BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)

Everyyear,theDirectorateofStudentAffairsofITBconductsaTracerStudy surveyofITBgraduateswiththeaimofprovidingrecommendationstoITBin improvingtheeducationsystemandquality.Thesurveydatacanbeprocessed using apredictivemodelwithmachinelearningapproach,oneofwhichispredicting ITB alumni’ssalariesastheir...

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Main Author:	Alibasyah Wiriaatmadja, Nur
Format:	Final Project
Language:	Indonesia
Online Access:	https://digilib.itb.ac.id/gdl/view/65209
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Institution:	Institut Teknologi Bandung
Language:	Indonesia

id	id-itb.:65209
spelling	id-itb.:652092022-06-21T11:58:19ZBAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021) Alibasyah Wiriaatmadja, Nur Indonesia Final Project Multiple Linear Regression,Posterior Distribution,Metropolis-Hastings Algorithm, Salary Prediction,ITB Graduates INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/65209 Everyyear,theDirectorateofStudentAffairsofITBconductsaTracerStudy surveyofITBgraduateswiththeaimofprovidingrecommendationstoITBin improvingtheeducationsystemandquality.Thesurveydatacanbeprocessed using apredictivemodelwithmachinelearningapproach,oneofwhichispredicting ITB alumni’ssalariesastheircareerbuildingmilestoneaftercompletingtheir education atITB.ThisFinalProjectaimstobuildaMultipleLinearRegression model topredictthesalaryofITBGraduatesusingthe2021ITBTracerStudydata whose columnshavebeenselected.Thedatausedinthisstudyconsistedof1815 observationsoffourteenpredictorvariablesandsalaryasresponsevariable.To simplify themodelingprocessandtofulfillmodelassumptions,datapreprocessing wasconductedsothatthedatareducedto1803observationsconsistingofeight predictor variablesandoneresponsevariable.Furthermore,theMultipleLinear RegressionmodelwassolvedbyusingBayesianstatisticalapproachtoapproximate the posteriordistributionoftheparametermodel.Metropolis-HastingsAlgorithm are usedasnumericalmethodwhichrequirestheparameterspriordistributionand the likelihooddistributionofthedataasinput.Theposteriordistributionobtained has ameanthatisquiteclosetotheestimatedparametervaluesobtainedusingthe Ordinary LeastSquaremethodasfrequentiststatisticalapproach.Samplingcanbe done fromtheposteriordistributiontomakepredictionsifanewdatasetisknown. Prediction canbedonerepeatedlyusingdifferentsetofparametersobtainedfrom sampling, sothatahistogramofpredictedvaluescanbegeneratedthatismore informativethansinglepredictionusingtheOrdinaryLeastSquaremethod.The proximity ofprediction’smeanfromthetruevaluearevariative. text
institution	Institut Teknologi Bandung
building	Institut Teknologi Bandung Library
continent	Asia
country	Indonesia Indonesia
content_provider	Institut Teknologi Bandung
collection	Digital ITB
language	Indonesia
description	Everyyear,theDirectorateofStudentAffairsofITBconductsaTracerStudy surveyofITBgraduateswiththeaimofprovidingrecommendationstoITBin improvingtheeducationsystemandquality.Thesurveydatacanbeprocessed using apredictivemodelwithmachinelearningapproach,oneofwhichispredicting ITB alumni’ssalariesastheircareerbuildingmilestoneaftercompletingtheir education atITB.ThisFinalProjectaimstobuildaMultipleLinearRegression model topredictthesalaryofITBGraduatesusingthe2021ITBTracerStudydata whose columnshavebeenselected.Thedatausedinthisstudyconsistedof1815 observationsoffourteenpredictorvariablesandsalaryasresponsevariable.To simplify themodelingprocessandtofulfillmodelassumptions,datapreprocessing wasconductedsothatthedatareducedto1803observationsconsistingofeight predictor variablesandoneresponsevariable.Furthermore,theMultipleLinear RegressionmodelwassolvedbyusingBayesianstatisticalapproachtoapproximate the posteriordistributionoftheparametermodel.Metropolis-HastingsAlgorithm are usedasnumericalmethodwhichrequirestheparameterspriordistributionand the likelihooddistributionofthedataasinput.Theposteriordistributionobtained has ameanthatisquiteclosetotheestimatedparametervaluesobtainedusingthe Ordinary LeastSquaremethodasfrequentiststatisticalapproach.Samplingcanbe done fromtheposteriordistributiontomakepredictionsifanewdatasetisknown. Prediction canbedonerepeatedlyusingdifferentsetofparametersobtainedfrom sampling, sothatahistogramofpredictedvaluescanbegeneratedthatismore informativethansinglepredictionusingtheOrdinaryLeastSquaremethod.The proximity ofprediction’smeanfromthetruevaluearevariative.
format	Final Project
author	Alibasyah Wiriaatmadja, Nur
spellingShingle	Alibasyah Wiriaatmadja, Nur BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)
author_facet	Alibasyah Wiriaatmadja, Nur
author_sort	Alibasyah Wiriaatmadja, Nur
title	BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)
title_short	BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)
title_full	BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)
title_fullStr	BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)
title_full_unstemmed	BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)
title_sort	bayesianlinearregressionforpredictingitb alumniâs income(casestudyofitbtracer study2021)
url	https://digilib.itb.ac.id/gdl/view/65209
_version_	1822932675604774912

BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)

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BAYESIANLINEARREGRESSIONFORPREDICTINGITB ALUMNIâS INCOME(CASESTUDYOFITBTRACER STUDY2021)