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ISYE 6414 - Midterm Exam Questions And Answers.

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ISYE 6414 - Midterm Exam Questions And Answers.ISYE 6414 - Midterm Exam Questions And Answers.ISYE 6414 - Midterm Exam Questions And Answers.ISYE 6414 - Midterm Exam Questions And Answers.ISYE 6414 - Midterm Exam Questions And Answers.ISYE 6414 - Midterm Exam Questions And Answers.ISYE 6414 - Midte...

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  • September 7, 2024
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  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • ISYE 6414
  • ISYE 6414
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Wisdoms
ISYE 6414 - Midterm Exam Questions And
Answers




Regression iAnalysis i- icorrect iAnswers i✔✔ i-Regression ianalysis iis ia isimple iway ito iinvestigate ithe
irelationship ibetween i2 ior imore ivariables iin ia inon-deterministic iway.


Response/Target iVariable i(Y) i- icorrect iAnswers i✔✔ i-This iis ia ivariable iwe're iinterested iin
iunderstanding, imodeling ior itesting i




This iis ia irandom ivariable. iIt ivaries iwith ichanges iin ithe ipredictor(s)

2. iPredicting/Explanatory i(independent) iVariables(Xs i~ iX1, iX2) i- icorrect iAnswers i✔✔ i-These iare
ivariables iwe ithink imight ibe iuseful iin ipredicting ior imodeling ithe iresponse ivariable i




This iis ia ifixed ivariable. iIt idoes inot ichange iwith ithe iresponse

Simple iLinear iRegression i- icorrect iAnswers i✔✔ i-We ihave ia istraight iline iwhich idoesn't ifit iperfectly
ito ithe ipoints




The iobjective iis ito ifit ia inon-deterministic ilinear imodel ibetween ithe ipredicting ivariable iand iY. i



In isimple ilinear iregression, iwe ihave i3 iparameters ito iestimate.

Multiple iLinear iRegression i- icorrect iAnswers i✔✔ i-We ican ihave ia iplane iif iwe ihave itwo ipredictions

Polynomial iRegression i- icorrect iAnswers i✔✔ i-We iare icapturing ia inonlinear irelationship

Objectives iof iLinear iRegression i- icorrect iAnswers i✔✔ i-1. iPrediction: iWe iwant ito isee ihow ithe
iresponse ivariable ibehaves iin idifferent isettings i

, 2. iModeling: iWe iare iinterested iin imodeling ithe irelationship ibetween ithe iresponse ivariable iand ithe
iexplanatory/predicting ivariables




3. iTesting: iWe iare ialso iinterested iin itesting ithe ihypotheses iof iassociation irelationships.

Simple iLinear iRegression iAssumptions i- icorrect iAnswers i✔✔ i-• iLinearity/Mean iZero iAssumption:
iThis imeans ithat ithe iexpected ivalue iof ithe ierrors iis izero




• iConstant iVariance iAssumption: iThis imeans ithat ithe ivariance iof ithe ierror iterm iis iequal ito
isigma_squared iis ithe isame iacross iall ierror iterms




• iIndependence iAssumption: iThis imeans ithat ithe ierror iterms iare iindependent irandom ivariables ii.e.
ideviances i(response ivariables iYs) iare iindependently idrawn ifrom ithe idata igenerating iprocess i-- iit
icannot ibe itrue ithat ithe imodel iunder-predicts iY ifor ione iparticular icase itells iyou ianything ior iall
iabout iwhat iit idoes ifor iany iother icase




• iNormal iAssumption: iThe ierrors iare iassumed ito ibe inormally idistributed.

Linearity iAssumption i- icorrect iAnswers i✔✔ i-A iviolation iof ithis iassumption iwill ilead ito idifficulties iin
iestimating i0 iand imeans ithat iyour imodel idoes inot iinclude ia inecessary isystematic icomponent


Constant iVariance iAssumption i- icorrect iAnswers i✔✔ i-This imeans ithat ithe imodel icannot ibe imore
iaccurate iin isome iparts iand iless iaccurate iin iother iparts iof ithe imodel. iThe ivariance ihas ito ibe
iconstant.




iA iviolation iof ithis iassumption imeans ithat ithe iestimates iare inot ias iefficient ias ithey icould ibe iin
iestimating ithe itrue iparameters iand ibetter iestimates ican ibe icalculated ialso iresults iin ipoorly
icalibrated iprediction iintervals


Independence iAssumption i- icorrect iAnswers i✔✔ i-It icannot ibe itrue ithat ithe imodel iunder-predicts iY.
iOne iparticular icase idoesn't itell iyou ianything ior iall iabout iwhat iit idoes ifor iany iother icase




This iviolation imost ioften ioccurs iin idata ithat iare iordered iin itime i(time iseries idata) iwhere iareas ithat
iare inear ieach iother iin itime iare isimilar ito ieach iother. i

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