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1、TeamControlNumberForofficeuseonlyForofficeuseonlyT1________________44952F1________________T2________________F2________________T3________________F3________________ProblemChosenT4________________F4________________COneinahundred--TheGoodgrantChallengeSummaryTheGoodgrantFo
2、undationwantstodonatemoneytoanappropriategroupofschoolsperyear,wewereaskedtodevelopamodeltodetermineanoptimalinvestmentstrategy.Firstly,filteringdata.Becausetheproblemhasalargenumberofadditionaldata,wemustpreliminarilyclassifythedata.WeuseSPSStoachieveabovepurpose,afte
3、rthatwegetthevaliddataof2936potentialcandidateschools.Secondly,usingGreyRelationalAnalysis(GRA)tooptimizethedataroughly.ByusingMATLABwecalculatethegreyrelationalcoefficientandgreyweightrelation.Andthenwecomparethesizeofeverygreyweightrelation,takingthefirst300potential
4、candidateschoolsastheevaluationobjects.Thirdly,usingPrincipalComponentAnalysis(PCA)tobuilduptheROI(returnoninvestment)Model.DuringtheprocessofGRA,therearesomanyindexesinfluencetheresult,wecan’tgettheaccuratesolution.Soweuse“PCA”methodtosimplifytheproblemandevaluatetheR
5、OIofeveryschool.ByusingSPSS,wedeterminethemaincomponentsandcalculatethecomprehensivescore.Weranktheevaluationobjectsonthebasisofscoreandchoosethefirst40objectsastheoptimizationresult.Andthen,testingthereliability.WestandardizetheindexesofPCA,bycomparingtheoptimizationr
6、esultwithwell-knownuniversitieswhichwedon’tchoose(suchasHarvardUniversity),wecanexaminetherationalityofourmodelandgetthecandidateslist.Finally,usingTOPSISmethodtobuilduptheInvestmentPredictionModel.Weusethepreviousdata(2009~2013)tomaketheLinearRegressionForecasting(LRF
7、),afterthatwegetthepredicteddatafrom2016to2020.WeuseTOPSISmethodtoevaluatethecandidateschoollist.Accordingtothecomprehensivescorefrom2016to2020,wefindthefirst20schoolswhichgetthehigherscorearealwayshigherthanotherschoolfrom2016to2020.Sowechoosethat20schoolwhichbothhave
8、higherROIanddevelopmentpotentialasthefinalcandidateschoollist.Accordingtothefinalcandidateschoollist,weusetheTOPSISme