Solutions Manual Data Analytics for Accounting 2nd Edition by Vernon Richardson, ISBN: 9781260837834.
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Course
Intro to analytics
Institution
Intro To Analytics
Solutions for Solutions Manual Data Analytics for Accounting 2nd Edition by Vernon Richardson, Ryan Teeter, Katie Terrell.
SBN-10 : 1260837831
ISBN-13 : 9781260837834
chapters 1 to 11, solutions manual for Data Analytics for Accounting 2e Richardson.
SOLUTIONS MANUAL Data Analytics for Accounting 2nd Edition by Vernon Richardson
Solutions Manual – Chapter 1
Solutions to Multiple Choice Questions
1. B
2. D
3. C
4. C
5. C
6. C
7. D
8. B
9. B
10. B
Solutions to Discussion Questions
1. 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 students? By analyzing this type of data, previously unknown patterns will
emerge that will make recruiting students more effective.
2. 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 plans 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)
3. There are many potential answers. Accountants might use data analytics to learn more about
their allowance for doubtful accounts by learning which customers pay or do not pay their
receivable balances on a timely basis. This will help make a more accurate balance of net
receivables.
4. 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.
5. 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.
6. 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.
7. 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.
8. Alibaba uses the profiling data approach to identify potential cases of fraud. Alibaba has worked
to capture fraud signals directly from its extensive database of user behaviors and its network,
then analyzes them in real-time using machine learning to accurately sort the bad users from
the good ones.
9. Facebook uses link prediction to predict a relationship between two people when it suggests
people that one likely knows due to similar other friends, high schools, college or work
locations, etc.
10. 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.
11. 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.
12. 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 loanee would be able to repay the loan.
13. 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
they have a clean criminal record? Have they had a loan with LendingClub before and did they
repay it?
,Solutions to Problems
Note: Some problems and solutions may be altered in Connect for auto grading purposes.
Problem 1-1
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.
Yes/No Predictive Attributes
No desc (Loan description provided by borrower)
Yes dti (Monthly debt payments to monthly income Ratio)
Yes grade (LC assigned loan grade)
Yes home_ownership (values include Rent, Own, Mortgage, Other)
No next_pymnt_d (Next scheduled payment date)
No term (The number of payments on the loan)
Yes tot_cur_bal (Total current balance of all accounts)
Problem 1-2
Potential attributes from the RejectStats data dictionary that might help predict loan acceptance or
rejection include the following:
Amount Requested
Risk_Score
Debt-to-Income Ratio
Zip Code
State (Possibly)
Employment Length
Problem 1-3
Percentage of total loans rejected that live in Arkansas = 1.219%
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.
Problem 1-4
, Loan
State Rejection %
CA 0.13292708
TX 0.08344411
NY 0.0797736
FL 0.07688089
PA 0.04401981
IL 0.04246422
OH 0.03779744
NJ 0.03708008
GA 0.03683527
VA 0.03131478
MI 0.02718255
NC 0.02672393
MA 0.02547822
MD 0.02340048
AZ 0.02142811
MO 0.01954559
WA 0.0187585
CO 0.01812325
AL 0.0169798
CT 0.01640652
SC 0.01569535
LA 0.01450077
WI 0.01430865
MN 0.01407314
KY 0.01367649
NV 0.01275305
AR 0.01219062
OK 0.01103943
OR 0.00954581
KS 0.00862547
UT 0.00692579
WV 0.00643153
NM 0.00590939
HI 0.005756
NH 0.00551739
RI 0.00498905
DE 0.00354346
MT 0.00284933
VT 0.00250537
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