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Test Bank for Introduction to Management Science and Business Analytics, 27th Edition by Hillier.pdf

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Test Bank for Introduction to Management Science and Business Analytics, 27th Edition by H

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,ACCESS Test Bank for Introduction to Management Science and Business Analy
n n n n n n n n n n

tics 7th Edition Hillier
n n n




CHAPTER 1 n


n INTRODUCTIO
N

Review Questions
n


1.1 –1 Thenrapidndevelopmentnofnthendisciplinenbeganninnthen1940’snandn1950’s.
1.1 –2 Thentraditionalnnamengivenntonthendisciplinenisnoperationsnresearch.
1.1 –3
Anmanagementnsciencenstudynprovidesnannanalysisnandnrecommendations,nbase
dnonnthenquantitativenfactorsninvolvedninnthenproblem,nasninputntonthenmanagers.
1.1 –4
Managementnsciencenisnbasednstronglynonnsomenscientificnfields,nincludingnmathema
ticsnandncomputernscience.n Itnalsondrawsnuponnthensocialnsciences,nespeciallyneconomics.
1.1 –5
Manynmanagerialnproblemsnrevolvenaroundnsuchnquantitativenfactorsnasnproductionnqu
antities,nrevenues,ncosts,nthenamountsnavailablenofnneedednresources,netc.
1.2 –1
Businessnanalyticsnhasndrawnnonnvariousnothernquantitativendecisionnsciences,ninc
ludingnmanagementnscience,nmathematics,nstatistics,ncomputernscience,ninformationntech
nology,nindustrialnengineering,netc.
1.2 –2
Theneranofnbigndatanisnwherenmassivenamountsnofndatan(accompaniednbynmassivenam
ountsnofncomputationalnpower)narennowncommonlynavailablentonmanynbusinessesntonhelpng
uide
managerialndecisionnmaking.nAnprimarynfocusnofnbusinessnanalyticsnisnonnhowntonmakenthenmostnef
fectivenusenofnallnthesendata.
1.2 –
3n Descriptivenanalyticsnusesninnovativentechniquesn(includingnalgorithms)ntonexplorenthend
ata,nlocatenandnextractnthendatanthatnarenrelevant,nandnthennidentifyntheninterestingnpattern
snandnsummaryndata.
1.2 –4 Predictivenanalyticsnoftenninvolvesnapplyingnstatisticalnmodelsntonpredictnfutureneventsnorntrends.
1.2 –5
Prescriptivenanalyticsnusesnpowerfulntechniquesndrawnnmainlynfromnmanagementnsc
iencentonprescribenwhatnshouldnbendoneninnthenfuture.
1.2 –6 Datansciencentendsntonbenmoreninterdisciplinary,nmorenbasednonnscientificnmethods,nmore
applicablentonvariousnareasninnadditionntonbusiness,nandnmorenconcernednwithnhowntondealnwithnev
ennmassivenamountsnofndataninnvariousnforms.
1.2 –7
Thengoalnofnmachinenlearningnisntonallowncomputersntonlearnnautomaticallynfromnhistorica
lnrelationshipsnandntrendsninnthendataninnorderntondonsuchnthingsnasnmakingndata-

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,ACCESS Test Bank for Introduction to Management Science and Business Analy
n n n n n n n n n n

tics 7th Edition Hilliern n n




drivennpredictions.
Machinenlearningnisnanpopularnmethodnfornapplyingnpredictivenanalyticsnbynperformingnpatternnrec
ognition.
1.2 –8
Machinenlearningnisnjustnonenimportantnpartnofnartificialnintelligence.nBecausenitsnfoc
usnisnonnautomaticallynlearningnfromnhistoricalnrelationshipsnandntrendsninnthendata,nmachinen
learning
providesnannidealnplatformnfornperformingnartificialnintelligence.nThendifficultnpartnthennisntakingnt
hennextnstepntonusenthisnplatformntontrulynsimulatenhumannthinkingncapabilitynandnbehavior.

1–1
©nMcGrawnHillnLLC.nAllnrightsnreserved.nNonreproductionnorndistributionnwithoutnthenpriornwrittennconsentnofnMcGrawnHillnLLC.




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, ACCESS Test Bank for Introduction to Management Science and Business Analy
n n n n n n n n n n

tics 7th Edition Hillier
n n n




1.3 –1 Businessnanalyticsnprovidesnanlargerntoolkitnwhenndealingnwithndescriptivenanalytics,ndata
preparation,nandnpredictivenanalytics.nManagementnsciencentechniquesnnormallyntakenthenleadnwh
ennperformingnprescriptivenanalytics.
1.3 –2
Analyticsnhavenbeennmakingnimportantncontributionsninnsuchnareasnasnsports,npoliticalnca
mpaigns,nhealthcare,ncombatingncrime,npersonalnfinancialnanalysis,netc.
1.3 –3 Topnmanagementnnownunderstandsnverynwellnthenimpactnthatnbusinessnanalyticsnand
managementnsciencencannhavenonnthenbottomnline.nThisnwillncontinuentonrequirenmanynmorenpeo
plenwhonarenverynwellntrainedninnbusinessnanalyticsnandnmanagementnscience
1.3 –4
Operationsnresearchnhasnbeennthenusualnnamengivenntonmanagementnsciencenoutsidenofn
businessnschools.
1.3 –5 INFORMSn(thenInstitutenofnOperationsnResearchnandnthenManagementnSciences)nisnthenlargest
professionalnsocietynofnmanagementnsciencenandnbusinessnanalyticsnprofessionalsnornstudentsninnt
henworld.
1.4 –1 Variablencostsnincludenallnofnthencostsnthatnarenproportionalnwithnthennumbernofnunitsnproduced.
1.4 –2
Fixedncostsnincludenthencostsnthatnarenincurrednregardlessnofnhownmanynunitsnarenproduce
d.nThesenmightnincludenanproratednsharenofnthensalariesnfornuppernmanagement,ncapitalnequipme
nt,
propertyntaxes,nandnmore.
1.4 –3 Therenarenoftenndiminishingnreturnsnfromnadvertisingnthatnarennotncapturednbynlinearnregression.
1.4 –4
Ifnanpolynomialnequationnisnusedntontryntonpredictnwhatnsalesnwillnbenfornadvertisingnbudge
ts,nitnwillneventuallynstartnslopingndownward.nWhilenwenintuitivelynwouldnexpectndiminishingnretu
rnsnasnadvertisingnisnincreased,nwenwouldn’tnexpectnsalesntonactuallyndecrease.
1.4 –5
Salesndonnotnincreasenproportionallynwithnthenlevelnofnadvertising,nbutnratherni
ncreasenproportionallynwithnthensquarenrootnofnadvertising.
1.4–6
Solvernwillnfindnthenvaluenofndecisionnvariablencellsnthatnwillnoptimizenthenvaluenofnanno
bjectivencell.


Problems
1.1 ThenthreenmodulesnarenthenIncidentnTicketnClassificationnModulen(whichnlistsnandnclassifiesnthenu
ndesirablenincidentsnfornthenvariousnservers),nthenServernClassificationnModulen(whichnlinksnthens
ervernunavailablenincidentsntonthenserversninvolvednandnthenndevelopsnmetricsnfornthenindividualn
servers),nandnthenRemediationnRecommendationnModulen(whichndevelopsnrecommendationsnof
nthenappropriatenmodernizationnactionsnthatnshouldnbentakennfornproblematicnservers).




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