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Lecture 10 All Possible Regressions and Statistics for Comparing Models

# Lecture 10 All Possible Regressions and Statistics for Comparing Models

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Unformatted text preview: I OWA S TATE U NIVERSITY Department of Animal Science I OWA S TATE U NIVERSITY Department of Animal Science All Possible Regressions and Statistics for Comparing Models Animal Science 500 Lecture No. 12 October 12, 2010 I OWA S TATE U NIVERSITY Department of Animal Science Example analysis ◆ The RSQUARE Procedure RECALL ◆ The RSQUARE procedure selects optimal subsets of independent variables in a multiple regression analysis I OWA S TATE U NIVERSITY Department of Animal Science Example analysis PROC RSQUARE options; MODEL dependents = independents / options; (options can appear in either PROC RSQUARE or any MODEL statement). ■ SELECT = n specific maximum number of subset models ■ INCLUDE = I requests that the first I variables after the equal sign be included in every regression ■ SIGMA = n specifies the true standard deviation of I OWA S TATE U NIVERSITY Department of Animal Science Example analysis PROC RSQUARE options; MODEL dependents = independents / options; (options can appear in either PROC RSQUARE or any MODEL statement). PROC RSQUARE DATA=name OUTEST=EST ADJRSQ MSE CP; SELECT=n; MODEL = variable list; I OWA S TATE U NIVERSITY Department of Animal Science Example analysis PROC PRINT DATA=EST; PROC PLOT; PLOT _CP_*_P_ = ‘C’ _P_*_P_ = ‘P’ / OVERLAY; PLOT _MSE_*_P_ = ‘M’; Run; Quit I OWA S TATE U NIVERSITY Department of Animal Science PROC STEPWISE The STEPWISE procedure provides five methods for stepwise regression. General form: PROC STEPWISE; MODEL dependents = independents / options; Run; Quit; ** Assumes that you have at least one dependent variable and 2 or more independent variables. If only one independent variable exists then you are just doing a simple regression of x on y or y on x. I OWA S TATE U NIVERSITY Department of Animal Science Types of Regression ◆ Uses of PROC REG for standard problems:...
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Lecture 10 All Possible Regressions and Statistics for Comparing Models

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