Solution Manual for Data Analytics for Accounting, 3rd Edition by Richardson - Chapters 1-9 (Full Solutions)

Richardson,VTeeter,VTerrellV–
VDataVAnalyticsVforVAccounting,V3e




SOLUTIONV MANUALV FOR
DataVAnalyticsVforVAccounting,V3rdVEditionVRichardsonVChapt
erV1-9
SolutionsVManualV–VChapterV1
SolutionsVtoVMultipleVChoiceVQuestions

1. (LOV1-1)VBigVDataVisVoftenVdescribedVbyVtheVfourVVs,Vor

a. volume,Vvelocity,Vveracity,VandVvariability.
b. volume,Vvelocity,Vveracity,VandVvariety.
c. volume,Vvolatility,Vveracity,VandVvariability.
d. variability,Vvelocity,Vveracity,VandVvariety.

Answer:Vb

2. LOV1-
4)VWhichVdataVapproachVattemptsVtoVassignVeachVunitVinVaVpopulationVintoVaVsmallVsetVo
fVclassesV(orVgroups)VwhereVtheVunitVbestVfits?

a. Regression
b. SimilarityVmatching
c. Co-occurrenceVgrouping
d. Classification

Answer:Vd

3. (LOV1-
4)VWhichVdataVapproachVattemptsVtoVidentifyVsimilarVindividualsVbasedVonVdataVknownVa
boutVthem?

a. Classification
b. Regression
c. SimilarityVmatching
d. DataVreduction

Answer:Vc

4. (LOV1-4)VWhichVdataVapproachVattemptsVtoVpredictVconnectionsVbetweenVtwoVdataVitems?

a. Profiling
b. Classification
c. LinkVprediction
d. Regression


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, Richardson,VTeeter,VTerrellV–
VDataVAnalyticsVforVAccounting,V3e



Answer:Vc


5. (LOV1-
6)VWhichVofVtheseVtermsVisVdefinedVasVbeingVaVcentralVrepositoryVofVdescriptionsVforVallVof
VtheVdataVattributesVofVtheVdataset?


a. BigVData
b. DataVwarehouse
c. DataVdictionary
d. DataVAnalytics

Answer:Vc

6. (LOV1-5)VWhichVskillsVwereVnotVemphasizedVthatVanalytic-mindedVaccountantsVshouldVhave?

a. DevelopedVanVanalyticsVmindset
b. DataVscrubbingVandVdataVpreparation
c. ClassificationVofVtestVapproaches
d. StatisticalVdataVanalysisVcompetency

Answer:Vc

7. (LOV1-5)VInVwhichVareasVwereVskillsVnotVemphasizedVforVanalytic-mindedVaccountants?

a. DataVquality
b. DescriptiveVdataVanalysis
c. DataVvisualizationVandVdataVreporting
d. DataVandVsystemsVanalysisVandVdesign

Answer:Vd

8. (LOV1-4)VTheVIMPACTVcycleVincludesVallVexceptVtheVfollowingVsteps:

a. performVtestVplan.
b. visualizeVtheVdata.
c. masterVtheVdata.
d. trackVoutcomes.

Answer:Vb

9. (LOV1-4)VTheVIMPACTVcycleVspecificallyVincludesVallVexceptVtheVfollowingVsteps:

a. dataVpreparation.
b. communicateVinsights.
c. addressVandVrefineVresults.
d. performVtestVplan.



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, Richardson,VTeeter,VTerrellV–
VDataVAnalyticsVforVAccounting,V3e



Answer:Va


10. LOV1-
1)VByVtheVyearV2024,VtheVvolumeVofVdataVcreated,Vcaptured,Vcopied,VandVconsumedV
worldwideVwillVbeV149V .

a. zettabytes
b. petabytes
c. exabytes
d. yottabytes

Answer:Va


SolutionsVtoVDiscussionVandVAnalysisVQuestions

1. TheVaccountingVfunctionVisVoneVofVbeingVanVinformationVprovider.V ToVtheVextentVthatVdataV
isVavailableVtoVaddressVaccountingVquestions,VbeVtheyVtax,Vmanagerial,VauditVorVfinancialVqu
estions.VWithVsuchVrichVavailableVdata,VandVsoftwareVtoolsVtoVprepareVandVanalyzeVtheVdata,
VdataVanalyticsVwillVcontinueVtoVbeVanVimportantVtoolVforVaccountantsVtoVuse.


2. DataVanalyticsVisVdefinedVasVtheVprocessVofVevaluatingVdataVwithVtheVpurposeVofVdrawing
VconclusionsVtoVaddressVbusinessVquestions.VIndeed,VeffectiveVDataVAnalyticsVprovidesVaV
wayVtoVsearchVthroughVlargeVstructuredVandVunstructuredVdataVtoVidentifyVunknownVpatt
ernsVorVrelationships.

AVuniversityVmightVlearnVfromVtheVanalyzingVtheVdemographicsVofVitsVcurrentVsetVofVstuden
tsVinVorderVtoVattractVitsVfutureVstudentVrecruits.VDidVtheyVcomeVfromVcitiesVorVhighVschool
sVthatVwereVcloseVby?VWereVtheirVparentsValumniVofVtheVuniversity?VDidVtheyVscoreVhighVo
nVcertainVpartsVofVtheVACT?VWereVthoseVofferedVaVscholarshipVmoreVlikelyVtoVattend,Vetc.?
VWasVsocialVmediaVeffectiveVinVattractingVnew,VpotentiallyVstrongerVstudents?VByVanalyzingV
thisVtypeVofVdata,VpreviouslyVunknownVpatternsVwillVemergeVthatVwillVmakeVrecruitingVstud
entsVmoreVeffective.

3. ThereVareVmanyVpotentialVanswers.V ForVexample,VMonsantoVmayVuseVmathematicalVandVst
atisticalVmodelsVtoVplotVoutVtheVbestVtimesVtoVplantVbothVmaleVandVfemaleVplantsVandVwh
ereVtoVplantVthemVtoVmaximizeVyield.V(https://www.cio.com/article/3221621/analytics/6-
data-Vanalytics-success-stories-an-inside-look.html#tk.cio_rs)

4. ThereVareVmanyVpotentialVanswers.VDataVanalyticsVgivesVbothVinternalVandVexternalVauditor
sVadditionalVtoolsVtoVexamineVeveryVaccountingVtransactionVandVassessVforVcomplianceVwith
VGAAP.VTheVauditVprocessVisVchangingVfromVaVtraditionalVprocessVtowardVaVmoreVautomate
dVone,VwhichVwillVallowVauditVprofessionalsVtoVfocusVmoreVonVtheVlogicVandVrationaleVbehi
ndVdataVqueriesVandVlessVonVtheVgatheringVofVtheVactualVdata.VNoVlongerVwillVtheyVbeVsim
plyVcheckingVforVerrors,VmaterialVmisstatements,Vfraud,VandVriskVinVfinancialVstatementsVorV
merelyVbeVreportingVtheirVfindingsVatVtheVendVofVtheVengagement.VInstead,VauditVprofessio
nalsVwillVnowVbeVcollectingVand


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, Richardson,VTeeter,VTerrellV–
VDataVAnalyticsVforVAccounting,V3e



analyzingVtheVcompany’sVdataVsimilarVtoVtheVwayVaVbusinessVanalystVwouldVhelpVmanagem
entVmakeVbetterVbusinessVdecisions.V InVthisVway,VdataVanalyticsVoffersVvalueVtoVtheVauditVf
unction.

5. ThereVareVmanyVpotentialVanswers.VForVexample,VdataVanalyticsVassociatedVwithVfinancialV
reportingVmayVhelpVaccountantsVdetermineVifVanyVofVtheirVinventoryVobsolete?VItVmayVals
oVhelpVtheVcompanyVbenchmarkVonVtheVfinancialVstatementsVandVfinancialVreportingVofVo
therVsimilarVcompaniesVandVunderstandVtheirVaccountingVpracticesVtoVhelpVinferVtheirVow
n.

6. ManagementVaccountantsVaddressVtheVinformationVneedsVofVmanagement.V TheyVwillVofte
nVseeVwhatVquestionsVmanagementVhas,VfindVapplicableVdataVtoVaddressVthoseVquestions,V
conductVanalysisVofVtheVdata,VandVreportVtheVresultsVtoVmanagementVtoVhelpVthemVmake
Vdata-
drivenVdecisions.V ThisVisVconsistentVwithVtheVdataVanalyticsVprocessVandVtheVIMPACTVmod
el.

7. TheVIMPACTVcycleVsuggestsVanVorderVofV1)VIdentifyingVtheVQuestions;V2)VMasteringVtheVDat
a;V3)VPerformingVtheVtestVplan;V4)VAddressingVandVrefiningVresults;V5)VCommunicatingVinsig
htsVandV6)VTrackingVoutcomes.VTheVcycleVstartsVwithVaVquestionVandVthenVidentifyingVdataV
andVtestVplanVthatVmightVaddressVthatVquestion.VTheVresultsVofVtheVdataVanalysisVareVcom
municatedVandVtrackedVwhichVmayVleadVtoVadditional,VpossiblyVmoreVrefinedVquestionsVtha
tVthenVrestartVtheVcycle.

8. DataVanalysisVisVmostVeffectiveVwhenVaVquestionVisVidentifiedVthatVneedsVtoVbeVaddresse
d.VThatVwillVfocusVtheVanalysisVonVwhichVdataVandVwhichVtestVmethodVmightVbeVmostVef
fectiveVinVaddressingVorVansweringVtheVquestion.

9. MasteringVtheVdataVrequiresVoneVtoVknowVwhatVdataVisVavailableVandVwhetherVitVmightVbeV
ableVtoVhelpVaddressVtheVbusinessVproblem.VWeVneedVtoVknowVeverythingVaboutVtheVdata,V
includingVhowVtoVaccessVit,VitsVavailability,VhowVreliableVitVisV(ifVthereVareVerrors),VandVwhat
VtimeVperiodsVitVcoversVtoVmakeVsureVitVcoincidesVwithVtheVtimingVofVourVbusinessVproblem
,Vetc.

10. FacebookVusesVlinkVpredictionVtoVpredictVaVrelationshipVbetweenVtwoVpeopleVwhenVitVsugg
estsVpeopleVthatVoneVlikelyVknowsVdueVtoVsimilarVotherVfriends,VextendedVfamily,VhighVscho
ols,VcollegeVorVworkVlocations,Vetc.

11. WhileVsamplingVisVuseful,VitVisVstillVjustVthat,Vsampling.VByVlookingVatVallVofVtheVtransactions
VandVtestingVthemVinVaVwayVthatVwillVhighlightVtheVonesVthatVareVtheVbiggestVdollarVitems,V
orVareVmostVunusual,VthatVwillVallowVauditorsVtoVfocusVonVspecificVitemsVthatVmightVbeVofV
materialVsignificance.

12. ThereVareVseveralVcorrectVanswers.VOneVdataVapproachVmightVbeVregressionVanalysisVwhere,
VgivenVaVbalanceVofVtotalVaccountsVreceivableVheldVbyVaVfirm,VhowVlongVitVhasVbeenVoutsta
nding,VifVtheyVhaveVpaidVdebtsVinVtheVpastVallVwillVhelpVpredictVtheVappropriateVlevelVofVallo
wanceVforVdoubtfulVaccountsVforVbadVdebts.

13. TheVDebt-to-
IncomeVratioVmightVsuggestVtoVLendingClubVthatVtheVpersonVaskingVforVtheVloanVwasVsimpl
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