Selecting Variables in Multiple Regression - Statpower

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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.

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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