30Nov2011 - CR Long C532 Regression Modeling 30 Nov 2011 Learning Objectives 1 To learn methods of selecting the best regression model Methods for

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CR Long C532: Regression Modeling 30 Nov 2011 1 Learning Objectives 1. To learn methods of selecting the “best” regression model. Methods for choosing the explanatory variables for the linear regression model. Purpose : want to find the smallest subset of explanatory variables that provides the “best fit” to the observed data. This “best” subset of explanatory variables should include those that are most important in explaining the variation in the outcome variable. There is rarely (if ever) one “best” model, so we choose between a few of the “best.” Assess all possible models by using hierarchical partitioning : Quantifies the independent contribution of each explanatory variable with the outcome variable by partitioning the explained variance (R 2 ) into components. Steps: 1. Choose several possible “best” subsets of explanatory variables 2. Compare between models by examining the change in R 2 Use partial F-tests to test the hypothesis H 0 : change in R 2 =0 for the addition of explanatory variables
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This note was uploaded on 01/03/2012 for the course C 532 taught by Professor Long during the Fall '11 term at Palmer Chiropractic.

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30Nov2011 - CR Long C532 Regression Modeling 30 Nov 2011 Learning Objectives 1 To learn methods of selecting the best regression model Methods for

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