Richardson,eTeeter,eTerrelle–eDataeAnalyticseforeAccounting,e3e
SOLUTION MANUAL FOR e e
Data Analytics for Accounting, 3rd Edition Richardson Chapter
e e e e e e e e
1-9
Solutions Manual – Chapter 1
e e e e
SolutionsetoeMultipleeChoiceeQuestions
1. (LOe1-1)eBigeDataeiseoftenedescribedebyetheefoureVs,eor
a. volume,evelocity,everacity,eandevariability.
b. volume,evelocity,everacity,eandevariety.
c. volume,evolatility,everacity,eandevariability.
d. variability,evelocity,everacity,eandevariety.
Answer:eb
2. LOe1-
4)eWhichedataeapproacheattemptsetoeassigneeacheuniteineaepopulationeintoeaesmalleseteofeclassese(ore
groups)ewhereetheeunitebestefits?
a. Regression
b. Similarityematching
c. Co-occurrenceegrouping
d. Classification
Answer:ed
3. (LOe1-
4)eWhichedataeapproacheattemptsetoeidentifyesimilareindividualsebasedeonedataeknowneaboutethem
?
a. Classification
b. Regression
c. Similarityematching
d. Dataereduction
Answer:ec
4. (LOe1-4)eWhichedataeapproacheattemptsetoepredicteconnectionsebetweenetwoedataeitems?
a. Profiling
b. Classification
c. Linkeprediction
d. Regression
1
, Richardson,eTeeter,eTerrelle–eDataeAnalyticseforeAccounting,e3e
Answer:ec
5. (LOe1-
6)eWhicheofetheseetermseisedefinedeasebeingeaecentralerepositoryeofedescriptionseforealleofetheedataeattr
ibuteseofetheedataset?
a. BigeData
b. Dataewarehouse
c. Dataedictionary
d. DataeAnalytics
Answer:ec
6. (LOe1-5)eWhicheskillsewereenoteemphasizedethateanalytic-mindedeaccountantseshouldehave?
a. Developedeaneanalyticsemindset
b. Dataescrubbingeandedataepreparation
c. Classificationeofetesteapproaches
d. Statisticaledataeanalysisecompetency
Answer:ec
7. (LOe1-5)eInewhicheareasewereeskillsenoteemphasizedeforeanalytic-mindedeaccountants?
a. Dataequality
b. Descriptiveedataeanalysis
c. Dataevisualizationeandedataereporting
d. Dataeandesystemseanalysiseandedesign
Answer:ed
8. (LOe1-4)eTheeIMPACTecycleeincludesealleexceptetheefollowingesteps:
a. performetesteplan.
b. visualizeetheedata.
c. masteretheedata.
d. trackeoutcomes.
Answer:eb
9. (LOe1-4)eTheeIMPACTecycleespecificallyeincludesealleexceptetheefollowingesteps:
a. dataepreparation.
b. communicateeinsights.
c. addresseanderefineeresults.
d. performetesteplan.
2
, Richardson,eTeeter,eTerrelle–eDataeAnalyticseforeAccounting,e3e
Answer:ea
10. LOe1-
1)eByetheeyeare2024,etheevolumeeofedataecreated,ecaptured,ecopied,eandeconsumedeworldwidee
willebee149e .
a. zettabytes
b. petabytes
c. exabytes
d. yottabytes
Answer:ea
SolutionsetoeDiscussioneandeAnalysiseQuestions
1. Theeaccountingefunctioneiseoneeofebeingeaneinformationeprovider.e Toetheeextentethatedataeiseavail
ableetoeaddresseaccountingequestions,ebeetheyetax,emanagerial,eauditeorefinancialequestions.eWith
esuchericheavailableedata,eandesoftwareetoolsetoeprepareeandeanalyzeetheedata,edataeanalyticsewille
continueetoebeeaneimportantetooleforeaccountantsetoeuse.
2. Dataeanalyticseisedefinedeasetheeprocesseofeevaluatingedataewithetheepurposeeofedrawingeconcl
usionsetoeaddressebusinessequestions.eIndeed,eeffectiveeDataeAnalyticseprovideseaewayetoesear
chethroughelargeestructuredeandeunstructurededataetoeidentifyeunknownepatternseorerelations
hips.
Aeuniversityemightelearnefrometheeanalyzingetheedemographicseofeitsecurrenteseteofestudentseineor
deretoeattracteitsefutureestudenterecruits.eDidetheyecomeefromecitieseorehigheschoolsethatewereeclo
seeby?eWereetheireparentsealumnieofetheeuniversity?eDidetheyescoreehigheonecertainepartseofethee
ACT?eWereethoseeofferedeaescholarshipemoreelikelyetoeattend,eetc.?eWasesocialemediaeeffectiveei
neattractingenew,epotentiallyestrongerestudents?eByeanalyzingethisetypeeofedata,epreviouslyeunkn
ownepatternsewilleemergeethatewillemakeerecruitingestudentsemoreeeffective.
3. Thereeareemanyepotentialeanswers.e Foreexample,eMonsantoemayeuseemathematicaleandestatisti
calemodelsetoeploteoutetheebestetimesetoeplantebothemaleeandefemaleeplantseandewhereetoeplantet
hemetoemaximizeeyield.e(https://www.cio.com/article/3221621/analytics/6-data-eanalytics-
success-stories-an-inside-look.html#tk.cio_rs)
4. Thereeareemanyepotentialeanswers.eDataeanalyticsegivesebotheinternaleandeexternaleauditorseaddi
tionaletoolsetoeexamineeeveryeaccountingetransactioneandeassesseforecomplianceewitheGAAP.eThe
eauditeprocesseisechangingefromeaetraditionaleprocessetowardeaemoreeautomatedeone,ewhichewille
alloweauditeprofessionalsetoefocusemoreeonetheelogiceanderationaleebehindedataequerieseandelesse
onetheegatheringeofetheeactualedata.eNoelongerewilletheyebeesimplyecheckingeforeerrors,emateriale
misstatements,efraud,eanderiskeinefinancialestatementseoremerelyebeereportingetheirefindingseatet
heeendeofetheeengagement.eInstead,eauditeprofessionalsewillenowebeecollectingeand
3
, Richardson,eTeeter,eTerrelle–eDataeAnalyticseforeAccounting,e3e
analyzingetheecompany’sedataesimilaretoetheewayeaebusinesseanalystewouldehelpemanagementem
akeebetterebusinessedecisions.e Inethiseway,edataeanalyticseoffersevalueetoetheeauditefunction.
5. Thereeareemanyepotentialeanswers.eForeexample,edataeanalyticseassociatedewithefinancialerepo
rtingemayehelpeaccountantsedetermineeifeanyeofetheireinventoryeobsolete?eItemayealsoehelpethee
companyebenchmarkeonetheefinancialestatementseandefinancialereportingeofeotheresimilarecom
panieseandeunderstandetheireaccountingepracticesetoehelpeinferetheireown.
6. Managementeaccountantseaddressetheeinformationeneedseofemanagement.e Theyewilleofteneseee
whatequestionsemanagementehas,efindeapplicableedataetoeaddressethoseequestions,econductean
alysiseofetheedata,eandereportetheeresultsetoemanagementetoehelpethememakeedata-
drivenedecisions.e ThiseiseconsistentewithetheedataeanalyticseprocesseandetheeIMPACTemodel.
7. TheeIMPACTecycleesuggestseaneordereofe1)eIdentifyingetheeQuestions;e2)eMasteringetheeData;e3)eP
erformingetheetesteplan;e4)eAddressingeanderefiningeresults;e5)eCommunicatingeinsightseande6)eTr
ackingeoutcomes.eTheecycleestartsewitheaequestioneandetheneidentifyingedataeandetesteplanethatem
ighteaddressethatequestion.eTheeresultseofetheedataeanalysiseareecommunicatedeandetrackedewhic
hemayeleadetoeadditional,epossiblyemoreerefinedequestionsethatethenerestartetheecycle.
8. Dataeanalysiseisemosteeffectiveewheneaequestioneiseidentifiedethateneedsetoebeeaddressed.eThat
ewillefocusetheeanalysiseonewhichedataeandewhichetestemethodemightebeemosteeffectiveeineaddr
essingeoreansweringetheequestion.
9. Masteringetheedataerequireseoneetoeknowewhatedataeiseavailableeandewhethereitemightebeeableetoe
helpeaddressetheebusinesseproblem.eWeeneedetoeknoweeverythingeaboutetheedata,eincludingehowe
toeaccesseit,eitseavailability,ehowereliableeiteise(ifethereeareeerrors),eandewhatetimeeperiodseitecoverse
toemakeesureeitecoincidesewithetheetimingeofeourebusinesseproblem,eetc.
10. Facebookeuseselinkepredictionetoepredicteaerelationshipebetweenetwoepeopleewheneitesuggestsepe
opleethateoneelikelyeknowsedueetoesimilareotherefriends,eextendedefamily,ehigheschools,ecollegeeor
eworkelocations,eetc.
11. Whileesamplingeiseuseful,eiteisestillejustethat,esampling.eByelookingeatealleofetheetransactionseandetes
tingethemeineaewayethatewillehighlightetheeonesethateareetheebiggestedollareitems,eoreareemosteunu
sual,ethatewillealloweauditorsetoefocuseonespecificeitemsethatemightebeeofematerialesignificance.
12. Thereeareeseveralecorrecteanswers.eOneedataeapproachemightebeeregressioneanalysisewhere,egiven
eaebalanceeofetotaleaccountsereceivableeheldebyeaefirm,ehowelongeitehasebeeneoutstanding,eifetheyeh
aveepaidedebtseinetheepasteallewillehelpepredictetheeappropriateeleveleofeallowanceeforedoubtfuleac
countseforebadedebts.
13. TheeDebt-to-
IncomeeratioemightesuggestetoeLendingClubethatetheepersoneaskingeforetheeloanewasesimplyeaskin
geforetooebigeofeaeloaneandetheyewouldehaveelittleeabilityetoerepayeit.eTheeloweretheecreditescore,et
heelesselikelyetheepotentialeborrowerewouldebeeableetoerepayetheeloan.
14. ThereeareemanyeotherepotentialepredictorseofewhetheretheeLendingClubewouldepayeaeloan.eHereea
reeaefewepossibilities:eWhateotheredebtedoetheyehave?eHowemucheisetheiredisposableeincome?eDo
4
SOLUTION MANUAL FOR e e
Data Analytics for Accounting, 3rd Edition Richardson Chapter
e e e e e e e e
1-9
Solutions Manual – Chapter 1
e e e e
SolutionsetoeMultipleeChoiceeQuestions
1. (LOe1-1)eBigeDataeiseoftenedescribedebyetheefoureVs,eor
a. volume,evelocity,everacity,eandevariability.
b. volume,evelocity,everacity,eandevariety.
c. volume,evolatility,everacity,eandevariability.
d. variability,evelocity,everacity,eandevariety.
Answer:eb
2. LOe1-
4)eWhichedataeapproacheattemptsetoeassigneeacheuniteineaepopulationeintoeaesmalleseteofeclassese(ore
groups)ewhereetheeunitebestefits?
a. Regression
b. Similarityematching
c. Co-occurrenceegrouping
d. Classification
Answer:ed
3. (LOe1-
4)eWhichedataeapproacheattemptsetoeidentifyesimilareindividualsebasedeonedataeknowneaboutethem
?
a. Classification
b. Regression
c. Similarityematching
d. Dataereduction
Answer:ec
4. (LOe1-4)eWhichedataeapproacheattemptsetoepredicteconnectionsebetweenetwoedataeitems?
a. Profiling
b. Classification
c. Linkeprediction
d. Regression
1
, Richardson,eTeeter,eTerrelle–eDataeAnalyticseforeAccounting,e3e
Answer:ec
5. (LOe1-
6)eWhicheofetheseetermseisedefinedeasebeingeaecentralerepositoryeofedescriptionseforealleofetheedataeattr
ibuteseofetheedataset?
a. BigeData
b. Dataewarehouse
c. Dataedictionary
d. DataeAnalytics
Answer:ec
6. (LOe1-5)eWhicheskillsewereenoteemphasizedethateanalytic-mindedeaccountantseshouldehave?
a. Developedeaneanalyticsemindset
b. Dataescrubbingeandedataepreparation
c. Classificationeofetesteapproaches
d. Statisticaledataeanalysisecompetency
Answer:ec
7. (LOe1-5)eInewhicheareasewereeskillsenoteemphasizedeforeanalytic-mindedeaccountants?
a. Dataequality
b. Descriptiveedataeanalysis
c. Dataevisualizationeandedataereporting
d. Dataeandesystemseanalysiseandedesign
Answer:ed
8. (LOe1-4)eTheeIMPACTecycleeincludesealleexceptetheefollowingesteps:
a. performetesteplan.
b. visualizeetheedata.
c. masteretheedata.
d. trackeoutcomes.
Answer:eb
9. (LOe1-4)eTheeIMPACTecycleespecificallyeincludesealleexceptetheefollowingesteps:
a. dataepreparation.
b. communicateeinsights.
c. addresseanderefineeresults.
d. performetesteplan.
2
, Richardson,eTeeter,eTerrelle–eDataeAnalyticseforeAccounting,e3e
Answer:ea
10. LOe1-
1)eByetheeyeare2024,etheevolumeeofedataecreated,ecaptured,ecopied,eandeconsumedeworldwidee
willebee149e .
a. zettabytes
b. petabytes
c. exabytes
d. yottabytes
Answer:ea
SolutionsetoeDiscussioneandeAnalysiseQuestions
1. Theeaccountingefunctioneiseoneeofebeingeaneinformationeprovider.e Toetheeextentethatedataeiseavail
ableetoeaddresseaccountingequestions,ebeetheyetax,emanagerial,eauditeorefinancialequestions.eWith
esuchericheavailableedata,eandesoftwareetoolsetoeprepareeandeanalyzeetheedata,edataeanalyticsewille
continueetoebeeaneimportantetooleforeaccountantsetoeuse.
2. Dataeanalyticseisedefinedeasetheeprocesseofeevaluatingedataewithetheepurposeeofedrawingeconcl
usionsetoeaddressebusinessequestions.eIndeed,eeffectiveeDataeAnalyticseprovideseaewayetoesear
chethroughelargeestructuredeandeunstructurededataetoeidentifyeunknownepatternseorerelations
hips.
Aeuniversityemightelearnefrometheeanalyzingetheedemographicseofeitsecurrenteseteofestudentseineor
deretoeattracteitsefutureestudenterecruits.eDidetheyecomeefromecitieseorehigheschoolsethatewereeclo
seeby?eWereetheireparentsealumnieofetheeuniversity?eDidetheyescoreehigheonecertainepartseofethee
ACT?eWereethoseeofferedeaescholarshipemoreelikelyetoeattend,eetc.?eWasesocialemediaeeffectiveei
neattractingenew,epotentiallyestrongerestudents?eByeanalyzingethisetypeeofedata,epreviouslyeunkn
ownepatternsewilleemergeethatewillemakeerecruitingestudentsemoreeeffective.
3. Thereeareemanyepotentialeanswers.e Foreexample,eMonsantoemayeuseemathematicaleandestatisti
calemodelsetoeploteoutetheebestetimesetoeplantebothemaleeandefemaleeplantseandewhereetoeplantet
hemetoemaximizeeyield.e(https://www.cio.com/article/3221621/analytics/6-data-eanalytics-
success-stories-an-inside-look.html#tk.cio_rs)
4. Thereeareemanyepotentialeanswers.eDataeanalyticsegivesebotheinternaleandeexternaleauditorseaddi
tionaletoolsetoeexamineeeveryeaccountingetransactioneandeassesseforecomplianceewitheGAAP.eThe
eauditeprocesseisechangingefromeaetraditionaleprocessetowardeaemoreeautomatedeone,ewhichewille
alloweauditeprofessionalsetoefocusemoreeonetheelogiceanderationaleebehindedataequerieseandelesse
onetheegatheringeofetheeactualedata.eNoelongerewilletheyebeesimplyecheckingeforeerrors,emateriale
misstatements,efraud,eanderiskeinefinancialestatementseoremerelyebeereportingetheirefindingseatet
heeendeofetheeengagement.eInstead,eauditeprofessionalsewillenowebeecollectingeand
3
, Richardson,eTeeter,eTerrelle–eDataeAnalyticseforeAccounting,e3e
analyzingetheecompany’sedataesimilaretoetheewayeaebusinesseanalystewouldehelpemanagementem
akeebetterebusinessedecisions.e Inethiseway,edataeanalyticseoffersevalueetoetheeauditefunction.
5. Thereeareemanyepotentialeanswers.eForeexample,edataeanalyticseassociatedewithefinancialerepo
rtingemayehelpeaccountantsedetermineeifeanyeofetheireinventoryeobsolete?eItemayealsoehelpethee
companyebenchmarkeonetheefinancialestatementseandefinancialereportingeofeotheresimilarecom
panieseandeunderstandetheireaccountingepracticesetoehelpeinferetheireown.
6. Managementeaccountantseaddressetheeinformationeneedseofemanagement.e Theyewilleofteneseee
whatequestionsemanagementehas,efindeapplicableedataetoeaddressethoseequestions,econductean
alysiseofetheedata,eandereportetheeresultsetoemanagementetoehelpethememakeedata-
drivenedecisions.e ThiseiseconsistentewithetheedataeanalyticseprocesseandetheeIMPACTemodel.
7. TheeIMPACTecycleesuggestseaneordereofe1)eIdentifyingetheeQuestions;e2)eMasteringetheeData;e3)eP
erformingetheetesteplan;e4)eAddressingeanderefiningeresults;e5)eCommunicatingeinsightseande6)eTr
ackingeoutcomes.eTheecycleestartsewitheaequestioneandetheneidentifyingedataeandetesteplanethatem
ighteaddressethatequestion.eTheeresultseofetheedataeanalysiseareecommunicatedeandetrackedewhic
hemayeleadetoeadditional,epossiblyemoreerefinedequestionsethatethenerestartetheecycle.
8. Dataeanalysiseisemosteeffectiveewheneaequestioneiseidentifiedethateneedsetoebeeaddressed.eThat
ewillefocusetheeanalysiseonewhichedataeandewhichetestemethodemightebeemosteeffectiveeineaddr
essingeoreansweringetheequestion.
9. Masteringetheedataerequireseoneetoeknowewhatedataeiseavailableeandewhethereitemightebeeableetoe
helpeaddressetheebusinesseproblem.eWeeneedetoeknoweeverythingeaboutetheedata,eincludingehowe
toeaccesseit,eitseavailability,ehowereliableeiteise(ifethereeareeerrors),eandewhatetimeeperiodseitecoverse
toemakeesureeitecoincidesewithetheetimingeofeourebusinesseproblem,eetc.
10. Facebookeuseselinkepredictionetoepredicteaerelationshipebetweenetwoepeopleewheneitesuggestsepe
opleethateoneelikelyeknowsedueetoesimilareotherefriends,eextendedefamily,ehigheschools,ecollegeeor
eworkelocations,eetc.
11. Whileesamplingeiseuseful,eiteisestillejustethat,esampling.eByelookingeatealleofetheetransactionseandetes
tingethemeineaewayethatewillehighlightetheeonesethateareetheebiggestedollareitems,eoreareemosteunu
sual,ethatewillealloweauditorsetoefocuseonespecificeitemsethatemightebeeofematerialesignificance.
12. Thereeareeseveralecorrecteanswers.eOneedataeapproachemightebeeregressioneanalysisewhere,egiven
eaebalanceeofetotaleaccountsereceivableeheldebyeaefirm,ehowelongeitehasebeeneoutstanding,eifetheyeh
aveepaidedebtseinetheepasteallewillehelpepredictetheeappropriateeleveleofeallowanceeforedoubtfuleac
countseforebadedebts.
13. TheeDebt-to-
IncomeeratioemightesuggestetoeLendingClubethatetheepersoneaskingeforetheeloanewasesimplyeaskin
geforetooebigeofeaeloaneandetheyewouldehaveelittleeabilityetoerepayeit.eTheeloweretheecreditescore,et
heelesselikelyetheepotentialeborrowerewouldebeeableetoerepayetheeloan.
14. ThereeareemanyeotherepotentialepredictorseofewhetheretheeLendingClubewouldepayeaeloan.eHereea
reeaefewepossibilities:eWhateotheredebtedoetheyehave?eHowemucheisetheiredisposableeincome?eDo
4
