,ACCESS Test Bank for Data Analytics for Accounting 3rd Edition
n n n n n n n n n
Richardson, Teeter, T erRreil lc–hDaartadAsnoan
n n l y ti c s for Accounting, 3e n n n
Solutions Manual – Chapter 1
n n n n
SolutionsntonMultiple nChoice nQuestions
1. (LO n 1-1)n Bign Datan isn oftenn described n byn then fourn Vs,n or
a. volume,n velocity,n veracity,n and n variability.
b. volume,n velocity,n veracity,n and n variety.
c. volume,n volatility,n veracity,n and n variability.
d. variability,n velocity,n veracity,n and n variety.
Answer:nb
2. LOn 1-
4)n Whichn datan approachn attemptsntonassignn eachnunit ninn anpopulationn inton ansmalln set n of n classesn (o
rn groups)n wheren then unit n best n fits?
a. Regression
b. Similarityn matching
c. Co-occurrencen grouping
d. Classification
Answer:nd
3. (LO n 1-
4)n Whichn datan approachn attemptsnton identifyn similarn individualsn based nonn datan knownn about n the
m?
a. Classification
b. Regression
c. Similarityn matching
d. Datan reduction
Answer:nc
4. (LO n 1-4)n Whichn datan approachn attemptsn ton predict n connectionsn betweenn twon datan items?
a. Profiling
b. Classification
c. Linkn prediction
d. Regression
Answer:nc
5. (LO n 1-
6)n Whichn of n thesen termsn isn definednasn beingnan centraln repositoryn of ndescriptionsn forn alln ofnthendatanatt
ributesn of n then dataset?
©nMcGrawnHillnLLC.nAllnrightsnreserved.nNonreproductionnorndistribution nwithout nthe npriornwrittennconsen
tnof
McGrawnHill nLLC.
mynursytest.store
,ACCESS Test Bank for Data Analytics for Accounting 3rd Edition
n n n n n n n n n
Richardson, Teeter, T erRreil lc–hDaartadAsnoan
n n l y ti c s for Accounting, 3e n n n
a. Bign Data
b. Datan warehouse
c. Datan dictionary
d. Datan Analytics
Answer:nc
6. (LO n 1-5)n Whichn skillsn weren notn emphasized n that n analytic-minded n accountantsn should n have?
a. Developed n ann analyticsn mindset
b. Datan scrubbingn and n datan preparation
c. Classificationn of n test n approaches
d. Statisticaln datan analysisn competency
Answer:nc
7. (LO n 1-5)n Inn whichn areasn weren skillsn notn emphasized n forn analytic-minded n accountants?
a. Datan quality
b. Descriptiven datan analysis
c. Datan visualizationn and n datan reporting
d. Datan and n systemsn analysisn and n design
Answer:nd
8. (LO n 1-4)n Then IMPACTn cyclen includesn alln exceptn then followingn steps:
a. performn test n plan.
b. visualizen then data.
c. mastern then data.
d. trackn outcomes.
Answer:nb
9. (LO n 1-4)n Then IMPACTn cyclen specificallyn includesn alln exceptn then followingn steps:
a. datan preparation.
b. communicaten insights.
c. addressn and n refinen results.
d. performn test n plan.
Answer:na
10. LOn 1-
1)n Byn then yearn 2024,nthen volumen ofndatancreated,ncaptured,ncopied,nandnconsumed nworldwide
n willn ben 149n .
©nMcGrawnHillnLLC.nAllnrightsnreserved.nNonreproductionnorndistribution nwithout nthe npriornwrittennconsen
tnof
McGrawnHill nLLC.
mynursytest.store
, ACCESS Test Bank for Data Analytics for Accounting 3rd Edition
n n n n n n n n n
Richardson, Teeter, T erRreil lc–hDaartadAsnoan
n n l y ti c s for Accounting, 3e n n n
a. zettabytes
b. petabytes
c. exabytes
d. yottabytes
Answer:na
SolutionsntonDiscussionnandnAnalysisnQuestions
1. The naccountingnfunctionnisnonenofnbeingnanninformationnprovider.n Tonthe nextentnthatndatanisnavai
lable ntonaddressnaccountingnquestions,nbentheyntax,nmanagerial,nauditnornfinancialnquestions.nWit
hnsuchnrichnavailable ndata,nandnsoftware ntoolsntonpreparenandnanalyzenthendata,ndatananalyticsnwil
lncontinue ntonbe nannimportantntoolnfornaccountantsntonuse.
2. Datananalyticsnisndefinednasnthenprocessnof nevaluatingndatanwithnthe npurposenofndrawingnconcl
usionsntonaddressnbusinessnquestions.nIndeed,neffectivenDatanAnalyticsnprovidesnanwayntonsear
chnthroughnlarge nstructurednandnunstructuredndatantonidentifynunknownnpatternsnornrelations
hips.
Anuniversitynmightnlearnnfromnthe nanalyzingnthendemographicsnof nitsncurrentnsetnofnstudentsninno
rderntonattractnitsnfuturenstudentnrecruits.nDidntheyncome nfromncitiesnornhighnschoolsnthatnwerencl
ose nby?nWere ntheirnparentsnalumninof nthenuniversity?nDidntheynscorenhighnonncertainnpartsnof nth
e nACT?nWerenthosenofferednanscholarshipnmore nlikelyntonattend,netc.?nWasnsocialnmedianeffectiv
e ninnattractingnnew,npotentiallynstrongernstudents?nBynanalyzingnthisntypenofndata,npreviouslynun
knownnpatternsnwillnemerge nthatnwillnmake nrecruitingnstudentsnmore neffective.
3. There nare nmanynpotentialnanswers.n Fornexample,nMonsantonmaynuse nmathematicalnandnstatisti
calnmodelsntonplotnoutnthe nbestntimesntonplantnbothnmale nandnfemale nplantsnandnwherentonplantn
themntonmaximize nyield.n(https://www.cio.com/article/3221621/analytics/6-data-nanalytics-
success-stories-an-inside-look.html#tk.cio_rs)
4. There nare nmanynpotentialnanswers.nDatananalyticsngivesnbothninternalnandnexternalnauditorsnadd
itionalntoolsntonexamineneverynaccountingntransactionnandnassessnforncompliance nwithnGAAP.nTh
e nauditnprocessnisnchangingnfromnantraditionalnprocessntowardnanmore nautomatednone,nwhichnwi
llnallownauditnprofessionalsntonfocusnmore nonnthenlogicnandnrationale nbehindndatanqueriesnandnle
ssnonnthe ngatheringnofnthenactualndata.nNonlongernwillntheynbensimplyncheckingnfornerrors,nmateri
alnmisstatements, nfraud,nandnriskninnfinancialnstatementsnornmerelynbenreportingntheirnfindingsna
tnthe nendnofnthe nengagement.nInstead,nauditnprofessionalsnwillnnownbe ncollectingnandnanalyzingn
the ncompany’sndatansimilarntonthe nwaynanbusinessnanalystnwouldnhelpnmanagementnmakenbette
rnbusinessndecisions.n Innthisnway,ndatananalyticsnoffersnvalue ntonthe nauditnfunction.
5. There nare nmanynpotentialnanswers.nFornexample,ndatananalyticsnassociatednwithnfinancialnrepo
rtingnmaynhelpnaccountantsndetermine nifnanynofntheirninventorynobsolete?nItnmaynalsonhelpnthen
companynbenchmarknonnthenfinancialnstatementsnandnfinancialnreportingnofnothernsimilarncom
paniesnandnunderstandntheirnaccountingnpracticesntonhelpninferntheirnown.
©nMcGrawnHillnLLC.nAllnrightsnreserved.nNonreproductionnorndistribution nwithout nthe npriornwrittennconsen
tnof
McGrawnHill nLLC.
mynursytest.store
n n n n n n n n n
Richardson, Teeter, T erRreil lc–hDaartadAsnoan
n n l y ti c s for Accounting, 3e n n n
Solutions Manual – Chapter 1
n n n n
SolutionsntonMultiple nChoice nQuestions
1. (LO n 1-1)n Bign Datan isn oftenn described n byn then fourn Vs,n or
a. volume,n velocity,n veracity,n and n variability.
b. volume,n velocity,n veracity,n and n variety.
c. volume,n volatility,n veracity,n and n variability.
d. variability,n velocity,n veracity,n and n variety.
Answer:nb
2. LOn 1-
4)n Whichn datan approachn attemptsntonassignn eachnunit ninn anpopulationn inton ansmalln set n of n classesn (o
rn groups)n wheren then unit n best n fits?
a. Regression
b. Similarityn matching
c. Co-occurrencen grouping
d. Classification
Answer:nd
3. (LO n 1-
4)n Whichn datan approachn attemptsnton identifyn similarn individualsn based nonn datan knownn about n the
m?
a. Classification
b. Regression
c. Similarityn matching
d. Datan reduction
Answer:nc
4. (LO n 1-4)n Whichn datan approachn attemptsn ton predict n connectionsn betweenn twon datan items?
a. Profiling
b. Classification
c. Linkn prediction
d. Regression
Answer:nc
5. (LO n 1-
6)n Whichn of n thesen termsn isn definednasn beingnan centraln repositoryn of ndescriptionsn forn alln ofnthendatanatt
ributesn of n then dataset?
©nMcGrawnHillnLLC.nAllnrightsnreserved.nNonreproductionnorndistribution nwithout nthe npriornwrittennconsen
tnof
McGrawnHill nLLC.
mynursytest.store
,ACCESS Test Bank for Data Analytics for Accounting 3rd Edition
n n n n n n n n n
Richardson, Teeter, T erRreil lc–hDaartadAsnoan
n n l y ti c s for Accounting, 3e n n n
a. Bign Data
b. Datan warehouse
c. Datan dictionary
d. Datan Analytics
Answer:nc
6. (LO n 1-5)n Whichn skillsn weren notn emphasized n that n analytic-minded n accountantsn should n have?
a. Developed n ann analyticsn mindset
b. Datan scrubbingn and n datan preparation
c. Classificationn of n test n approaches
d. Statisticaln datan analysisn competency
Answer:nc
7. (LO n 1-5)n Inn whichn areasn weren skillsn notn emphasized n forn analytic-minded n accountants?
a. Datan quality
b. Descriptiven datan analysis
c. Datan visualizationn and n datan reporting
d. Datan and n systemsn analysisn and n design
Answer:nd
8. (LO n 1-4)n Then IMPACTn cyclen includesn alln exceptn then followingn steps:
a. performn test n plan.
b. visualizen then data.
c. mastern then data.
d. trackn outcomes.
Answer:nb
9. (LO n 1-4)n Then IMPACTn cyclen specificallyn includesn alln exceptn then followingn steps:
a. datan preparation.
b. communicaten insights.
c. addressn and n refinen results.
d. performn test n plan.
Answer:na
10. LOn 1-
1)n Byn then yearn 2024,nthen volumen ofndatancreated,ncaptured,ncopied,nandnconsumed nworldwide
n willn ben 149n .
©nMcGrawnHillnLLC.nAllnrightsnreserved.nNonreproductionnorndistribution nwithout nthe npriornwrittennconsen
tnof
McGrawnHill nLLC.
mynursytest.store
, ACCESS Test Bank for Data Analytics for Accounting 3rd Edition
n n n n n n n n n
Richardson, Teeter, T erRreil lc–hDaartadAsnoan
n n l y ti c s for Accounting, 3e n n n
a. zettabytes
b. petabytes
c. exabytes
d. yottabytes
Answer:na
SolutionsntonDiscussionnandnAnalysisnQuestions
1. The naccountingnfunctionnisnonenofnbeingnanninformationnprovider.n Tonthe nextentnthatndatanisnavai
lable ntonaddressnaccountingnquestions,nbentheyntax,nmanagerial,nauditnornfinancialnquestions.nWit
hnsuchnrichnavailable ndata,nandnsoftware ntoolsntonpreparenandnanalyzenthendata,ndatananalyticsnwil
lncontinue ntonbe nannimportantntoolnfornaccountantsntonuse.
2. Datananalyticsnisndefinednasnthenprocessnof nevaluatingndatanwithnthe npurposenofndrawingnconcl
usionsntonaddressnbusinessnquestions.nIndeed,neffectivenDatanAnalyticsnprovidesnanwayntonsear
chnthroughnlarge nstructurednandnunstructuredndatantonidentifynunknownnpatternsnornrelations
hips.
Anuniversitynmightnlearnnfromnthe nanalyzingnthendemographicsnof nitsncurrentnsetnofnstudentsninno
rderntonattractnitsnfuturenstudentnrecruits.nDidntheyncome nfromncitiesnornhighnschoolsnthatnwerencl
ose nby?nWere ntheirnparentsnalumninof nthenuniversity?nDidntheynscorenhighnonncertainnpartsnof nth
e nACT?nWerenthosenofferednanscholarshipnmore nlikelyntonattend,netc.?nWasnsocialnmedianeffectiv
e ninnattractingnnew,npotentiallynstrongernstudents?nBynanalyzingnthisntypenofndata,npreviouslynun
knownnpatternsnwillnemerge nthatnwillnmake nrecruitingnstudentsnmore neffective.
3. There nare nmanynpotentialnanswers.n Fornexample,nMonsantonmaynuse nmathematicalnandnstatisti
calnmodelsntonplotnoutnthe nbestntimesntonplantnbothnmale nandnfemale nplantsnandnwherentonplantn
themntonmaximize nyield.n(https://www.cio.com/article/3221621/analytics/6-data-nanalytics-
success-stories-an-inside-look.html#tk.cio_rs)
4. There nare nmanynpotentialnanswers.nDatananalyticsngivesnbothninternalnandnexternalnauditorsnadd
itionalntoolsntonexamineneverynaccountingntransactionnandnassessnforncompliance nwithnGAAP.nTh
e nauditnprocessnisnchangingnfromnantraditionalnprocessntowardnanmore nautomatednone,nwhichnwi
llnallownauditnprofessionalsntonfocusnmore nonnthenlogicnandnrationale nbehindndatanqueriesnandnle
ssnonnthe ngatheringnofnthenactualndata.nNonlongernwillntheynbensimplyncheckingnfornerrors,nmateri
alnmisstatements, nfraud,nandnriskninnfinancialnstatementsnornmerelynbenreportingntheirnfindingsna
tnthe nendnofnthe nengagement.nInstead,nauditnprofessionalsnwillnnownbe ncollectingnandnanalyzingn
the ncompany’sndatansimilarntonthe nwaynanbusinessnanalystnwouldnhelpnmanagementnmakenbette
rnbusinessndecisions.n Innthisnway,ndatananalyticsnoffersnvalue ntonthe nauditnfunction.
5. There nare nmanynpotentialnanswers.nFornexample,ndatananalyticsnassociatednwithnfinancialnrepo
rtingnmaynhelpnaccountantsndetermine nifnanynofntheirninventorynobsolete?nItnmaynalsonhelpnthen
companynbenchmarknonnthenfinancialnstatementsnandnfinancialnreportingnofnothernsimilarncom
paniesnandnunderstandntheirnaccountingnpracticesntonhelpninferntheirnown.
©nMcGrawnHillnLLC.nAllnrightsnreserved.nNonreproductionnorndistribution nwithout nthe npriornwrittennconsen
tnof
McGrawnHill nLLC.
mynursytest.store
