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ISYE 6414 FINAL EXAM REVIEW QUESTIONS AND 100% CORRECT ANSWERS 2024
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ISYE 6414 FINAL EXAM REVIEW QUESTIONS AND 100% CORRECT ANSWERS 2024
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ISYE 6414 FINAL EXAM REVIEWQUESTIONS AND 100% CORRECT
ANSWERS 2024
LeastLSquareLEliminationL(LSE)LcannotLbeLappliedLtoLGLMLmodels.L-LANSWERLFalseL-
LitLisLapplicableLbutLdoesLnotLuseLdataLdistributionLinformationLfully.
InLmultipleLlinearLregressionLwithLiddLandLequalLvariance,LtheLleastLsquaresLestimationLofLregressionLcoeffic
ientsLareLalwaysLunbiased.L-LANSWERLTrueL-
LtheLleastLsquaresLestimatesLareLBLUEL(BestLLinearLUnbiasedLEstimates)LinLmultipleLlinearLregression.
MaximumLLikelihoodLEstimationLisLnotLapplicableLforLsimpleLlinearLregressionLandLmultipleLlinearLregressio
n.L-LANSWERLFalseL-LInLSLRLandLMLR,LtheLSLELandLMLELareLtheLsameLwithLnormalLiddLdata.
TheLbackwardLeliminationLrequiresLaLpre-setLprobabilityLofLtypeLIILerrorL-LANSWERLFalseL-LTypeLILerror
TheLfirstLdegreeLofLfreedomLinLtheLFLdistributionLforLanyLofLtheLthreeLproceduresLinLstepwiseLisLalwaysLequa
lLtoLone.L-LANSWERLTrue
MLELisLusedLforLtheLGLMsLforLhandlingLcomplicatedLlinkLfunctionLmodelingLinLtheLX-YLrelationship.L-
LANSWERLTrue
InLtheLGLMsLtheLlinkLfunctionLcannotLbeLaLnonLlinearLregression.L-LANSWERLFalseL-
LItLcanLbeLlinear,LnonLlinear,LorLparametric
WhenLtheLp-valueLofLtheLslopeLestimateLinLtheLSLRLisLsmallLtheLr-squaredLbecomesLsmallerLtoo.L-
LANSWERLFalseL-
LWhenLPLvalueLisLsmall,LtheLmodelLfitsLbecomeLmoreLsignificantLandLRLsquaredLbecomeLlarger.
InLGLMsLtheLmainLreasonLoneLdoesLnotLuseLLSELtoLestimateLmodelLparametersLisLtheLpotentialLconstrainedL
inLtheLparameters.L-LANSWERLFalseL-
LTheLpotentialLconstraintLinLtheLparametersLofLGLMsLisLhandledLbyLtheLlinkLfunction.
, TheLR-squaredLandLadjustedLR-
squaredLareLnotLappropriateLmodelLcomparisonsLforLnonLlinearLregressionLbutLareLforLlinearLregressionLmo
dels.L-LANSWERLTRUEL-LTheLunderlyingLassumptionLofLR-
squaredLcalculationsLisLthatLyouLareLfittingLaLlinearLmodel.
TheLdecisionLinLusingLANOVALtableLforLtestingLwhetherLaLmodelLisLsignificantLdependsLonLtheLnormalLdistri
butionLofLtheLresponseLvariableL-LANSWERLTrue
WhenLtheLdataLmayLnotLbeLnormallyLdistributed,LAICLisLmoreLappropriateLforLvariableLselectionLthanLadjust
edLR-squaredL-LANSWERLTrue
TheLslopeLofLaLlinearLregressionLequationLisLanLexampleLofLaLcorrelationLcoefficient.L-LANSWERLFalseL-
LtheLcorrelationLcoefficientLisLtheLrLvalue.LWillLhaveLtheLsameL+LorL-LsignLasLtheLslope.
InLmultipleLlinearLregression,LasLtheLvalueLofLR-squaredLincreases,LtheLrelationship
betweenLpredictorsLbecomesLstrongerL-LANSWERLFalseL-
LrLsquaredLmeasuresLhowLmuchLvariabilityLisLexplainedLbyLtheLmodel,LNOTLhowLstrongLtheLpredictorsLare.
WhenLdealingLwithLaLmultipleLlinearLregressionLmodel,LanLadjustedLR-squaredLcan
beLgreaterLthanLtheLcorrespondingLunadjustedLR-SquaredLvalue.L-LANSWERLFalseL-
LtheLadjustedLrsquaredLvalueLtakeLtheLnumberLandLtypesLofLpredictorsLintoLaccount.LItLisLlowerLthanLtheLrLsq
uaredLvalue.
InLaLmultipleLregressionLproblem,LaLquantitativeLinputLvariableLxLisLreplacedLbyLxL−
mean(x).LTheLR-squaredLforLtheLfittedLmodelLwillLbeLtheLsameL-LANSWERLTrue
TheLestimatedLcoefficientsLofLaLregressionLlineLisLpositive,LwhenLtheLcoefficientLof
determinationLisLpositive.L-LANSWERLFalseL-LrLsquaredLisLalwaysLpositive.
IfLtheLoutcomeLvariableLisLquantitativeLandLallLexplanatoryLvariablesLtakeLvaluesL0Lor
1,LaLlogisticLregressionLmodelLisLmostLappropriate.L-LANSWERLFalseL-
LMoreLresearchLisLnecessaryLtoLdetermineLtheLcorrectLmodel.
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