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.

Introduction Introduction One problem that can arise in \exploratory" multiple regression studies is which predictors from a set of potential predictor variables should be included in the multiple regression

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