hw14 - logistic logistic regression hw 14 16:14 Wednesday,...

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logistic regression hw 14 16:14 Wednesday, December 8, 2010 1 The LOGISTIC Procedure Model Information Data Set WORK.AUTOMOBILE Response Variable y Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 20 Number of Observations Used 20 Response Profile Ordered Value y Total Frequency 1 0 10 2 1 10 Probability modeled is y=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 29.726 27.082 SC 30.722 30.069 -2 Log L 27.726 21.082 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 6.6444 2 0.0361 Score 5.2627 2 0.0720 Wald 3.8086 2 0.1489
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logistic regression hw 14 16:14 Wednesday, December 8, 2010 2 The LOGISTIC Procedure Analysis of Maximum Likelihood Estimates Parameter DF Estimate Standard Error Wald Chi-Square Pr > ChiSq Intercept 1 -7.0468 4.6742 2.2729 0.1317 x1 1 0.000074 0.000064 1.3423 0.2466 x2 1 0.9879 0.5274 3.5089 0.0610 Odds Ratio Estimates Effect Point Estimate 95% Wald Confidence Limits x1 1.000 1.000 1.000 x2 2.685 0.955 7.549 Association of Predicted Probabilities and Observed Responses Percent Concordant 77.0 Somers' D 0.550 Percent Discordant 22.0 Gamma 0.556 Percent Tied 1.0 Tau-a 0.289 Pairs 100 c 0.775
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logistic regression hw 14 16:14 Wednesday, December 8, 2010 3 The LOGISTIC Procedure Regression Diagnostics Covariates Pearson Residual Deviance Residual Hat Matrix Diagonal Case Number x1 x2 Value (1 unit = 0.25) -8 -4 0 2 4 6 8 Value (1 unit = 0.22) -8 -4 0 2 4 6 8 Value (1 unit = 0.03) 0 2 4 6 8 12 16 1 45000.0 2.0000 -0.4170 | * | | -0.5662 | * | | 0.1433 | * | 2 40000.0 4.0000 -0.9311 | * | | -1.1174 | * | | 0.0998 | * | 3 60000.0 3.0000 0.8413 | | * | 1.0346 | | * | 0.1501 | * | 4 50000.0 2.0000 1.9941
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hw14 - logistic logistic regression hw 14 16:14 Wednesday,...

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