Exam (elaborations)
ISYE 6414 - MIDTERM 1 PREP QUESTIONS AND 100% CORRECT ANSWERS 2024
ISYE 6414 - MIDTERM 1 PREP QUESTIONS AND 100% CORRECT ANSWERS 2024
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1. Exam (elaborations) - Isye 6414 - all units questions and 100% correct answers 2024
2. Exam (elaborations) - Isye 6414 final exam review questions and 100% correct answers 2024
3. Exam (elaborations) - Isye 6414 midterm prep questions and 100% correct answers 2024
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5. Exam (elaborations) - Isye 6414 - unit 1 flashcards questions and 100% correct answers 2024
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8. Exam (elaborations) - Isye 6414 - midterm 1 prep questions and 100% correct answers 2024
9. Exam (elaborations) - Isye 6414 midterm 1 study notes questions and answers with solutions 2024
10. Exam (elaborations) - Isye 6414 - unit 5 questions and 100% correct answer 2024
11. Exam (elaborations) - Isye 6414 - midterm exam 3 questions and 100% correct answers 2024
12. Exam (elaborations) - Isye 6414 – homeworks questions and 100% correct answers 2024
13. Exam (elaborations) - Isye6414 (regression) midterm 2 questions and 100% correct answers 2024
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ISYE 6414 - MIDTERM 1 PREP
QUESTIONS AND 100% CORRECT
ANSWERS 2024
IfPλ=1P-PANSWERPwePdoPnotPtransform
non-deterministicP-
PANSWERPRegressionPanalysisPisPonePofPthePsimplestPwaysPwePhavePinPstatisticsPtoPinvestigatePthePrelations
hipPbetweenPtwoPorPmorePvariablesPinPaP___Pway
randomP-
PANSWERPThePresponsePvariablePisPaP___Pvariable,PbecausePitPvariesPwithPchangesPinPthePpredictingPvariabl
e,PorPwithPotherPchangesPinPthePenvironment
fixedP-PANSWERPThePpredictingPvariablePisPaP___Pvariable.PItPisPsetPfixed,PbeforePthePresponsePisPmeasured.
simplePlinearPregressionP-
PANSWERPregressionPanalysisPinvolvingPonePindependentPvariablePandPonePdependentPvariablePinPwhichPt
hePrelationshipPbetweenPthePvariablesPisPapproximatedPbyPaPstraightPline
MultiplePLinearPRegressionP-
PANSWERPAPstatisticalPmethodPusedPtoPmodelPthePrelationshipPbetweenPonePdependentP(orPresponse)Pvari
ablePandPtwoPorPmorePindependentP(orPexplanatory)PvariablesPbyPfittingPaPlinearPequationPtoPobservedPdat
a
polynomialPregressionP-
PANSWERPaPregressionPmodelPwhichPdoesPnotPassumePaPlinearPrelationship;PaPcurvilinearPcorrelationPcoeffi
cientPisPcomputedP(wePcanPthinkPofPXPandPX-squaredPasPtwoPdifferentPpredictingPvariables)
threePobjectivesPinPregressionP-PANSWERP1)PPrediction
2)PModeling
3)PTestingPhypothesis
,PredictionP-
PANSWERPWePwantPtoPseePhowPthePresponsePvariablePbehavesPinPdifferentPsettings.PForPexample,PforPaPdiff
erentPlocation,PifPwePthinkPaboutPaPgeographicPprediction,PorPinPtime,PifPwePthinkPaboutPtemporalPpredictio
n
ModelingP-
PANSWERPmodelingPthePrelationshipPbetweenPthePresponsePvariablePandPthePexplanatoryPvariables,PorPpre
dictingPvariables
TestingPhypothesesP-PANSWERPofPassociationPrelationships
usefulPrepresentationPofPrealityP-
PANSWERPWePdoPnotPbelievePthatPthePlinearPmodelPrepresentsPaPtruePrepresentationPofPreality.PRather,Pwe
PthinkPthat,Pperhaps,PitPprovidesPaP___
β0P-PANSWERPinterceptPparameterP(thePvaluePatPwhichPthePlinePintersectsPthePy-axis)
β1P-PANSWERPslopePparameterP(slopePofPthePlinePweParePtryingPtoPfit)
epsilonP(ε)P-PANSWERPisPthePdeviancePofPthePdataPfromPthePlinearPmodel
toPfindPβ0PandPβ1P-
PANSWERPtoPfindPthePlinePthatPdescribesPaPlinearPrelationship,PsuchPthatPwePfitPthisPmodel.
simplePlinearPregressionPdataPstructureP-
PANSWERPpairsPofPdataPconsistingPofPaPvaluePforPthePresponsePvariable,andPaPvaluePforPthePpredictingPvaria
ble.PAndPwePhavePnPsuchPpairs
modelingPframeworkPforPthePsimplePlinearPregression:P-PANSWERP1)PidentifyingPdataPstructure
2)PclearlyPstatingPthePmodelPassumptions
linearPregressionPassumptionsP-PANSWERP1)Plinearity
, 2)PconstantPvariancePassumption
3)PindependencePassumption
linearityPassumptionP-
PANSWERPmeanPzeroPassumption,PmeansPthatPthePexpectedPvaluePofPthePerrorsPisPzero.
APviolationPofPthisPassumptionPwillPleadPtoPdifficultiesPinPestimatingPβ0,PandPmeansPthatPyourPmodelPdoesP
notPincludePaPnecessaryPsystematicPcomponent.
constantPvariancePassumptionP-
PANSWERPwhichPmeansPthatPthePvarianceP(σ^2)PofPthePerrorPtermsPorPdeviancesPisPconstantPforPthePgivenP
population.PAPviolationPofPthisPassumptionPmeansPthatPthePestimatesParePnotPasPefficientPasPtheyPcouldPbeP
inPestimatingPthePtruePparameters
IndependencePAssumptionP-
PANSWERPwhichPmeansPthatPthePdeviancesParePindependentPrandomPvariables.
ViolationPofPthisPassumptionPcanPleadPtoPmisleadingPassessmentsPofPthePstrengthPofPthePregression.
normalityPassumptionP-
PANSWERPerrorsP(ε)ParePnormallyPdistributed.PThisPisPneededPforPstatisticalPinference,PforPexample,Pconfide
ncePorPpredictionPintervals,PandPhypothesisPtesting.PIfPthisPassumptionPisPviolated,PhypothesisPtestsPandPco
nfidencePandPpredictionPintervalsPcanPbePmisleading.v
thirdPparameterP-PANSWERPthePvariancePofPthePerrorPtermsP(σ^2)
OnePapproachPisPtoPminimizePthePsumPofPsquaredPresidualsPorPerrorsPwithPrespectPtoPβ0PandPβ1.PThisPtrans
latedPintoPfindingPthePlinePsuchPthatPthePtotalPsquaredPdeviancesPfromPthePlinePisPminimum.P-
PANSWERPHowPcanPwePgetPestimatesPofPthePregressionPcoefficientsPorPparametersPinPlinear
regressionPanalysis?
fittedPvaluesP-PANSWERPtoPbePthePregressionPlinePwherePthePparametersParePreplaced
byPthePestimatedPvaluesPofPthePparameters.
ResidualsP-PANSWERParePsimplyPthePdifference