1
, Chapter 1 End-of-Chapter Assignment Solutions
r r r r
Multiple rChoice rQuestions
1. (LO r1.1) rA rcoordinated, rstandardized rset rof ractivities rconducted rby rboth rpeople rand requipment rto
raccomplish ra rspecific rbusiness rtask ris rcalled r .
a. business rprocesses
b. business ranalysis
c. business rprocedure
d. business rvalue
2. (LO r1.2) rAccording rto rthe rinformation rvalue rchain, rdata rcombined rwith rcontext ris
a. Information.
b. Knowledge.
c. Insight.
d. Value.
3. (LO r 1.5) rWhich rphase rof rthe rSOAR ranalytics rmodel raddresses rthe rproper rway rto rcommunicate rresults rto
rthe rdecision rmaker?
a. Specify rthe rquestion
b. Obtain rthe rdata
c. Analyze rthe rdata
d. Report rthe rresults
4. (LO r1.5) rWhich rphase rof rthe rSOAR ranalytics rmodel rinvolves rfinding rthe rmost rappropriate rdata rneeded rto
raddress rthe rbusiness rquestion?
a. Specify rthe rquestion
b. Obtain rthe rdata
c. Analyze rthe rdata
d. Report rthe rresults
5. (LO r1.5) rWhich rquestions rseek rinformation rabout rTesla’s rsales rin rthe rnext rquarter?
a. What rhappened? rWhat ris rhappening?
b. Why rdid rit rhappen? rWhat rare rthe rcauses rof rpast rresults?
c. Will rit rhappen rin rthe rfuture? rWhat ris rthe rprobability rsomething rwill rhappen? rCan rwe rforecast
rwhat rwill rhappen?
d. What rshould rwe rdo, rbased ron rwhat rwe rexpect rwill rhappen? rHow rdo rwe roptimize rour rperformance
rbased ron rpotential rconstraints?
6. (LO r1.5) rWhich rquestions rseek rinformation ron rthe rrouting rof rproducts rfrom rQueretaro, rMexico rto
rChicago, rUnited rStates rin rthe rlast rquarter?
a. What rhappened? rWhat ris rhappening?
b. Why rdid rit rhappen? rWhat rare rthe rcauses rof rpast rresults?
c. Will rit rhappen rin rthe rfuture? rWhat ris rthe rprobability rsomething rwill rhappen? rCan rwe rforecast rwhat
rwill rhappen?
d. What rshould rwe rdo, rbased ron rwhat rwe rexpect rwill rhappen? rHow rdo rwe roptimize rour rperformance
rbased ron rpotential rconstraints?
© rMcGraw rHill rLLC. rAll rrights rreserved. rNo rreproduction ror rdistribution rwithout rthe rprior rwritten rconsent rof rMcGraw rHill rLLC.
2
,7. (LO r1.5) rWhich rquestions rask rwhy rnet rincome ris rincreasing rwhen rrevenues rare rdecreasing, rcounter
rto rexpectations?
a. What rhappened? rWhat ris rhappening?
b. Why rdid rit rhappen? rWhat rare rthe rcauses rof rpast rresults?
c. Will rit rhappen rin rthe rfuture? rWhat ris rthe rprobability rsomething rwill rhappen? rCan rwe rforecast rwhat
rwill rhappen?
d. What rshould rwe rdo, rbased ron rwhat rwe rexpect rwill rhappen? rHow rdo rwe roptimize rour rperformance
rbased ron rpotential rconstraints?
8. (LO r1.5) rWhich rquestions rhelp rmanagers runderstand rhow rto rorganize rfuture rshipments rbased ron
rexpected rdemand?
a. What rhappened? rWhat ris rhappening?
b. Why rdid rit rhappen? rWhat rare rthe rcauses rof rpast rresults?
c. Will rit rhappen rin rthe rfuture? rWhat ris rthe rprobability rsomething rwill rhappen? rCan rwe rforecast rwhat
rwill rhappen?
d. What rshould rwe rdo, rbased ron rwhat rwe rexpect rwill rhappen? rHow rdo rwe roptimize rour
rperformance rbased ron rpotential rconstraints?
9. (LO r 1.5) rWhich rterm rrefers rto rthe rcombined raccuracy, rvalidity, rand rconsistency rof rdata rstored rand rused
rover rtime?
a. Data rintegrity
b. Data roverload
c. Data rvalue
d. Information rvalue
10. (LO r1.3) rA rspecialist rwho rknows rhow rto rwork rwith, rmanipulate, rand rstatistically rtest rdata ris ra
a. decision rmaker.
b. data rscientist.
c. data ranalyst.
d. decision rscientist.
11. (LO r 1.4) rWhich rtype rof ranalysts rpredicts rthe ramount rof rmoney rthat ra rcompany rwill rreceive rfrom rits
rcustomers rto rhelp rmanagement revaluate rfuture rinvestments rbased ron rexpected rinvestment rperformance,
rsuch ras rinvestments rin requipment ror remployee rtraining?
a. Marketing ranalyst
b. Operations ranalyst
c. Financial ranalyst
d. Accounting ranalyst
12. (LO r1.4) rWhich rtype rof ranalyst raddresses rquestions rregarding rtax rand rauditing?
a. Marketing ranalyst
b. Operations ranalyst
c. Financial ranalyst
d. Accounting ranalyst
13. (LO r 1.5) rSuppose ra rcompany rhas rtimely rproduct rreviews rthat rare ravailable rwhen rneeded, rbut rthe rreviews
rare rbiased. rThese rproduct rreviews rare rwhich rtype rof rdata?
a. Reliable
b. Relevant
c. Curated
d. Consistent
© rMcGraw rHill rLLC. rAll rrights rreserved. rNo rreproduction ror rdistribution rwithout rthe rprior rwritten rconsent rof rMcGraw rHill rLLC.
3
, 14. (LO r1.6) rWhich rcommon rvisualization rtype rshows rtrends rin rvalues rover rtime?
a. Line rgraph
b. Scatterplot
c. Pie rchart
d. Bar rchart
15. (LO r1.6) rWhich rcommon rvisualization rtype rshows rthe rcomposition rof rvalues rover rtime?
a. Line rgraph
b. Scatterplot
c. Pie rchart
d. Bar rchart
Discussion rQuestions
1. (LO r 1.1) rGive rfive rexamples rof rbusiness rprocesses rat rTesla. r How rdo rthey rcreate rbusiness rvalue rfor rTesla
rand rits rshareholders?
Suggested rSolution:
Answers rwill rvary,
1. Tesla rprocures rautomobile rparts rfrom rauto rsuppliers r– Because rof rTesla’s runique rstyling, rgetting
rquality rparts rfrom rits rsuppliers ron ra rtimely rbasis rwill rsupport rits rmanufacturing rbusiness.
2. Tesla rmanufactures rbatteries rfor rits relectric rvehicle rat rits rdesired rspecifications r– rThe rquantity rand
rquality rof rits rbatteries rare rof rcritical rimportance rto rTesla.
3. Accepting rand rprocessing rpreorders rfrom rits rcustomers r– rTesla rreceives rsome rindication rof rthe rdemand
rfor reach rof rits rproducts, rthat rhelps rwith rplanning.
4. Tesla rmarkets rits rproducts r– rTesla rworks rto rget rTesla rproducts rin rthe rfront rof rmind rfor rits rcustomers.
5. Tesla rcar rand rtruck rdesign r– rTesla rdesigns rits rautomobiles rin ra rway rthat rwill rappeal rto rits rcustomers
r(for rexample, rCybertruck).
2. (LO r 1.2) rExplain rthe rinformation rvalue rchain rby rsummarizing rhow rdata rare rtransformed rinto rknowledge
rinsights rfor rdecision-making. rUse rthe rexample rof ra rbook rreview ron rAmazon rand rhow rit rmight rlead rAmazon rto
rdecide rhow rmany rof rthose rbooks rto rstock rat rits rwarehouses.
SuggestedrSolution:
Amazon rallows rthose rwho rpurchase rbooks rand rother rproducts rat rits rwebsite rto rgive rproduct rreviews rand
rassign rproduct rratings. r The rproduct rreviews rmay rprovide rtext rwhich rtextual ranalytics rcould ruse rto
runderstand rthe rgeneral rsentiment rabout rthe rspecific rbook. r The rproduct rrating rcould ralso rbe rused rto
runderstand rhow rwell rthe rbook ris rliked rby rverified rbuyers. r Statistical rcorrelations rcould rbe rrun ramong
rproduct rreview rsentiment, rproduct rratings rand rproduct rsales rto rhelp rforecast rdemand rfor rthe rproduct. r This
rwill rhelp rAmazon rdetermine rhow rmany rbooks rto rkeep rin rits rwarehouse rready rfor rdelivery.
This ris ran rexample rof rhow rdata rturns rinto rinformation, rknowledge rand rultimately rhelps rwith rdecision rmaking.
3. (LO r 1.3) rExplain rthe rinformation rvalue rchain rby rsummarizing rhow rdata rare rtransformed rinto rknowledge
rinsights rfor rdecision-making. rUse rthe rexample rof ra rbook rreview rof rthis rbook ron rAmazon rand rhow rit rmight
rhelp rthe rpublisher, rMcGraw rHill, rdetermine rwhether rto rrevise rthis rbook rfor ra rnew, rupdated redition ras rthe
rdiscipline rof rdata ranalytics revolves.
© rMcGraw rHill rLLC. rAll rrights rreserved. rNo rreproduction ror rdistribution rwithout rthe rprior rwritten rconsent rof rMcGraw rHill rLLC.
4
, Chapter 1 End-of-Chapter Assignment Solutions
r r r r
Multiple rChoice rQuestions
1. (LO r1.1) rA rcoordinated, rstandardized rset rof ractivities rconducted rby rboth rpeople rand requipment rto
raccomplish ra rspecific rbusiness rtask ris rcalled r .
a. business rprocesses
b. business ranalysis
c. business rprocedure
d. business rvalue
2. (LO r1.2) rAccording rto rthe rinformation rvalue rchain, rdata rcombined rwith rcontext ris
a. Information.
b. Knowledge.
c. Insight.
d. Value.
3. (LO r 1.5) rWhich rphase rof rthe rSOAR ranalytics rmodel raddresses rthe rproper rway rto rcommunicate rresults rto
rthe rdecision rmaker?
a. Specify rthe rquestion
b. Obtain rthe rdata
c. Analyze rthe rdata
d. Report rthe rresults
4. (LO r1.5) rWhich rphase rof rthe rSOAR ranalytics rmodel rinvolves rfinding rthe rmost rappropriate rdata rneeded rto
raddress rthe rbusiness rquestion?
a. Specify rthe rquestion
b. Obtain rthe rdata
c. Analyze rthe rdata
d. Report rthe rresults
5. (LO r1.5) rWhich rquestions rseek rinformation rabout rTesla’s rsales rin rthe rnext rquarter?
a. What rhappened? rWhat ris rhappening?
b. Why rdid rit rhappen? rWhat rare rthe rcauses rof rpast rresults?
c. Will rit rhappen rin rthe rfuture? rWhat ris rthe rprobability rsomething rwill rhappen? rCan rwe rforecast
rwhat rwill rhappen?
d. What rshould rwe rdo, rbased ron rwhat rwe rexpect rwill rhappen? rHow rdo rwe roptimize rour rperformance
rbased ron rpotential rconstraints?
6. (LO r1.5) rWhich rquestions rseek rinformation ron rthe rrouting rof rproducts rfrom rQueretaro, rMexico rto
rChicago, rUnited rStates rin rthe rlast rquarter?
a. What rhappened? rWhat ris rhappening?
b. Why rdid rit rhappen? rWhat rare rthe rcauses rof rpast rresults?
c. Will rit rhappen rin rthe rfuture? rWhat ris rthe rprobability rsomething rwill rhappen? rCan rwe rforecast rwhat
rwill rhappen?
d. What rshould rwe rdo, rbased ron rwhat rwe rexpect rwill rhappen? rHow rdo rwe roptimize rour rperformance
rbased ron rpotential rconstraints?
© rMcGraw rHill rLLC. rAll rrights rreserved. rNo rreproduction ror rdistribution rwithout rthe rprior rwritten rconsent rof rMcGraw rHill rLLC.
2
,7. (LO r1.5) rWhich rquestions rask rwhy rnet rincome ris rincreasing rwhen rrevenues rare rdecreasing, rcounter
rto rexpectations?
a. What rhappened? rWhat ris rhappening?
b. Why rdid rit rhappen? rWhat rare rthe rcauses rof rpast rresults?
c. Will rit rhappen rin rthe rfuture? rWhat ris rthe rprobability rsomething rwill rhappen? rCan rwe rforecast rwhat
rwill rhappen?
d. What rshould rwe rdo, rbased ron rwhat rwe rexpect rwill rhappen? rHow rdo rwe roptimize rour rperformance
rbased ron rpotential rconstraints?
8. (LO r1.5) rWhich rquestions rhelp rmanagers runderstand rhow rto rorganize rfuture rshipments rbased ron
rexpected rdemand?
a. What rhappened? rWhat ris rhappening?
b. Why rdid rit rhappen? rWhat rare rthe rcauses rof rpast rresults?
c. Will rit rhappen rin rthe rfuture? rWhat ris rthe rprobability rsomething rwill rhappen? rCan rwe rforecast rwhat
rwill rhappen?
d. What rshould rwe rdo, rbased ron rwhat rwe rexpect rwill rhappen? rHow rdo rwe roptimize rour
rperformance rbased ron rpotential rconstraints?
9. (LO r 1.5) rWhich rterm rrefers rto rthe rcombined raccuracy, rvalidity, rand rconsistency rof rdata rstored rand rused
rover rtime?
a. Data rintegrity
b. Data roverload
c. Data rvalue
d. Information rvalue
10. (LO r1.3) rA rspecialist rwho rknows rhow rto rwork rwith, rmanipulate, rand rstatistically rtest rdata ris ra
a. decision rmaker.
b. data rscientist.
c. data ranalyst.
d. decision rscientist.
11. (LO r 1.4) rWhich rtype rof ranalysts rpredicts rthe ramount rof rmoney rthat ra rcompany rwill rreceive rfrom rits
rcustomers rto rhelp rmanagement revaluate rfuture rinvestments rbased ron rexpected rinvestment rperformance,
rsuch ras rinvestments rin requipment ror remployee rtraining?
a. Marketing ranalyst
b. Operations ranalyst
c. Financial ranalyst
d. Accounting ranalyst
12. (LO r1.4) rWhich rtype rof ranalyst raddresses rquestions rregarding rtax rand rauditing?
a. Marketing ranalyst
b. Operations ranalyst
c. Financial ranalyst
d. Accounting ranalyst
13. (LO r 1.5) rSuppose ra rcompany rhas rtimely rproduct rreviews rthat rare ravailable rwhen rneeded, rbut rthe rreviews
rare rbiased. rThese rproduct rreviews rare rwhich rtype rof rdata?
a. Reliable
b. Relevant
c. Curated
d. Consistent
© rMcGraw rHill rLLC. rAll rrights rreserved. rNo rreproduction ror rdistribution rwithout rthe rprior rwritten rconsent rof rMcGraw rHill rLLC.
3
, 14. (LO r1.6) rWhich rcommon rvisualization rtype rshows rtrends rin rvalues rover rtime?
a. Line rgraph
b. Scatterplot
c. Pie rchart
d. Bar rchart
15. (LO r1.6) rWhich rcommon rvisualization rtype rshows rthe rcomposition rof rvalues rover rtime?
a. Line rgraph
b. Scatterplot
c. Pie rchart
d. Bar rchart
Discussion rQuestions
1. (LO r 1.1) rGive rfive rexamples rof rbusiness rprocesses rat rTesla. r How rdo rthey rcreate rbusiness rvalue rfor rTesla
rand rits rshareholders?
Suggested rSolution:
Answers rwill rvary,
1. Tesla rprocures rautomobile rparts rfrom rauto rsuppliers r– Because rof rTesla’s runique rstyling, rgetting
rquality rparts rfrom rits rsuppliers ron ra rtimely rbasis rwill rsupport rits rmanufacturing rbusiness.
2. Tesla rmanufactures rbatteries rfor rits relectric rvehicle rat rits rdesired rspecifications r– rThe rquantity rand
rquality rof rits rbatteries rare rof rcritical rimportance rto rTesla.
3. Accepting rand rprocessing rpreorders rfrom rits rcustomers r– rTesla rreceives rsome rindication rof rthe rdemand
rfor reach rof rits rproducts, rthat rhelps rwith rplanning.
4. Tesla rmarkets rits rproducts r– rTesla rworks rto rget rTesla rproducts rin rthe rfront rof rmind rfor rits rcustomers.
5. Tesla rcar rand rtruck rdesign r– rTesla rdesigns rits rautomobiles rin ra rway rthat rwill rappeal rto rits rcustomers
r(for rexample, rCybertruck).
2. (LO r 1.2) rExplain rthe rinformation rvalue rchain rby rsummarizing rhow rdata rare rtransformed rinto rknowledge
rinsights rfor rdecision-making. rUse rthe rexample rof ra rbook rreview ron rAmazon rand rhow rit rmight rlead rAmazon rto
rdecide rhow rmany rof rthose rbooks rto rstock rat rits rwarehouses.
SuggestedrSolution:
Amazon rallows rthose rwho rpurchase rbooks rand rother rproducts rat rits rwebsite rto rgive rproduct rreviews rand
rassign rproduct rratings. r The rproduct rreviews rmay rprovide rtext rwhich rtextual ranalytics rcould ruse rto
runderstand rthe rgeneral rsentiment rabout rthe rspecific rbook. r The rproduct rrating rcould ralso rbe rused rto
runderstand rhow rwell rthe rbook ris rliked rby rverified rbuyers. r Statistical rcorrelations rcould rbe rrun ramong
rproduct rreview rsentiment, rproduct rratings rand rproduct rsales rto rhelp rforecast rdemand rfor rthe rproduct. r This
rwill rhelp rAmazon rdetermine rhow rmany rbooks rto rkeep rin rits rwarehouse rready rfor rdelivery.
This ris ran rexample rof rhow rdata rturns rinto rinformation, rknowledge rand rultimately rhelps rwith rdecision rmaking.
3. (LO r 1.3) rExplain rthe rinformation rvalue rchain rby rsummarizing rhow rdata rare rtransformed rinto rknowledge
rinsights rfor rdecision-making. rUse rthe rexample rof ra rbook rreview rof rthis rbook ron rAmazon rand rhow rit rmight
rhelp rthe rpublisher, rMcGraw rHill, rdetermine rwhether rto rrevise rthis rbook rfor ra rnew, rupdated redition ras rthe
rdiscipline rof rdata ranalytics revolves.
© rMcGraw rHill rLLC. rAll rrights rreserved. rNo rreproduction ror rdistribution rwithout rthe rprior rwritten rconsent rof rMcGraw rHill rLLC.
4
