penalty - A look at using logistic regression to control...

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A look at using logistic regression to control for Simpson’s paradox and aggregated tables. Death Penalty Example revisited. The SAS System 14:13 Thursday, June 12, 2008 2 The LOGISTIC Procedure Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 228.513 230.291 SC 228.459 230.183 -2 Log L 226.513 226.291 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 0.2215 1 0.6379 Score 0.2214 1 0.6379 Wald 0.2211 1 0.6382 Type 3 Analysis of Effects Wald Effect DF Chi-Square Pr > ChiSq race 1 0.2211 0.6382 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 1 -2.0043 0.2444 67.2647 <.0001 race 1 1 -0.1664 0.3539 0.2211 0.6382 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits race 1 vs 0 0.847 0.423 1.694
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14:13 Thursday, June 12, 2008 3 The LOGISTIC Procedure Association of Predicted Probabilities and Observed Responses Percent Concordant 16.7
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This note was uploaded on 07/25/2008 for the course STT 422 taught by Professor Porter during the Summer '08 term at Michigan State University.

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penalty - A look at using logistic regression to control...

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