Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
SOLUTION . MANUAL . FOR
Data .Analytics .for .Accounting, .3rd .Edition .Richardson
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, Richardson, Teeter, Terrell – Data Analytics for Accounting, 3e
Chapter .1-9
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
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, 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.
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, 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
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