TEST BANK FOR nd nd
Data Analytics for Accounting, 3rd Edition Richardson
nd nd nd nd nd nd
Chapter 1-9
nd nd
Answers are at the End of Each Chapter
nd nd nd nd nd nd nd
Chapter 01: nd
Student name: nd
1) Data ndanalytics ndis ndthe ndprocess ndof ndevaluating nddata ndwith ndthe ndpurpose ndof nddrawing
conclusions ndto ndaddress ndbusiness ndquestions.
nd
⊚ n d true
⊚ n d false
2) The ndprocess ndof nddata ndanalytics ndaims ndto ndtransform ndraw ndinformation ndinto nddata ndto ndcreate
nd value.
⊚ n d true
⊚ n d false
3) Data ndanalytics ndhas ndthe ndpotential ndto ndtransform ndthe ndmanner ndin ndwhich ndcompanies
run ndtheir ndbusinesses, ndhowever ndit ndis ndnot ndpractical ndin ndthe ndnear ndfuture.
nd
⊚ n d true
⊚ n d false
4) Auditors ndcan nduse ndsocial ndmedia ndto ndhear ndwhat ndcustomers ndare ndsaying ndabout nda
nd company ndand ndcompare ndthis ndto ndinventory ndobsolescence ndand ndother ndestimates.
⊚ n d true
⊚ n d false
5) Data ndanalytics ndallows ndauditors ndto ndglean ndinsights ndthat ndare ndbeneficial ndto ndthe ndclient,
without ndbreeching ndindependence.
nd
⊚ n d true
⊚ n d false
,6) The ndpredictive ndanalytics ndis ndan ndimportant ndaspect ndof nddata ndanalytics ndfor ndauditors,
but ndis ndnot ndapplicable ndfor ndtax ndaccountants.
nd
⊚ n d true
⊚ n d false
7) The ndI ndin ndIMPACT ndCycle ndrepresents ndIdentify ndthe ndQuestion.
⊚ n d true
⊚ n d false
8) The ndM ndin ndIMPACT ndCycle ndrepresents ndMaster ndthe ndData.
⊚ n d true
⊚ n d false
9) The ndP ndin ndIMPACT ndCycle ndrepresents ndPredict ndthe ndResults.
⊚ n d true
⊚ n d false
10) The ndA ndin ndIMPACT ndCycle ndrepresents ndAnalyze ndthe ndData.
⊚ n d true
⊚ n d false
11) The ndC ndin ndIMPACT ndCycle ndrepresents ndContinuously ndTrack.
⊚ n d true
⊚ n d false
12) The ndT ndin ndIMPACT ndCycle ndrepresents ndTrack ndOutcomes.
⊚ n d true
⊚ n d false
,13) The ndIMPACT ndcycle ndis nditerative, ndas ndinsights ndare ndgained, ndoutcomes ndare ndtracked,
nd and ndnew ndquestions ndare ndidentified.
⊚ n d true
⊚ n d false
14) Data ndanalysis ndthrough nddata ndmanipulation ndis ndperforming ndbasic ndanalysis ndto
nd understand ndthe ndquality ndof ndthe ndunderlying nddata ndand ndits ndability ndto ndaddress ndthe
nd business ndquestion.
⊚ n d true
⊚ n d false
15) To ndbe ndproficient ndin nddata ndanalysis, ndaccountants ndneed ndto ndbecome nddata ndscientists.
⊚ n d true
⊚ n d false
16) By nddeveloping ndan ndanalytics ndmindset, ndaccountants ndwill ndbe ndable ndto ndrecognize ndwhen
nd and ndhow nddata ndanalytics ndcan ndaddress ndbusiness ndquestions.
⊚ n d true
⊚ n d false
17) While ndit ndis ndimportant ndfor ndaccountants ndto ndclearly ndarticulate ndthe ndbusiness ndproblem,
nddrawing ndappropriate ndconclusions, ndbased ndon ndthe nddata, ndshould ndbe ndleft ndto
ndstatisticians.
⊚ n d true
⊚ n d false
18) Analytic-minded ndaccountants ndshould ndreport ndresults ndof ndanalysis ndin ndan ndaccessible ndway
to ndeach ndvaried nddecision ndmaker ndand ndtheir ndspecific ndneeds.
nd
⊚ n d true
⊚ n d false
, 19) With nda ndgoal ndto ndgive ndorganizations ndthe ndinformation ndthey ndneed ndto ndmake ndsound
ndand ndtimely ndbusiness nddecisions, nddata ndanalytics ndoften ndinvolves ndall ndof ndthe
ndfollowing ndexcept:
A) technologies.
B) statistics.
C) strategies.
D) databases.
20) Patterns nddiscovered ndfrom nd enable ndbusinesses ndto ndidentify ndopportunities ndand
risks ndand ndbetter ndplan ndfor nd .
nd
A) past ndarchives; ndthe ndfuture
B) current nddata; ndthe ndfuture
C) current nddata; ndtoday
D) past ndarchives; ndtoday
21) Which ndof ndthe ndfollowing ndbest nddescribes ndthe ndgoal ndof nddescriptive nddata ndanalysis:
A) recognize ndwhat ndis ndmeant ndby nddata ndquality, ndbe ndit ndcompleteness, ndreliability ndor
validity
nd
B) perform ndbasic ndanalysis ndto ndunderstand ndthe ndquality ndof ndthe ndunderlying nddata ndand ndits
ndability ndto ndaddress ndthe ndbusiness ndquestion
C) demonstrate ndability ndto ndsort, ndrearrange, ndmerge, ndand ndreconfigure nddata ndin nda
ndmanner ndthat ndallows ndenhanced ndanalysis
D) comprehend ndthe ndprocess ndneeded ndto ndclean ndand ndprepare ndthe nddata ndbefore ndanalysis
22) Which ndof ndthe ndfollowing ndMicrosoft ndsoftware ndtool ndspecializes ndin nddata ndjoining?
A) Excel
B) Power ndQuery
C) Power ndBI
D) Power ndAutomate
23) Which ndof ndthe ndfollowing ndMicrosoft ndsoftware ndtools ndspecializes ndin ndcreating nddashboards?
A) Excel
B) Power ndQuery
C) Power ndBI
D) Power ndAutomate
Data Analytics for Accounting, 3rd Edition Richardson
nd nd nd nd nd nd
Chapter 1-9
nd nd
Answers are at the End of Each Chapter
nd nd nd nd nd nd nd
Chapter 01: nd
Student name: nd
1) Data ndanalytics ndis ndthe ndprocess ndof ndevaluating nddata ndwith ndthe ndpurpose ndof nddrawing
conclusions ndto ndaddress ndbusiness ndquestions.
nd
⊚ n d true
⊚ n d false
2) The ndprocess ndof nddata ndanalytics ndaims ndto ndtransform ndraw ndinformation ndinto nddata ndto ndcreate
nd value.
⊚ n d true
⊚ n d false
3) Data ndanalytics ndhas ndthe ndpotential ndto ndtransform ndthe ndmanner ndin ndwhich ndcompanies
run ndtheir ndbusinesses, ndhowever ndit ndis ndnot ndpractical ndin ndthe ndnear ndfuture.
nd
⊚ n d true
⊚ n d false
4) Auditors ndcan nduse ndsocial ndmedia ndto ndhear ndwhat ndcustomers ndare ndsaying ndabout nda
nd company ndand ndcompare ndthis ndto ndinventory ndobsolescence ndand ndother ndestimates.
⊚ n d true
⊚ n d false
5) Data ndanalytics ndallows ndauditors ndto ndglean ndinsights ndthat ndare ndbeneficial ndto ndthe ndclient,
without ndbreeching ndindependence.
nd
⊚ n d true
⊚ n d false
,6) The ndpredictive ndanalytics ndis ndan ndimportant ndaspect ndof nddata ndanalytics ndfor ndauditors,
but ndis ndnot ndapplicable ndfor ndtax ndaccountants.
nd
⊚ n d true
⊚ n d false
7) The ndI ndin ndIMPACT ndCycle ndrepresents ndIdentify ndthe ndQuestion.
⊚ n d true
⊚ n d false
8) The ndM ndin ndIMPACT ndCycle ndrepresents ndMaster ndthe ndData.
⊚ n d true
⊚ n d false
9) The ndP ndin ndIMPACT ndCycle ndrepresents ndPredict ndthe ndResults.
⊚ n d true
⊚ n d false
10) The ndA ndin ndIMPACT ndCycle ndrepresents ndAnalyze ndthe ndData.
⊚ n d true
⊚ n d false
11) The ndC ndin ndIMPACT ndCycle ndrepresents ndContinuously ndTrack.
⊚ n d true
⊚ n d false
12) The ndT ndin ndIMPACT ndCycle ndrepresents ndTrack ndOutcomes.
⊚ n d true
⊚ n d false
,13) The ndIMPACT ndcycle ndis nditerative, ndas ndinsights ndare ndgained, ndoutcomes ndare ndtracked,
nd and ndnew ndquestions ndare ndidentified.
⊚ n d true
⊚ n d false
14) Data ndanalysis ndthrough nddata ndmanipulation ndis ndperforming ndbasic ndanalysis ndto
nd understand ndthe ndquality ndof ndthe ndunderlying nddata ndand ndits ndability ndto ndaddress ndthe
nd business ndquestion.
⊚ n d true
⊚ n d false
15) To ndbe ndproficient ndin nddata ndanalysis, ndaccountants ndneed ndto ndbecome nddata ndscientists.
⊚ n d true
⊚ n d false
16) By nddeveloping ndan ndanalytics ndmindset, ndaccountants ndwill ndbe ndable ndto ndrecognize ndwhen
nd and ndhow nddata ndanalytics ndcan ndaddress ndbusiness ndquestions.
⊚ n d true
⊚ n d false
17) While ndit ndis ndimportant ndfor ndaccountants ndto ndclearly ndarticulate ndthe ndbusiness ndproblem,
nddrawing ndappropriate ndconclusions, ndbased ndon ndthe nddata, ndshould ndbe ndleft ndto
ndstatisticians.
⊚ n d true
⊚ n d false
18) Analytic-minded ndaccountants ndshould ndreport ndresults ndof ndanalysis ndin ndan ndaccessible ndway
to ndeach ndvaried nddecision ndmaker ndand ndtheir ndspecific ndneeds.
nd
⊚ n d true
⊚ n d false
, 19) With nda ndgoal ndto ndgive ndorganizations ndthe ndinformation ndthey ndneed ndto ndmake ndsound
ndand ndtimely ndbusiness nddecisions, nddata ndanalytics ndoften ndinvolves ndall ndof ndthe
ndfollowing ndexcept:
A) technologies.
B) statistics.
C) strategies.
D) databases.
20) Patterns nddiscovered ndfrom nd enable ndbusinesses ndto ndidentify ndopportunities ndand
risks ndand ndbetter ndplan ndfor nd .
nd
A) past ndarchives; ndthe ndfuture
B) current nddata; ndthe ndfuture
C) current nddata; ndtoday
D) past ndarchives; ndtoday
21) Which ndof ndthe ndfollowing ndbest nddescribes ndthe ndgoal ndof nddescriptive nddata ndanalysis:
A) recognize ndwhat ndis ndmeant ndby nddata ndquality, ndbe ndit ndcompleteness, ndreliability ndor
validity
nd
B) perform ndbasic ndanalysis ndto ndunderstand ndthe ndquality ndof ndthe ndunderlying nddata ndand ndits
ndability ndto ndaddress ndthe ndbusiness ndquestion
C) demonstrate ndability ndto ndsort, ndrearrange, ndmerge, ndand ndreconfigure nddata ndin nda
ndmanner ndthat ndallows ndenhanced ndanalysis
D) comprehend ndthe ndprocess ndneeded ndto ndclean ndand ndprepare ndthe nddata ndbefore ndanalysis
22) Which ndof ndthe ndfollowing ndMicrosoft ndsoftware ndtool ndspecializes ndin nddata ndjoining?
A) Excel
B) Power ndQuery
C) Power ndBI
D) Power ndAutomate
23) Which ndof ndthe ndfollowing ndMicrosoft ndsoftware ndtools ndspecializes ndin ndcreating nddashboards?
A) Excel
B) Power ndQuery
C) Power ndBI
D) Power ndAutomate
