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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 / 29 Selecting Variables in Multiple Regression1 Introduction2 The Problem with RedundancyCollinearity and Variances of Beta Estimates3 Detecting and Dealing with Redundancy4 Classic Selection ProceduresThe Akaike Information Criterion (AIC)The Bayesian Information Criterion(BIC)Cross-Validation Based CriteriaAn Example The Highway DataForward SelectionBackward EliminationStepwise Regression5 Computational Examples6 Caution about Selection MethodsJames H. Steiger (Vanderbilt University) Selecting Variables in Multiple Regression2 / 29 IntroductionIntroductionOne 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 approach
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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