SPSS Inc. SPSS Regression 17.0

67 Pages·0.569 MB·English

i
SPSS Regression 17.0
FormoreinformationaboutSPSSInc.softwareproducts,pleasevisitourWebsiteathttp://www.spss.comorcontact
SPSSInc.
233SouthWackerDrive,11thFloor
Chicago,IL60606-6412
Tel: [phone]
Fax: [phone]
SPSSisaregisteredtrademarkandtheotherproductnamesarethetrademarksofSPSSInc.foritsproprietarycomputer
software. Nomaterialdescribingsuchsoftwaremaybeproducedordistributedwithoutthewrittenpermissionofthe
ownersofthetrademarkandlicenserightsinthesoftwareandthecopyrightsinthepublishedmaterials.
TheSOFTWAREanddocumentationareprovidedwithRESTRICTEDRIGHTS.Use,duplication,ordisclosureby
theGovernmentissubjecttorestrictionsassetforthinsubdivision(c)(1)(ii)ofTheRightsinTechnicalDataand
ComputerSoftwareclauseat52.227-7013. Contractor/manufacturerisSPSSInc.,233SouthWackerDrive,11th
Floor,Chicago,IL60606-6412.
PatentNo. 7,023,453
Generalnotice:Otherproductnamesmentionedhereinareusedforidentificationpurposesonlyandmaybetrademarks
oftheirrespectivecompanies.
WindowsisaregisteredtrademarkofMicrosoftCorporation.
Apple,Mac,andtheMaclogoaretrademarksofAppleComputer,Inc.,registeredintheU.S.andothercountries.
ThisproductusesWinWrapBasic,Copyright1993-2007,PolarEngineeringandConsulting,http://www.winwrap.com.
PrintedintheUnitedStatesofAmerica.
Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmitted,inanyformorbyanymeans,
electronic,mechanical,photocopying,recording,orotherwise,withoutthepriorwrittenpermissionofthepublisher.
Preface
SPSSStatistics17.0isacomprehensivesystemforanalyzingdata. TheRegression
optionaladd-onmoduleprovidestheadditionalanalytictechniquesdescribedinthis
manual. TheRegressionadd-onmodulemustbeusedwiththeSPSSStatistics17.0
Basesystemandiscompletelyintegratedintothatsystem.
Installation
ToinstalltheRegressionadd-onmodule,runtheLicenseAuthorizationWizardusing
theauthorizationcodethatyoureceivedfromSPSSInc. Formoreinformation,seethe
installationinstructionssuppliedwiththeRegressionadd-onmodule.
Compatibility
SPSSStatisticsisdesignedtorunonmanycomputersystems. Seetheinstallation
instructionsthatcamewithyoursystemforspecificinformationonminimumand
recommended requirements.
SerialNumbers
YourserialnumberisyouridentificationnumberwithSPSSInc. Youwillneedthis
serialnumberwhenyoucontactSPSSInc. forinformationregardingsupport,payment,
oranupgradedsystem. TheserialnumberwasprovidedwithyourBasesystem.
CustomerService
Ifyouhaveanyquestionsconcerningyourshipmentoraccount,contactyourlocal
office,listedontheWebsiteathttp://www.spss.com/worldwide. Pleasehaveyour
serialnumberreadyforidentification.
iii
TrainingSeminars
SPSSInc. providesbothpublicandonsitetrainingseminars. Allseminarsfeature
hands-onworkshops. Seminarswillbeofferedinmajorcitiesonaregularbasis.
Formoreinformationontheseseminars,contactyourlocaloffice,listedontheWeb
siteathttp://www.spss.com/worldwide.
TechnicalSupport
TechnicalSupportservicesareavailabletomaintenancecustomers. Customersmay
contactTechnicalSupportforassistanceinusingSPSSStatisticsorforinstallation
helpforoneofthesupportedhardwareenvironments. ToreachTechnicalSupport,
seetheWebsiteathttp://www.spss.com, orcontactyourlocaloffice, listedonthe
Websiteathttp://www.spss.com/worldwide. Bepreparedtoidentifyyourself,your
organization,andtheserialnumberofyoursystem.
AdditionalPublications
TheSPSSStatisticalProceduresCompanion,byMarijaNorušis,hasbeenpublished
by PrenticeHall. A new versionofthisbook, updatedfor SPSSStatistics17.0,
is planned. The SPSS Advanced Statistical Procedures Companion, also based
on SPSS Statistics 17.0, is forthcoming. The SPSS Guide to Data Analysis for
SPSS Statistics 17.0 is also in development. Announcements of publications
available exclusively through Prentice Hall will be available on the Web site at
http://www.spss.com/estore(selectyourhomecountry,andthenclickBooks).
iv
Contents
1 Choosing a Procedure for Binary Logistic
Regression 1
2 Logistic Regression 3
LogisticRegressionSetRule.. ... ... ... ... ... ... ... ... ... ... ... ... 6
LogisticRegressionVariableSelectionMethods.. ... ... ... ... ... ... ... 6
LogisticRegressionDefineCategoricalVariables. ... ... ... ... ... ... ... 8
LogisticRegressionSaveNewVariables . ... ... ... ... ... ... ... ... ... 9
LogisticRegressionOptions .. ... ... ... ... ... ... ... ... ... ... ... .. 11
LOGISTICREGRESSIONCommandAdditionalFeatures... ... ... ... ... .. 12
3 Multinomial Logistic Regression 13
MultinomialLogisticRegression.. ... ... ... ... ... ... ... ... ... ... .. 15
BuildTerms.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 16
MultinomialLogisticRegressionReferenceCategory. ... ... ... ... ... .. 17
MultinomialLogisticRegressionStatistics ... ... ... ... ... ... ... ... .. 18
MultinomialLogisticRegressionCriteria.. ... ... ... ... ... ... ... ... .. 20
MultinomialLogisticRegressionOptions.. ... ... ... ... ... ... ... ... .. 21
MultinomialLogisticRegressionSave. ... ... ... ... ... ... ... ... ... .. 23
NOMREGCommandAdditionalFeatures.. ... ... ... ... ... ... ... ... .. 24
v
4 Probit Analysis 25
ProbitAnalysisDefineRange . ... ... ... ... ... ... ... ... ... ... ... .. 28
ProbitAnalysisOptions... ... ... ... ... ... ... ... ... ... ... ... ... .. 28
PROBITCommandAdditionalFeatures... ... ... ... ... ... ... ... ... .. 29
5 Nonlinear Regression 31
ConditionalLogic(NonlinearRegression). ... ... ... ... ... ... ... ... .. 33
NonlinearRegressionParameters ... ... ... ... ... ... ... ... ... ... .. 34
NonlinearRegressionCommonModels .. ... ... ... ... ... ... ... ... .. 35
NonlinearRegressionLossFunction.. ... ... ... ... ... ... ... ... ... .. 36
NonlinearRegressionParameterConstraints. ... ... ... ... ... ... ... .. 37
NonlinearRegressionSaveNewVariables... ... ... ... ... ... ... ... .. 38
NonlinearRegressionOptions ... ... ... ... ... ... ... ... ... ... ... .. 39
InterpretingNonlinearRegressionResults ... ... ... ... ... ... ... ... .. 40
NLRCommandAdditionalFeatures... ... ... ... ... ... ... ... ... ... .. 40
6 Weight Estimation 42
WeightEstimationOptions ... ... ... ... ... ... ... ... ... ... ... ... .. 45
WLSCommandAdditionalFeatures .. ... ... ... ... ... ... ... ... ... .. 45
7 Two-Stage Least-Squares Regression 46
Two-StageLeast-SquaresRegressionOptions ... ... ... ... ... ... ... .. 48
2SLSCommandAdditionalFeatures .. ... ... ... ... ... ... ... ... ... .. 49
vi
Appendix
A Categorical Variable Coding Schemes 50
Deviation . ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 50
Simple ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 51
Helmert .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 52
Difference ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 52
Polynomial ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 53
Repeated . ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 54
Special... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 55
Indicator.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 56
Index 57
vii

Chapter
1
Choosing a Procedure for Binary
Logistic Regression
BinarylogisticregressionmodelscanbefittedusingeithertheLogisticRegression
procedureortheMultinomialLogisticRegressionprocedure. Eachprocedurehas
optionsnotavailableintheother. Animportanttheoreticaldistinctionisthatthe
LogisticRegressionprocedureproducesallpredictions,residuals,influencestatistics,
andgoodness-of-fittestsusingdataattheindividualcaselevel,regardlessofhow
thedataareenteredandwhetherornotthenumberofcovariatepatternsissmaller
thanthetotalnumberofcases,whiletheMultinomialLogisticRegressionprocedure
internallyaggregatescasestoformsubpopulationswithidenticalcovariatepatterns
forthepredictors,producingpredictions,residuals,andgoodness-of-fittestsbasedon
thesesubpopulations. Ifallpredictorsarecategoricaloranycontinuouspredictorstake
ononlyalimitednumberofvalues—sothatthereareseveralcasesateachdistinct
covariatepattern—thesubpopulationapproachcanproducevalidgoodness-of-fittests
andinformativeresiduals,whiletheindividualcaselevelapproachcannot.
LogisticRegressionprovidesthefollowinguniquefeatures:
(cid:132) Hosmer-Lemeshowtestofgoodnessoffitforthemodel
(cid:132) Stepwise analyses
(cid:132) Contraststodefinemodelparameterization
(cid:132) Alternativecutpointsforclassification
(cid:132) Classification plots
(cid:132) Modelfittedononesetofcasestoaheld-outsetofcases
(cid:132) Savespredictions,residuals,andinfluencestatistics
MultinomialLogisticRegressionprovidesthefollowinguniquefeatures:
(cid:132) Pearsonanddeviancechi-squaretestsforgoodnessoffitofthemodel
1
2
Chapter 1
(cid:132) Specificationofsubpopulationsforgroupingofdataforgoodness-of-fittests
(cid:132) Listingofcounts,predictedcounts,andresidualsbysubpopulations
(cid:132) Correctionofvarianceestimatesforover-dispersion
(cid:132) Covariancematrixoftheparameterestimates
(cid:132) Testsoflinearcombinationsofparameters
(cid:132) Explicitspecificationofnestedmodels
(cid:132) Fit1-1matchedconditionallogisticregressionmodelsusingdifferencedvariables