Selecting Variables in Multiple Regression - …
Selecting Variables in Multiple RegressionJames H. SteigerDepartment of Psychology and Human DevelopmentVanderbilt UniversityJames H. Steiger (Vanderbilt University) Selecting Variables in Multiple Regression1 / 29Selecting Variables in Multiple Regression1Introduction2The Problem with RedundancyCollinearity and Variances of Beta Estimates3Detecting and Dealing with Redundancy4Classic Selection ProceduresThe Akaike Information Criterion (AIC)The Bayesian Information Criterion(BIC)Cross-Validation Based CriteriaAn Example The Highway DataForward SelectionBackward EliminationStepwise Regression5Computational Examples6Caution about Selection MethodsJames H. Steiger (Vanderbilt University) Selecting Variables in Multiple Regression2 / 29IntroductionIntroductionOne problem that can arise in exploratory Multiple Regression studies is which predictorsfrom a set of potential predictor Variables should be included in the Multiple regressionanalysis, and in the ultimate prediction this module, we review some traditional and newer approaches to var
Selecting Variables in Multiple Regression 1 Introduction 2 The Problem with Redundancy Collinearity and Variances of Beta Estimates 3 Detecting and Dealing with Redundancy 4 Classic Selection Procedures The Akaike Information Criterion (AIC) The Bayesian Information Criterion(BIC)
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