Logistic regression for 2x2 table 1 The LOGISTIC Procedure Model Information Data Set WORK.SMOKE Response Variable (Events) y Response Variable (Trials) n Number of Observations 2 Model binary logit Optimization Technique Fisher's scoring Response Profile Ordered Binary Total Value Outcome Frequency 1 Event 1004 2 Nonevent 4371 Class Level Information Design Variables Class Value 1 s nosmoke 0 smoke 1 Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Deviance and Pearson Goodness-of-Fit Statistics Criterion DF Value Value/DF Pr > ChiSq Deviance 0 0.0000 . . Pearson 0 0.0000 . . Number of events/trials observations: 2 Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 5178.510 5151.390 SC 5185.100 5164.569 -2 Log L 5176.510 5147.390 Logistic regression for 2x2 table 2 The LOGISTIC Procedure Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 29.1207 1 <.0001 Score 27.6766 1 <.0001 Wald 27.3361 1 <.0001 Type III Analysis of Effects Wald Effect DF Chi-Square Pr > ChiSq s 1 27.3361 <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 1 -1.8266 0.0786 540.2949 <.0001 s smoke 1 0.4592 0.0878 27.3361 <.0001 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits s smoke vs nosmoke 1.583 1.332 1.880 Association of Predicted Probabilities and Observed Responses Percent Concordant 21.7 Somers' D 0.080 Percent Discordant 13.7 Gamma 0.226 Percent Tied 64.6 Tau-a 0.024 Pairs 4388484 c 0.540 Logistic regression for 2x2 table 3 Obs s y n prob 1 smoke 816 4019 0.20304 2 nosmoke 188 1356 0.13864 Logistic regression for 2x3 table 4 The LOGISTIC Procedure Model Information Data Set WORK.SMOKE Response Variable (Events) y Response Variable (Trials) n Number of Observations 3 Model binary logit Optimization Technique Fisher's scoring Response Profile Ordered Binary Total Value Outcome Frequency 1 Event 1004 2 Nonevent 4371 Class Level Information Design Variables Class Value 1 2 s both 1 0 one 0 1 neither 0 0 Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Deviance and Pearson Goodness-of-Fit Statistics Criterion DF Value Value/DF Pr > ChiSq Deviance 0 0.0000 . . Pearson 0 0.0000 . . Number of events/trials observations: 3 Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 5178.510 5144.144 SC 5185.100 5163.913 -2 Log L 5176.510 5138.144 Logistic regression for 2x3 table 5 The LOGISTIC Procedure Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 38.3658 2 <.0001 Score 37.5663 2 <.0001 Wald 37.0861 2 <.0001 Type III Analysis of Effects Wald Effect DF Chi-Square Pr > ChiSq s 2 37.0861 <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 1 -1.8266 0.0786 540.2949 <.0001 s both 1 0.5882 0.0970 36.8105 <.0001 s one 1 0.3491 0.0955 13.3481 0.0003 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits s both vs neither 1.801 1.489 2.178 s one vs neither 1.418 1.176 1.710 Association of Predicted Probabilities and Observed Responses Percent Concordant 38.3 Somers' D 0.115 Percent Discordant 26.8 Gamma 0.177 Percent Tied 34.9 Tau-a 0.035 Pairs 4388484 c 0.558 Partition for the Hosmer and Lemeshow Test Event Nonevent Group Total Observed Expected Observed Expected 1 1356 188 188.00 1168 1168.00 2 2239 416 416.00 1823 1823.00 3 1780 400 400.00 1380 1380.00 Logistic regression for 2x3 table 6 The LOGISTIC Procedure Hosmer and Lemeshow Goodness-of-Fit Test Chi-Square DF Pr > ChiSq 0.0000 1 1.0000 Logistic regression for 2x3 table 7 Obs s y n prob 1 both 400 1780 0.22472 2 one 416 2239 0.18580 3 neither 188 1356 0.13864 Logistic regression fro 2x3 tables with residuals 8 The LOGISTIC Procedure Model Information Data Set WORK.SMOKE Response Variable (Events) y Response Variable (Trials) n Number of Observations 3 Model binary logit Optimization Technique Fisher's scoring Response Profile Ordered Binary Total Value Outcome Frequency 1 Event 1004 2 Nonevent 4371 Class Level Information Design Variables Class Value 1 2 s 0 0 0 1 1 0 2 0 1 Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Deviance and Pearson Goodness-of-Fit Statistics Criterion DF Value Value/DF Pr > ChiSq Deviance 0 0.0000 . . Pearson 0 0.0000 . . Number of events/trials observations: 3 Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 5178.510 5144.144 SC 5185.100 5163.913 -2 Log L 5176.510 5138.144 Logistic regression fro 2x3 tables with residuals 9 The LOGISTIC Procedure Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 38.3658 2 <.0001 Score 37.5663 2 <.0001 Wald 37.0861 2 <.0001 Type III Analysis of Effects Wald Effect DF Chi-Square Pr > ChiSq s 2 37.0861 <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 1 -1.8266 0.0786 540.2949 <.0001 s 1 1 0.3491 0.0955 13.3481 0.0003 s 2 1 0.5882 0.0970 36.8105 <.0001 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits s 1 vs 0 1.418 1.176 1.710 s 2 vs 0 1.801 1.489 2.178 Association of Predicted Probabilities and Observed Responses Percent Concordant 38.3 Somers' D 0.115 Percent Discordant 26.8 Gamma 0.177 Percent Tied 34.9 Tau-a 0.035 Pairs 4388484 c 0.558 Partition for the Hosmer and Lemeshow Test Event Nonevent Group Total Observed Expected Observed Expected 1 1356 188 188.00 1168 1168.00 2 2239 416 416.00 1823 1823.00 3 1780 400 400.00 1380 1380.00 Logistic regression fro 2x3 tables with residuals 10 The LOGISTIC Procedure Hosmer and Lemeshow Goodness-of-Fit Test Chi-Square DF Pr > ChiSq 0.0000 1 1.0000 Logistic regression diagnostics for 2x3 table 11 Obs s y n prob shat fhat pearson deviance 1 2 400 1780 0.22472 400.000 1380.00 -.000000031 0 2 1 416 2239 0.18580 416.000 1823.00 -3.6617E-15 0 3 0 188 1356 0.13864 188.000 1168.00 -.000001291 -.000001307