100% satisfaction guarantee Immediately available after payment Both online and in PDF No strings attached
logo-home
Previously searched by you
Previously searched by you
logo
ISYE 6414 – Final COMPLETELY SOLVED GRADED A 2024. $12.49   Add to cart
Quickly navigate to
Preview
  2.  
  3. ISYE 6414
  4.  
  5. ISYE 6414

Exam (elaborations)

ISYE 6414 – Final COMPLETELY SOLVED GRADED A 2024.
 0 view  0 purchase
  • Course
  • ISYE 6414
  • Institution
  • ISYE 6414

ISYE 6414 – Final COMPLETELY SOLVED GRADED A 2024.ISYE 6414 – Final COMPLETELY SOLVED GRADED A 2024.ISYE 6414 – Final COMPLETELY SOLVED GRADED A 2024.ISYE 6414 – Final COMPLETELY SOLVED GRADED A 2024.ISYE 6414 – Final COMPLETELY SOLVED GRADED A 2024.ISYE 6414 – Final COMPLETELY SOLVED G...

[Show more]

Preview 3 out of 30  pages

Document information

  • September 7, 2024
  • 30
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers

Subjects

  • isye 6414 final completely solved graded a 2024

Written for

  • ISYE 6414
  • ISYE 6414

Seller

avatar-seller
Wisdoms

Reviews received

Content preview

ISYE 6414 – Final COMPLETELY SOLVED
GRADED A 2024




Logistic iRegression i- i(correct ianswer) i-Commonly iused ifor imodeling ibinary iresponse idata. iThe
iresponse ivariable iis ia ibinary ivariable, iand ithus, inot inormally idistributed. i




In ilogistic iregression, iwe imodel ithe iprobability iof ia isuccess, inot ithe iresponse ivariable. iIn ithis
imodel, iwe ido inot ihave ian ierror iterm


g-function i- i(correct ianswer) i-We ilink ithe iprobability iof isuccess ito ithe ipredicting ivariables iusing ithe
ig ilink ifunction. iThe ig ifunction iis ithe is-shape ifunction ithat imodels ithe iprobability iof isuccess iwith
irespect ito ithe ipredicting ivariables




The ilink ifunction ig iis ithe ilog iof ithe iratio iof ip iover ione iminus ip, iwhere ip iagain iis ithe iprobability iof
isuccess




Logit ifunction i(log iodds ifunction) iof ithe iprobability iof isuccess iis ia ilinear imodel iin ithe ipredicting
ivariables




The iprobability iof isuccess iis iequal ito ithe iratio ibetween ithe iexponential iof ithe ilinear icombination iof
ithe ipredicting ivariables iover i1 iplus ithis isame iexponential


Odds iof ia isuccess i- i(correct ianswer) i-This iis ithe iexponential iof ithe iLogit ifunction

Logistic iRegression iAssumptions i- i(correct ianswer) i-Linearity: iThe irelationship ibetween ithe ig iof ithe
iprobability iof isuccess iand ithe ipredicted ivariable, iis ia ilinear ifunction. i

,Independence: iThe iresponse ibinary ivariables iare iindependently iobserved



Logit: iThe ilogistic iregression imodel iassumes ithat ithe ilink ifunction ig iis ia ilogit ifunction

Linearity iAssumption i- i(correct ianswer) i-The iLogit itransformation iof ithe iprobability iof isuccess iis ia
ilinear icombination iof ithe ipredicting ivariables. iThe irelationship imay inot ibe ilinear, ihowever, iand
itransformation imay iimprove ithe ifit




The ilinearity iassumption ican ibe ievaluated iby iplotting ithe ilogit iof ithe isuccess irate iversus ithe
ipredicting ivariables. i




If ithere's ia icurvature ior isome inon-linear ipattern, iit imay ibe ian iindication ithat ithe ilack iof ifit imay ibe
idue ito ithe inon-linearity iwith irespect ito isome iof ithe ipredicting ivariables


Logistic iRegression iCoefficient i- i(correct ianswer) i-We iinterpret ithe iregression icoefficient ibeta ias ithe
ilog iof ithe iodds iratio ifor ian iincrease iof ione iunit iin ithe ipredicting ivariable




We ido inot iinterpret ibeta iwith irespect ito ithe iresponse ivariable ibut iwith irespect ito ithe iodds iof
isuccess




The iestimators ifor ithe iregression icoefficients iin ilogistic iregression iare iunbiased iand ithus ithe imean
iof ithe iapproximate inormal idistribution iis ibeta. iThe ivariance iof ithe iestimator idoes inot ihave ia
iclosed iform iexpression


Model iparameters i- i(correct ianswer) i-The imodel iparameters iare ithe iregression icoefficients. i



There iis ino iadditional iparameter ito imodel ithe ivariance isince ithere's ino ierror iterm. i



For iP ipredictors, iwe ihave iP i+ i1 iregression icoefficients ifor ia imodel iwith iintercept i(beta i0).



We iestimate ithe imodel iparameters iusing ithe imaximum ilikelihood iestimation iapproach

, Response ivariable i- i(correct ianswer) i-The iresponse idata iare iBernoulli ior ibinomial iwith ione itrial iwith
iprobability iof isuccess


MLE i- i(correct ianswer) i-The iresulting ilog-likelihood ifunction ito ibe imaximized, iis ivery icomplicated
iand iit iis inon-linear iin ithe iregression icoefficients ibeta i0, ibeta i1, iand ibeta ip




MLE ihas igood istatistical iproperties iunder ithe iassumption iof ia ilarge isample isize ii.e. ilarge iN



For ilarge iN, ithe isampling idistribution iof iMLEs ican ibe iapproximated iby ia inormal idistribution



The ileast isquare iestimation ifor ithe istandard iregression imodel iis iequivalent iwith iMLE, iunder ithe
iassumption iof inormality.




MLE iis ithe imost iapplied iestimation iapproach

Parameter iestimation i- i(correct ianswer) i-Maximizing ithe ilog ilikelihood ifunction iwith irespect ito
ibeta0, ibeta1 ietc iin iclosed i(exact) iform iexpression iis inot ipossible ibecause ithe ilog ilikelihood ifunction
iis ia inon-linear ifunction iin ithe imodel iparameters ii.e. iwe icannot iderive ithe iestimated iregression
icoefficients iin ian iexact iform




Use inumerical ialgorithm ito iestimate ibetas i(maximize ithe ilog ilikelihood ifunction). iThe iestimated
iparameters iand itheir istandard ierrors iare iapproximate iestimates


Binomial iData i- i(correct ianswer) i-This iis ibinary idata iwith irepititions

Marginal iRelationship i- i(correct ianswer) i-Capturing ithe iassociation iof ia ipredicting ivariable ito ithe
iresponse ivariable iwithout iconsideration iof iother ifactors


Conditional iRelationship i- i(correct ianswer) i-Capturing ithe iassociation ioof ia ipredicting ivariable ito ithe
iresponse ivariable iconditional iof iother ipredicting ivariables iin ithe imodel


Simpson's iparadox i- i(correct ianswer) i-This iis iwhen ithe iaddition iof ia ipredictive ivariable ireverses ithe
isign ion ithe icoefficients iof ian iexisting iparameter




It irefers ito ireversal iof ian iassociation iwhen ilooking iat ia imarginal irelationship iversus ia ipartial ior
iconditional ione. iThis iis ia isituation iwhere ithe imarginal irelationship iadds ia iwrong isign

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

Quick and easy check-out

You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.

Focus on what matters

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller Wisdoms. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for $12.49. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

72042 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy study notes for 14 years now

Start selling
$12.49
  • (0)
  Add to cart