BSAN 160 exam review | With complete solution | RATED A+

BSAN 160 exam review | With complete solution | RATED A+ T IIor IIF:Decision IIsupport IIsystem IIare IIcomputer -based IIsupport IIsystems IIthat IIintegrate IIindividuals' IIexpertise IIand IIcomputer IIcapabilities, IIand IIthey IIhave IIprecise IIdefinitions IIagreed IIto IIby IIpractitioners. II- II II II IIFalse Business IIIntelligence II(BI) II- II II II IIis IIan IIumbrella IIterm IIthat IIcombines IIarchitectures, IIdatabases, IIanalytical IItools, IIapplications, IIand IImethodologies T IIor IIF: IIData IIis IIa IIcollection IIof IIobservations, IIexperiments, IIand IIexperiences IIthat IIdo IInot IInecessarily IIrepresent IIabsolute IIfacts IIthat IIare IIuniversally IItrue. II- II II II IITrue Descriptive IIAnalytics II- II II II IIhelp IImanagers IIunderstand IIcurrent IIevents IIin IIthe IIorganization IIincluding IIcauses, IItrends, IIand IIpatterns. What IItype IIof IIanalytics IIseeks IIto IIrecognize IIwhat IIis IIgoing IIon IIas IIwell IIas IIthe IIlikely IIforecast IIand IImake IIdecisions IIto IIachieve IIthe IIbest IIperformance IIpossible? II- II II II IIPrescriptive Which IIof IIthe IIfollowing IIis/are IIpredictive IIanalytics IImethod(s)? IIA)Boxplot IIB)Text IIanalysis IIC)Simulation IID)Regression IIanalysis, IIE)Clustering IIB, IID IIand IIEB, IIC IIand IIED IIand IIE II- II II II IIB, IID, IIE Using IIcharacteris tics IIof IIfirst IIyear IIundergraduate IIstudents, IIsuch IIas IIage, IIgender, IImajor, IIlocation, IIworkout/sports IIactivities, IIif IIwe IIdeveloped IIa IImodel IIto IIforecast IIwhich IIstudents IIare IIat IIrisk IIof IIdropping IIout IIafter IIthe IIfirst IIyear IIof IIcollege, IIdecided IIwhich IIstudents IIto IIreach IIout IIto IIand IIoffered IIthem IIsupport IIservices IIto IIreduce IItheir IIrisk IIof IIdropping IIout, IIwhat IIkind IIof IIanalytics IIapplication IIwould IIthis IIwork IIrepresent? II- II II II IIprescriptive IIanalytics Which IIchart IItype IIbelow IIwould IIbe IImost IIhelpful IIto IIshow IIthe IIcomparison IIbetween IIworldwide IIturnover IIrate IIcompared IIwith IItech IIsector IIturnover IIrate? Line IIchart Histogram Bar IIchart Scatterplot II- II II II IIBar IIchart Which IIchart IItype IIbelow IIwould IIbe IImost IIhelpful IIto IIshow IIthe IIrelative IIproportions IIof IIturnover IIrate IIof IIdifferent IIcategories II(e.g., IIcomputer IIgames, IIInternet, IIcomputer IIsoftware IIand IIother) IIwithin IIthe IItech IIsector IIthat IIdrive IItech IIturnover IIthe IImost? Histogram Pie IIchart Bar IIchart Scatterplot II- II II II IIPie IIChart T IIor IIF: IIOriginal II(raw) IIdata IIis IIusually IIcollected IIfrom IImultiple IIdata IIsources IIincluding IIvarious IIformats, IIand IIit IIis IIreadily IIusable IIby IIanalytics IItools IIand IIalgorithms II- II II II IIFalse T IIor IIF: IIDuring IIdata IItransformation, IIdepending IIon IIthe IIcontext IIand IIpurpose IIof IIpreprocessing IIthe IIdata IIcan IIbe IIrescaled IIto IIa IIfixed IIrange, IIand IInumeric IIvariables IIcan IIbe IIconverted IIto IIcategorical IIvariables II- II II II IITrue T IIor IIF: IIData IIreduction IIcan IIbe IIapplied IIto IIrows II(observations) IIand/or IIcolumns II(variables) IIin IIa IIgiven IIdataset II- II II II IITrue T IIor IIF: IIIn IIdata IIpreprocessing IIstep IIto IIreduce IIthe IIdimension IIof IIdata IIprior IIto IIanalysis, IIsampling IIthe IIrows IIis IImore IIcomplex IIthan IIselecting IIthe IIcolumns II(variables II- II II II IIFalse T IIor IIF: IIChoice IIof IIvisualization IImethod IIthat IImeets IIthe IIpresentation IIrequirements IIfor IIa IIgiven IIdata IIdepends IIon IIthe IIdata IItypes IIavailable, IIpurpose IIof IIthe IIvisual IIand IIcontext II- II II II IITrue Which IIof IIthe IIbelow IIis IInot IIa IIdata IIpreprocessing IIstep? data IIconsolidation data IItransformation data IIseparation data IIreduction II- II II II IIdata IIseperation Which IIof IIthe IIbelow IIis IIa IImethod IIto IIdeal IIwith IIfilling IIout IIthe IImissing IIvalues IIin IIdata? data IIcleaning data IIreduction data IIsmoothing data IIimputation II- II II II IIdata IIimputations Which IIof IIthe IIbelow IIstatement(s) IIis/are IIcorrect? A: IIAn IIimportant IIdata IItransformation IIsubtask IIis IIto IIselect IIthe IIrelevant IIdata IIusing IIdomain IIexpert IIinput, IIi.e., IIdecide IIwhich IIsources IIand IIdata IIto IIcollect. B: IIWhen IImerging IItwo IIdata IIsource IItables IIA IIand IIB, IIusing IIthe IIfull IIouter IIjoin IImethod IIeliminates IIall IIrows IIfrom IIthe IIresulting IItable IIthat IIdo IInot IIhave IIcorresponding IIrows IIin IIboth IIsource IItables IIA IIand IIB. C: IIFor IInumerical IIvariables, IInormalizing IIthe IIobserved IIvalues IIbetween IItwo IIvalues, IIsuch IIas II0 IIand II1, IIallows IIto IIrescale IIthe IIvalues IIand IIcompare IIvariables IIwith IIdifferent IImeans IIand/or IIstandard IIdeviations IIon IIa IIsingle IIscale. D: IIIdentifying IIand IIreducing IInoise IIin IIthe IIdata IIis IIa IIsubtask IIof IIdata IIreduction. II- II II II IIC When IIanalyzing IIthe IIoriginal IIdata IIof IIhousehold IIincome IIof IIa IIselected IIpopulation, IIanalysts IInotice IIthat II5% IIof IIobserva tions IIare IImissing IIand IIentered IIin IIthe IIdataset IIas IIN/A II(not IIavailable). IIFurther, IIthey IInotice IIthat IIthere IIare IIa IIfew IIextremely IIlow IIhousehold IIincome IIvalues. IIWhich IIof IIthe IIfollowing IImethod(s) IIwould IIbe IIwell-suited IIto IIprepare IIthe