Richardson, cTeeter, cTerrell c– cData cAnalytics cfor cAccounting,
c3e
Chapter c1-9
1
, Richardson, cTeeter, cTerrell c– cData cAnalytics cfor cAccounting,
c3e
Solutions cManual c– cChapter c1
Solutions cto cMultiple cChoice cQuestions
1. (LO c1-1) cBig cData cis coften cdescribed cby cthe cfour cVs, cor
a. volume, cvelocity, cveracity, cand cvariability.
b. volume, cvelocity, cveracity, cand cvariety.
c. volume, cvolatility, cveracity, cand cvariability.
d. variability, cvelocity, cveracity, cand cvariety.
Answer: cb
2. LO c1-4) cWhich cdata capproach cattempts cto cassign ceach cunit cin ca cpopulation cinto ca
csmall cset cof cclasses c(or cgroups) cwhere cthe cunit cbest cfits?
a. Regression
b. Similarity cmatching
c. Co-occurrence cgrouping
d. Classification
Answer: cd
3. (LO c1-4) cWhich cdata capproach cattempts cto cidentify csimilar cindividuals cbased con cdata
cknown cabout cthem?
a. Classification
b. Regression
c. Similarity cmatching
d. Data creduction
Answer: cc
4. (LO c1-4) cWhich cdata capproach cattempts cto cpredict cconnections cbetween ctwo cdata citems?
a. Profiling
b. Classification
c. Link cprediction
d. Regression
2
, Richardson, cTeeter, cTerrell c– cData cAnalytics cfor cAccounting,
c3e
Answer: cc
5. (LO c1-6) cWhich cof cthese cterms cis cdefined cas cbeing ca ccentral crepository cof cdescriptions
cfor call cof cthe cdata cattributes cof cthe cdataset?
a. Big cData
b. Data cwarehouse
c. Data cdictionary
d. Data cAnalytics
Answer: cc
6. (LO c1-5) cWhich cskills cwere cnot cemphasized cthat canalytic-minded caccountants cshould chave?
a. Developed can canalytics cmindset
b. Data cscrubbing cand cdata cpreparation
c. Classification cof ctest capproaches
d. Statistical cdata canalysis ccompetency
Answer: cc
7. (LO c1-5) cIn cwhich careas cwere cskills cnot cemphasized cfor canalytic-minded caccountants?
a. Data cquality
b. Descriptive cdata canalysis
c. Data cvisualization cand cdata creporting
d. Data cand csystems canalysis cand cdesign
Answer: cd
8. (LO c1-4) cThe cIMPACT ccycle cincludes call cexcept cthe cfollowing csteps:
a. perform ctest cplan.
b. visualize cthe cdata.
c. master cthe cdata.
d. track coutcomes.
Answer: cb
9. (LO c1-4) cThe cIMPACT ccycle cspecifically cincludes call cexcept cthe cfollowing csteps:
a. data cpreparation.
b. communicate cinsights.
c. address cand crefine cresults.
d. perform ctest cplan.
3
, Richardson, cTeeter, cTerrell c– cData cAnalytics cfor cAccounting,
c3e
Answer: ca
10. LO c1-1) cBy cthe cyear c2024, cthe cvolume cof cdata ccreated, ccaptured, ccopied, cand
cconsumed cworldwide cwill cbe c149 c .
a. zettabytes
b. petabytes
c. exabytes
d. yottabytes
Answer: ca
Solutions cto cDiscussion cand cAnalysis cQuestions
1. The caccounting cfunction cis cone cof cbeing can cinformation cprovider. c To cthe cextent cthat
cdata cis cavailable cto caddress caccounting cquestions, cbe cthey ctax, cmanagerial, caudit cor
cfinancial cquestions. cWith csuch crich cavailable cdata, cand csoftware ctools cto cprepare cand
canalyze cthe cdata, cdata canalytics cwill ccontinue cto cbe can cimportant ctool cfor caccountants
cto cuse.
2. Data canalytics cis cdefined cas cthe cprocess cof cevaluating cdata cwith cthe cpurpose cof
cdrawing cconclusions cto caddress cbusiness cquestions. cIndeed, ceffective cData cAnalytics
cprovides ca cway cto csearch cthrough clarge cstructured cand cunstructured cdata cto cidentify
cunknown cpatterns cor crelationships.
A cuniversity cmight clearn cfrom cthe canalyzing cthe cdemographics cof cits ccurrent cset cof
cstudents cin corder cto cattract cits cfuture cstudent crecruits. cDid cthey ccome cfrom ccities cor
chigh cschools cthat cwere cclose cby? cWere ctheir cparents calumni cof cthe cuniversity? cDid cthey
cscore chigh con ccertain cparts cof cthe cACT? cWere cthose coffered ca cscholarship cmore clikely
cto cattend, cetc.? cWas csocial cmedia ceffective cin cattracting cnew, cpotentially cstronger
cstudents? cBy canalyzing cthis ctype cof cdata, cpreviously cunknown cpatterns cwill cemerge cthat
cwill cmake crecruiting cstudents cmore ceffective.
3. There care cmany cpotential canswers. c For cexample, cMonsanto cmay cuse cmathematical cand
cstatistical cmodels cto cplot cout cthe cbest ctimes cto cplant cboth cmale cand cfemale cplants cand
cwhere cto cplant cthem cto cmaximize cyield.
c(https://www.cio.com/article/3221621/analytics/6-data- canalytics-success-stories-an-inside-
look.html#tk.cio_rs)
4. There care cmany cpotential canswers. cData canalytics cgives cboth cinternal cand cexternal
cauditors cadditional ctools cto cexamine cevery caccounting ctransaction cand cassess cfor
ccompliance cwith cGAAP. cThe caudit cprocess cis cchanging cfrom ca ctraditional cprocess ctoward
ca cmore cautomated cone, cwhich cwill callow caudit cprofessionals cto cfocus cmore con cthe
clogic cand crationale cbehind cdata cqueries cand cless con cthe cgathering cof cthe cactual cdata.
cNo clonger cwill cthey cbe csimply cchecking cfor cerrors, cmaterial cmisstatements, cfraud, cand
crisk cin cfinancial cstatements cor cmerely cbe creporting ctheir cfindings cat cthe cend cof cthe
cengagement. cInstead, caudit cprofessionals cwill cnow cbe ccollecting cand
4
c3e
Chapter c1-9
1
, Richardson, cTeeter, cTerrell c– cData cAnalytics cfor cAccounting,
c3e
Solutions cManual c– cChapter c1
Solutions cto cMultiple cChoice cQuestions
1. (LO c1-1) cBig cData cis coften cdescribed cby cthe cfour cVs, cor
a. volume, cvelocity, cveracity, cand cvariability.
b. volume, cvelocity, cveracity, cand cvariety.
c. volume, cvolatility, cveracity, cand cvariability.
d. variability, cvelocity, cveracity, cand cvariety.
Answer: cb
2. LO c1-4) cWhich cdata capproach cattempts cto cassign ceach cunit cin ca cpopulation cinto ca
csmall cset cof cclasses c(or cgroups) cwhere cthe cunit cbest cfits?
a. Regression
b. Similarity cmatching
c. Co-occurrence cgrouping
d. Classification
Answer: cd
3. (LO c1-4) cWhich cdata capproach cattempts cto cidentify csimilar cindividuals cbased con cdata
cknown cabout cthem?
a. Classification
b. Regression
c. Similarity cmatching
d. Data creduction
Answer: cc
4. (LO c1-4) cWhich cdata capproach cattempts cto cpredict cconnections cbetween ctwo cdata citems?
a. Profiling
b. Classification
c. Link cprediction
d. Regression
2
, Richardson, cTeeter, cTerrell c– cData cAnalytics cfor cAccounting,
c3e
Answer: cc
5. (LO c1-6) cWhich cof cthese cterms cis cdefined cas cbeing ca ccentral crepository cof cdescriptions
cfor call cof cthe cdata cattributes cof cthe cdataset?
a. Big cData
b. Data cwarehouse
c. Data cdictionary
d. Data cAnalytics
Answer: cc
6. (LO c1-5) cWhich cskills cwere cnot cemphasized cthat canalytic-minded caccountants cshould chave?
a. Developed can canalytics cmindset
b. Data cscrubbing cand cdata cpreparation
c. Classification cof ctest capproaches
d. Statistical cdata canalysis ccompetency
Answer: cc
7. (LO c1-5) cIn cwhich careas cwere cskills cnot cemphasized cfor canalytic-minded caccountants?
a. Data cquality
b. Descriptive cdata canalysis
c. Data cvisualization cand cdata creporting
d. Data cand csystems canalysis cand cdesign
Answer: cd
8. (LO c1-4) cThe cIMPACT ccycle cincludes call cexcept cthe cfollowing csteps:
a. perform ctest cplan.
b. visualize cthe cdata.
c. master cthe cdata.
d. track coutcomes.
Answer: cb
9. (LO c1-4) cThe cIMPACT ccycle cspecifically cincludes call cexcept cthe cfollowing csteps:
a. data cpreparation.
b. communicate cinsights.
c. address cand crefine cresults.
d. perform ctest cplan.
3
, Richardson, cTeeter, cTerrell c– cData cAnalytics cfor cAccounting,
c3e
Answer: ca
10. LO c1-1) cBy cthe cyear c2024, cthe cvolume cof cdata ccreated, ccaptured, ccopied, cand
cconsumed cworldwide cwill cbe c149 c .
a. zettabytes
b. petabytes
c. exabytes
d. yottabytes
Answer: ca
Solutions cto cDiscussion cand cAnalysis cQuestions
1. The caccounting cfunction cis cone cof cbeing can cinformation cprovider. c To cthe cextent cthat
cdata cis cavailable cto caddress caccounting cquestions, cbe cthey ctax, cmanagerial, caudit cor
cfinancial cquestions. cWith csuch crich cavailable cdata, cand csoftware ctools cto cprepare cand
canalyze cthe cdata, cdata canalytics cwill ccontinue cto cbe can cimportant ctool cfor caccountants
cto cuse.
2. Data canalytics cis cdefined cas cthe cprocess cof cevaluating cdata cwith cthe cpurpose cof
cdrawing cconclusions cto caddress cbusiness cquestions. cIndeed, ceffective cData cAnalytics
cprovides ca cway cto csearch cthrough clarge cstructured cand cunstructured cdata cto cidentify
cunknown cpatterns cor crelationships.
A cuniversity cmight clearn cfrom cthe canalyzing cthe cdemographics cof cits ccurrent cset cof
cstudents cin corder cto cattract cits cfuture cstudent crecruits. cDid cthey ccome cfrom ccities cor
chigh cschools cthat cwere cclose cby? cWere ctheir cparents calumni cof cthe cuniversity? cDid cthey
cscore chigh con ccertain cparts cof cthe cACT? cWere cthose coffered ca cscholarship cmore clikely
cto cattend, cetc.? cWas csocial cmedia ceffective cin cattracting cnew, cpotentially cstronger
cstudents? cBy canalyzing cthis ctype cof cdata, cpreviously cunknown cpatterns cwill cemerge cthat
cwill cmake crecruiting cstudents cmore ceffective.
3. There care cmany cpotential canswers. c For cexample, cMonsanto cmay cuse cmathematical cand
cstatistical cmodels cto cplot cout cthe cbest ctimes cto cplant cboth cmale cand cfemale cplants cand
cwhere cto cplant cthem cto cmaximize cyield.
c(https://www.cio.com/article/3221621/analytics/6-data- canalytics-success-stories-an-inside-
look.html#tk.cio_rs)
4. There care cmany cpotential canswers. cData canalytics cgives cboth cinternal cand cexternal
cauditors cadditional ctools cto cexamine cevery caccounting ctransaction cand cassess cfor
ccompliance cwith cGAAP. cThe caudit cprocess cis cchanging cfrom ca ctraditional cprocess ctoward
ca cmore cautomated cone, cwhich cwill callow caudit cprofessionals cto cfocus cmore con cthe
clogic cand crationale cbehind cdata cqueries cand cless con cthe cgathering cof cthe cactual cdata.
cNo clonger cwill cthey cbe csimply cchecking cfor cerrors, cmaterial cmisstatements, cfraud, cand
crisk cin cfinancial cstatements cor cmerely cbe creporting ctheir cfindings cat cthe cend cof cthe
cengagement. cInstead, caudit cprofessionals cwill cnow cbe ccollecting cand
4
