focus the analysis on which data and which test method might be most effective in
addressing or answering the question.
9. Mastering the data requires one to know what data is available and whether it might be able to
help address the business problem. We need to know everything about the data, including how
to access it, its availability, how reliable it is (if there are errors), and what time periods it covers
to make sure it coincides with the timing of our business problem, etc.
10. Facebook uses link prediction to predict a relationship between two people when it suggests
people that one likely knows due to similar other friends, extended family, high schools, college
or work locations, etc.
11. While sampling is useful, it is still just that, sampling. By looking at all of the transactions and
testing them in a way that will highlight the ones that are the biggest dollar items, or are most
unusual, that will allow auditors to focus on specific items that might be of material significance.
12. There are several correct answers. One data approach might be regression analysis where, given
a balance of total accounts receivable held by a firm, how long it has been outstanding, if they
have paid debts in the past all will help predict the appropriate level of allowance for doubtful
accounts for bad debts.
13. The Debt-to-Income ratio might suggest to LendingClub that the person asking for the loan was
simply asking for too big of a loan and they would have little ability to repay it. The lower the
credit score, the less likely the potential borrower would be able to repay the loan.
14. There are many other potential predictors of whether the LendingClub would pay a loan. Here
are a few possibilities: What other debt do they have? How much is their disposable income? Do
6
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
they have a clean criminal record? Have they had a loan with LendingClub before and did they
repay it? Do they rent or own their house?
Solutions to Problems
Note: Some problems and solutions may be altered in Connect for auto grading purposes.
1. (LO 1-4) Match each specific Data Analytics test to a specific test approach, as part of performing
test plan:
• Classification
• Regression
• Similarity Matching
• Clustering
• Co-occurrence Grouping
• Profiling
• Link Prediction
• Data Reduction
Specific Data Analytics Test Test Approach
1. Predict which firms will go bankrupt and which firms will Classification
not go bankrupt.
2. Use stratified sampling to focus audit effort on transactions Data Reduction
with greatest risk.
3. Work to understand normal behavior, to then be able Profiling
identify abnormal behavior (such as fraud).
4. Look for relationships between related parties that are not Link Prediction
otherwise disclosed.
5. Predict which new customers resemble the company’s best Similarity Matching
customers.
6. Predict the relationship between an investment in Regression
advertising expenditures and subsequent operating
income.
7. Segment all of the company’s customers into groups that Clustering
will allow further specific analysis.
8. The customers who buy product X will be most likely to be Co-occurrence
also interested in product Y. Grouping
7
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
2. (LO 1-4) Match each of the specific Data Analytics tasks to the stage of the IMPACT cycle:
• Identify the Question
• Master the Data
• Perform Test Plan
• Address and Refine Results
• Communicate Insights
• Track Outcomes
Specific Data Analytics Task Stage of IMPACT
Cycle
1. Should we use company-specific data or macro-economic Master the Data
data to address the accounting question?
2. What are appropriate cost drivers for activity-based costing Identify the Question
purposes?
3. Should we consider using regression analysis or clustering Perform the Analysis
analysis to evaluate the data?
4. Should we use tables or graphs to show management what Communicate Insights
we’ve found?
5. Now that we’ve evaluated the data one way, should we Address and Refine
perform another analysis to gain additional insights? Results
6. What type of dashboard should we use to get the latest, Track Outcomes
up-to-date results?
3. (LO 1-5) Match the specific analysis need/characteristic to the appropriate Microsoft Track tool:
• Excel
• Power Query
• Power BI
• Power Automate
Specific Analysis Need/Characteristic Microsoft Track Tool
1. Basic Visualization Excel
2. Robotics Process Automation Power Automate
3. Data joining Power Query
4. Advanced visualization Power BI
5. Works on Windows/Mac/Online platforms Excel
6. Dashboards Power BI
7. Collect data from multiple sources Power Automate
8. Data cleaning Power Query
4. (LO 1-5) Match the specific analysis need/characteristic to the appropriate Tableau Track tool:
• Tableau Prep Builder
• Tableau Desktop
• Tableau Public
8
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Specific Analysis Need/Characteristic Tableau Track Tool
1. Advanced Visualization Tableau Desktop
2. Analyze and share public datasets Tableau Public
3. Data joining Tableau Prep Builder
4. Presentations Tableau Desktop
5. Data transformation Tableau Prep Builder
6. Dashboards Tableau Desktop
7. Data cleaning Tableau Prep Builder
5. (LO 1-6) Here are the predictive attributes and whether they would be applicable to predicting
which loans would be delinquent and which loans will ultimately be fully repaid.
Predictive?
Predictive Attributes
(Yes/No)
1. date (Date when the borrower accepted the offer) No
2. desc (Loan description provided by borrower) No
3. dti (A ratio of debt owed to income earned) Yes
4. grade (LC assigned loan grade) Yes
5. home_ownership (Values include Rent, Own, Mortgage,
Yes
Other)
6. loanAmnt (Amount of the loan) Yes
7. next_pymnt_d (Next scheduled payment date) No
8. term (Number of payments on the loan) No
9. tot_cur_bal (Total current balance of all accounts) Yes
6. (LO 1-6) Navigate to the Connect Additional Student Resources page. Under Chapter 1 Data Files,
download and consider the rejected loans dataset of LendingClub data titled “DAA Chapter 1-1
Data”. Choose among these attributes in the data dictionary, and indicate which are likely to be
predictive of loan rejection, and which are not.
Predictive Attributes Predictive? (Yes/No)
1. Amount Requested Yes
2. Zip Code Yes
3. Loan Title No
4. Debt-To-Income Ratio Yes
5. Application Date No
6. Risk_Score Yes
7. Employment Length Yes
7.
9
, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
7A) Multiple Choice: What is the percentage of total loans rejected in the United States that
came from Arkansas?
a. Less than 1%.
b. Between 1% and 2%.
c. More than 2%.
Answer: b
Percentage of total loans rejected that live in Arkansas = 1.219%
7B) Multiple Choice: Is this loan rejection percentage greater than the percent of the U.S.
population that lives in Arkansas (per 2010 census)?
a. Loan rejection percentage is greater than the population.
b. Loan rejection percentage is less than the population.
Answer: a
2,915,918 population in Arkansas divided by USA population of 308,745,538 = 0.9444%
The loan rejection percentage is greater than the percent of the USA population that lives in Arkansas
(per 2010 census), but is reasonably close.
8. (LO 1-6) Download the rejected loans dataset of LendingClub data titled “DAA Chapter 1-1 Data”
from Connect Additional Student Resources and do an Excel PivotTable by state; then figure out the
number of rejected applications for each state.
8A) Put the following states in order of their loan rejection percentage based on the count of rejected
loans (from high [1] to low [11]) of the total rejected loans.
State Rank 1 (High) to 11 (Low)
1. Arkansas (AR) 2
2. Hawaii (HI) 9
3. Kansas (KS) 5
4. New Hampshire
10
(NH)
5. New Mexico (NM) 8
6. Nevada (NV) 1
7. Oklahoma (OK) 3
8. Oregon (OR) 4
9. Rhode Island (RI) 11
10. Utah (UT) 6
11. West Virginia (WV) 7
10
