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Results: Binary Logistic Regression

The LOGISTIC Procedure

Model Information

Model Information
Data Set MYLIB.SURVEYMONKEY  
Response Variable Price Price
Number of Response Levels 2  
Model binary logit  
Optimization Technique Fisher's scoring  

Observations Summary

Number of Observations Read 135
Number of Observations Used 135

Response Profile

Response Profile
Ordered
Value
Price Total
Frequency
1 0 15
2 1 120

Probability modeled is Price='1'.

Convergence Status

Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.

Fit Statistics

Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 96.185 88.767
SC 99.090 117.820
-2 Log L 94.185 68.767

Global Tests

Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 25.4175 9 0.0025
Score 23.1796 9 0.0058
Wald 15.2308 9 0.0848

Parameter Estimates

Analysis of Maximum Likelihood Estimates
Parameter DF Estimate Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept 1 1.0001 2.5494 0.1539 0.6948
Age 1 0.00549 0.0751 0.0053 0.9417
Price per night 1 -0.0128 0.00465 7.5461 0.0060
Safety 1 -0.1077 0.2251 0.2287 0.6325
Travel Frequency 1 0.2404 0.1366 3.0994 0.0783
Close to tourist att 1 -0.00255 0.9361 0.0000 0.9978
Housekeeping 1 -4.0055 1.5191 6.9523 0.0084
Number of Rooms 1 -0.8505 1.3237 0.4128 0.5205
Hotel Rewards 1 -1.0492 0.8848 1.4061 0.2357
Count 1 0.9945 0.3786 6.9014 0.0086

Odds Ratios

Odds Ratio Estimates
Effect Point Estimate 95% Wald
Confidence Limits
Age 1.006 0.868 1.165
Price per night 0.987 0.978 0.996
Safety 0.898 0.578 1.396
Travel Frequency 1.272 0.973 1.662
Close to tourist att 0.997 0.159 6.247
Housekeeping 0.018 <0.001 0.358
Number of Rooms 0.427 0.032 5.719
Hotel Rewards 0.350 0.062 1.984
Count 2.703 1.287 5.677

Odds Ratios with 95% Wald Confidence Limits

 Odds Ratio Estimate = 2.7033 
 COMPBL(_EFFECT) = Count  Odds Ratio Estimate = 0.3502 
 COMPBL(_EFFECT) = Hotel Rewards  Odds Ratio Estimate = 0.4272 
 COMPBL(_EFFECT) = Number of Rooms  Odds Ratio Estimate = 0.0182 
 COMPBL(_EFFECT) = Housekeeping  Odds Ratio Estimate = 0.9975 
 COMPBL(_EFFECT) = Close to tourist att  Odds Ratio Estimate = 1.2718 
 COMPBL(_EFFECT) = Travel Frequency  Odds Ratio Estimate = 0.8979 
 COMPBL(_EFFECT) = Safety  Odds Ratio Estimate = 0.9873 
 COMPBL(_EFFECT) = Price per night  Odds Ratio Estimate = 1.0055 
 COMPBL(_EFFECT) = Age  COMPBL(_EFFECT) = Count 
 Upper = 5.677 
 Lower = 1.2873  COMPBL(_EFFECT) = Count 
 Upper = 5.677 
 Lower = 1.2873  COMPBL(_EFFECT) = Count 
 Upper = 5.677 
 Lower = 1.2873  COMPBL(_EFFECT) = Hotel Rewards 
 Upper = 1.9838 
 Lower = 0.0618  COMPBL(_EFFECT) = Hotel Rewards 
 Upper = 1.9838 
 Lower = 0.0618  COMPBL(_EFFECT) = Hotel Rewards 
 Upper = 1.9838 
 Lower = 0.0618  COMPBL(_EFFECT) = Number of Rooms 
 Upper = 5.7193 
 Lower = 0.0319  COMPBL(_EFFECT) = Number of Rooms 
 Upper = 5.7193 
 Lower = 0.0319  COMPBL(_EFFECT) = Number of Rooms 
 Upper = 5.7193 
 Lower = 0.0319  COMPBL(_EFFECT) = Housekeeping 
 Upper = 0.3577 
 Lower = .00093  COMPBL(_EFFECT) = Housekeeping 
 Upper = 0.3577 
 Lower = .00093  COMPBL(_EFFECT) = Housekeeping 
 Upper = 0.3577 
 Lower = .00093  COMPBL(_EFFECT) = Close to tourist att 
 Upper = 6.2469 
 Lower = 0.1593  COMPBL(_EFFECT) = Close to tourist att 
 Upper = 6.2469 
 Lower = 0.1593  COMPBL(_EFFECT) = Close to tourist att 
 Upper = 6.2469 
 Lower = 0.1593  COMPBL(_EFFECT) = Close to tourist att 
 Upper = 6.2469 
 Lower = 0.1593  COMPBL(_EFFECT) = Travel Frequency 
 Upper = 1.6621 
 Lower = 0.9731  COMPBL(_EFFECT) = Travel Frequency 
 Upper = 1.6621 
 Lower = 0.9731  COMPBL(_EFFECT) = Travel Frequency 
 Upper = 1.6621 
 Lower = 0.9731  COMPBL(_EFFECT) = Safety 
 Upper = 1.3959 
 Lower = 0.5776  COMPBL(_EFFECT) = Safety 
 Upper = 1.3959 
 Lower = 0.5776  COMPBL(_EFFECT) = Safety 
 Upper = 1.3959 
 Lower = 0.5776  COMPBL(_EFFECT) = Price per night 
 Upper = 0.9963 
 Lower = 0.9783  COMPBL(_EFFECT) = Age 
 Upper = 1.1649 
 Lower = 0.8679  COMPBL(_EFFECT) = Age 
 Upper = 1.1649 
 Lower = 0.8679  COMPBL(_EFFECT) = Age 
 Upper = 1.1649 
 Lower = 0.8679  X = 1  COMPBL(_EFFECT) = Count  COMPBL(_EFFECT) = Hotel Rewards  COMPBL(_EFFECT) = Number of Rooms  COMPBL(_EFFECT) = Housekeeping  COMPBL(_EFFECT) = Close to tourist att  COMPBL(_EFFECT) = Travel Frequency  COMPBL(_EFFECT) = Safety  COMPBL(_EFFECT) = Price per night  COMPBL(_EFFECT) = Age
Plot of Odds Ratios with 95% Wald Confidence Limits

Association Statistics

Association of Predicted Probabilities and Observed Responses
Percent Concordant 84.2 Somers' D 0.686
Percent Discordant 15.7 Gamma 0.686
Percent Tied 0.1 Tau-a 0.136
Pairs 1800 c 0.843

ROC Curve

 Model = Model  (0.8428) 
 Sensitivity = 1 
 1 - Specificity = 1 
 Frequency = 1 
 Positive Predictive Value = 0.8889 
 Negative Predictive Value = . 
 Probability Level = 0.1248  Model = Model  (0.8428) 
 Sensitivity = 1 
 1 - Specificity = 0.9333 
 Frequency = 1 
 Positive Predictive Value = 0.8955 
 Negative Predictive Value = 1 
 Probability Level = 0.2106  Model = Model  (0.8428) 
 Sensitivity = 1 
 1 - Specificity = 0.9333 
 Frequency = 1 
 Positive Predictive Value = 0.8955 
 Negative Predictive Value = 1 
 Probability Level = 0.2106  Model = Model  (0.8428) 
 Sensitivity = 1 
 1 - Specificity = 0.8667 
 Frequency = 1 
 Positive Predictive Value = 0.9023 
 Negative Predictive Value = 1 
 Probability Level = 0.3069  Model = Model  (0.8428) 
 Sensitivity = 1 
 1 - Specificity = 0.8667 
 Frequency = 1 
 Positive Predictive Value = 0.9023 
 Negative Predictive Value = 1 
 Probability Level = 0.3069  Model = Model  (0.8428) 
 Sensitivity = 1 
 1 - Specificity = 0.8 
 Frequency = 1 
 Positive Predictive Value = 0.9091 
 Negative Predictive Value = 1 
 Probability Level = 0.4429  Model = Model  (0.8428) 
 Sensitivity = 1 
 1 - Specificity = 0.8 
 Frequency = 1 
 Positive Predictive Value = 0.9091 
 Negative Predictive Value = 1 
 Probability Level = 0.4429  Model = Model  (0.8428) 
 Sensitivity = 0.9917 
 1 - Specificity = 0.8 
 Frequency = 1 
 Positive Predictive Value = 0.9084 
 Negative Predictive Value = 0.75 
 Probability Level = 0.5001  Model = Model  (0.8428) 
 Sensitivity = 0.9917 
 1 - Specificity = 0.8 
 Frequency = 1 
 Positive Predictive Value = 0.9084 
 Negative Predictive Value = 0.75 
 Probability Level = 0.5001  Model = Model  (0.8428) 
 Sensitivity = 0.9833 
 1 - Specificity = 0.8 
 Frequency = 1 
 Positive Predictive Value = 0.9077 
 Negative Predictive Value = 0.6 
 Probability Level = 0.5346  Model = Model  (0.8428) 
 Sensitivity = 0.9833 
 1 - Specificity = 0.8 
 Frequency = 1 
 Positive Predictive Value = 0.9077 
 Negative Predictive Value = 0.6 
 Probability Level = 0.5346  Model = Model  (0.8428) 
 Sensitivity = 0.9833 
 1 - Specificity = 0.7333 
 Frequency = 1 
 Positive Predictive Value = 0.9147 
 Negative Predictive Value = 0.6667 
 Probability Level = 0.5761  Model = Model  (0.8428) 
 Sensitivity = 0.9833 
 1 - Specificity = 0.7333 
 Frequency = 1 
 Positive Predictive Value = 0.9147 
 Negative Predictive Value = 0.6667 
 Probability Level = 0.5761  Model = Model  (0.8428) 
 Sensitivity = 0.9833 
 1 - Specificity = 0.6667 
 Frequency = 1 
 Positive Predictive Value = 0.9219 
 Negative Predictive Value = 0.7143 
 Probability Level = 0.6088  Model = Model  (0.8428) 
 Sensitivity = 0.9833 
 1 - Specificity = 0.6667 
 Frequency = 1 
 Positive Predictive Value = 0.9219 
 Negative Predictive Value = 0.7143 
 Probability Level = 0.6088  Model = Model  (0.8428) 
 Sensitivity = 0.975 
 1 - Specificity = 0.6667 
 Frequency = 2 
 Positive Predictive Value = 0.9213 
 Negative Predictive Value = 0.625 
 Probability Level = 0.6273  Model = Model  (0.8428) 
 Sensitivity = 0.975 
 1 - Specificity = 0.6667 
 Frequency = 2 
 Positive Predictive Value = 0.9213 
 Negative Predictive Value = 0.625 
 Probability Level = 0.6273  Model = Model  (0.8428) 
 Sensitivity = 0.9667 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.928 
 Negative Predictive Value = 0.6 
 Probability Level = 0.639  Model = Model  (0.8428) 
 Sensitivity = 0.9667 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.928 
 Negative Predictive Value = 0.6 
 Probability Level = 0.639  Model = Model  (0.8428) 
 Sensitivity = 0.9583 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9274 
 Negative Predictive Value = 0.5455 
 Probability Level = 0.6521  Model = Model  (0.8428) 
 Sensitivity = 0.9583 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9274 
 Negative Predictive Value = 0.5455 
 Probability Level = 0.6521  Model = Model  (0.8428) 
 Sensitivity = 0.95 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9268 
 Negative Predictive Value = 0.5 
 Probability Level = 0.673  Model = Model  (0.8428) 
 Sensitivity = 0.95 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9268 
 Negative Predictive Value = 0.5 
 Probability Level = 0.673  Model = Model  (0.8428) 
 Sensitivity = 0.9417 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9262 
 Negative Predictive Value = 0.4615 
 Probability Level = 0.7096  Model = Model  (0.8428) 
 Sensitivity = 0.9417 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9262 
 Negative Predictive Value = 0.4615 
 Probability Level = 0.7096  Model = Model  (0.8428) 
 Sensitivity = 0.9333 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9256 
 Negative Predictive Value = 0.4286 
 Probability Level = 0.7223  Model = Model  (0.8428) 
 Sensitivity = 0.9333 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9256 
 Negative Predictive Value = 0.4286 
 Probability Level = 0.7223  Model = Model  (0.8428) 
 Sensitivity = 0.925 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.925 
 Negative Predictive Value = 0.4 
 Probability Level = 0.7419  Model = Model  (0.8428) 
 Sensitivity = 0.925 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.925 
 Negative Predictive Value = 0.4 
 Probability Level = 0.7419  Model = Model  (0.8428) 
 Sensitivity = 0.9167 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9244 
 Negative Predictive Value = 0.375 
 Probability Level = 0.7504  Model = Model  (0.8428) 
 Sensitivity = 0.9167 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9244 
 Negative Predictive Value = 0.375 
 Probability Level = 0.7504  Model = Model  (0.8428) 
 Sensitivity = 0.9083 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9237 
 Negative Predictive Value = 0.3529 
 Probability Level = 0.7737  Model = Model  (0.8428) 
 Sensitivity = 0.9083 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9237 
 Negative Predictive Value = 0.3529 
 Probability Level = 0.7737  Model = Model  (0.8428) 
 Sensitivity = 0.9 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9231 
 Negative Predictive Value = 0.3333 
 Probability Level = 0.785  Model = Model  (0.8428) 
 Sensitivity = 0.9 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9231 
 Negative Predictive Value = 0.3333 
 Probability Level = 0.785  Model = Model  (0.8428) 
 Sensitivity = 0.8917 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9224 
 Negative Predictive Value = 0.3158 
 Probability Level = 0.7907  Model = Model  (0.8428) 
 Sensitivity = 0.8917 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9224 
 Negative Predictive Value = 0.3158 
 Probability Level = 0.7907  Model = Model  (0.8428) 
 Sensitivity = 0.8833 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9217 
 Negative Predictive Value = 0.3 
 Probability Level = 0.8025  Model = Model  (0.8428) 
 Sensitivity = 0.8833 
 1 - Specificity = 0.6 
 Frequency = 1 
 Positive Predictive Value = 0.9217 
 Negative Predictive Value = 0.3 
 Probability Level = 0.8025  Model = Model  (0.8428) 
 Sensitivity = 0.8833 
 1 - Specificity = 0.5333 
 Frequency = 2 
 Positive Predictive Value = 0.9298 
 Negative Predictive Value = 0.3333 
 Probability Level = 0.803  Model = Model  (0.8428) 
 Sensitivity = 0.8833 
 1 - Specificity = 0.5333 
 Frequency = 2 
 Positive Predictive Value = 0.9298 
 Negative Predictive Value = 0.3333 
 Probability Level = 0.803  Model = Model  (0.8428) 
 Sensitivity = 0.875 
 1 - Specificity = 0.4667 
 Frequency = 1 
 Positive Predictive Value = 0.9375 
 Negative Predictive Value = 0.3478 
 Probability Level = 0.8072  Model = Model  (0.8428) 
 Sensitivity = 0.875 
 1 - Specificity = 0.4667 
 Frequency = 1 
 Positive Predictive Value = 0.9375 
 Negative Predictive Value = 0.3478 
 Probability Level = 0.8072  Model = Model  (0.8428) 
 Sensitivity = 0.875 
 1 - Specificity = 0.4 
 Frequency = 1 
 Positive Predictive Value = 0.9459 
 Negative Predictive Value = 0.375 
 Probability Level = 0.8115  Model = Model  (0.8428) 
 Sensitivity = 0.875 
 1 - Specificity = 0.4 
 Frequency = 1 
 Positive Predictive Value = 0.9459 
 Negative Predictive Value = 0.375 
 Probability Level = 0.8115  Model = Model  (0.8428) 
 Sensitivity = 0.8667 
 1 - Specificity = 0.4 
 Frequency = 1 
 Positive Predictive Value = 0.9455 
 Negative Predictive Value = 0.36 
 Probability Level = 0.817  Model = Model  (0.8428) 
 Sensitivity = 0.8667 
 1 - Specificity = 0.4 
 Frequency = 1 
 Positive Predictive Value = 0.9455 
 Negative Predictive Value = 0.36 
 Probability Level = 0.817  Model = Model  (0.8428) 
 Sensitivity = 0.8583 
 1 - Specificity = 0.4 
 Frequency = 1 
 Positive Predictive Value = 0.945 
 Negative Predictive Value = 0.3462 
 Probability Level = 0.8198  Model = Model  (0.8428) 
 Sensitivity = 0.8583 
 1 - Specificity = 0.4 
 Frequency = 1 
 Positive Predictive Value = 0.945 
 Negative Predictive Value = 0.3462 
 Probability Level = 0.8198  Model = Model  (0.8428) 
 Sensitivity = 0.85 
 1 - Specificity = 0.4 
 Frequency = 2 
 Positive Predictive Value = 0.9444 
 Negative Predictive Value = 0.3333 
 Probability Level = 0.8201  Model = Model  (0.8428) 
 Sensitivity = 0.85 
 1 - Specificity = 0.4 
 Frequency = 2 
 Positive Predictive Value = 0.9444 
 Negative Predictive Value = 0.3333 
 Probability Level = 0.8201  Model = Model  (0.8428) 
 Sensitivity = 0.8333 
 1 - Specificity = 0.4 
 Frequency = 1 
 Positive Predictive Value = 0.9434 
 Negative Predictive Value = 0.3103 
 Probability Level = 0.8274  Model = Model  (0.8428) 
 Sensitivity = 0.8333 
 1 - Specificity = 0.4 
 Frequency = 1 
 Positive Predictive Value = 0.9434 
 Negative Predictive Value = 0.3103 
 Probability Level = 0.8274  Model = Model  (0.8428) 
 Sensitivity = 0.8333 
 1 - Specificity = 0.3333 
 Frequency = 2 
 Positive Predictive Value = 0.9524 
 Negative Predictive Value = 0.3333 
 Probability Level = 0.8341  Model = Model  (0.8428) 
 Sensitivity = 0.8333 
 1 - Specificity = 0.3333 
 Frequency = 2 
 Positive Predictive Value = 0.9524 
 Negative Predictive Value = 0.3333 
 Probability Level = 0.8341  Model = Model  (0.8428) 
 Sensitivity = 0.8167 
 1 - Specificity = 0.3333 
 Frequency = 2 
 Positive Predictive Value = 0.9515 
 Negative Predictive Value = 0.3125 
 Probability Level = 0.8413  Model = Model  (0.8428) 
 Sensitivity = 0.8167 
 1 - Specificity = 0.3333 
 Frequency = 2 
 Positive Predictive Value = 0.9515 
 Negative Predictive Value = 0.3125 
 Probability Level = 0.8413  Model = Model  (0.8428) 
 Sensitivity = 0.8 
 1 - Specificity = 0.3333 
 Frequency = 1 
 Positive Predictive Value = 0.9505 
 Negative Predictive Value = 0.2941 
 Probability Level = 0.8449  Model = Model  (0.8428) 
 Sensitivity = 0.8 
 1 - Specificity = 0.3333 
 Frequency = 1 
 Positive Predictive Value = 0.9505 
 Negative Predictive Value = 0.2941 
 Probability Level = 0.8449  Model = Model  (0.8428) 
 Sensitivity = 0.7917 
 1 - Specificity = 0.3333 
 Frequency = 1 
 Positive Predictive Value = 0.95 
 Negative Predictive Value = 0.2857 
 Probability Level = 0.8545  Model = Model  (0.8428) 
 Sensitivity = 0.7917 
 1 - Specificity = 0.3333 
 Frequency = 1 
 Positive Predictive Value = 0.95 
 Negative Predictive Value = 0.2857 
 Probability Level = 0.8545  Model = Model  (0.8428) 
 Sensitivity = 0.7917 
 1 - Specificity = 0.2667 
 Frequency = 1 
 Positive Predictive Value = 0.9596 
 Negative Predictive Value = 0.3056 
 Probability Level = 0.8656  Model = Model  (0.8428) 
 Sensitivity = 0.7917 
 1 - Specificity = 0.2667 
 Frequency = 1 
 Positive Predictive Value = 0.9596 
 Negative Predictive Value = 0.3056 
 Probability Level = 0.8656  Model = Model  (0.8428) 
 Sensitivity = 0.7917 
 1 - Specificity = 0.2 
 Frequency = 1 
 Positive Predictive Value = 0.9694 
 Negative Predictive Value = 0.3243 
 Probability Level = 0.8661  Model = Model  (0.8428) 
 Sensitivity = 0.7917 
 1 - Specificity = 0.2 
 Frequency = 1 
 Positive Predictive Value = 0.9694 
 Negative Predictive Value = 0.3243 
 Probability Level = 0.8661  Model = Model  (0.8428) 
 Sensitivity = 0.7833 
 1 - Specificity = 0.2 
 Frequency = 1 
 Positive Predictive Value = 0.9691 
 Negative Predictive Value = 0.3158 
 Probability Level = 0.8675  Model = Model  (0.8428) 
 Sensitivity = 0.7833 
 1 - Specificity = 0.2 
 Frequency = 1 
 Positive Predictive Value = 0.9691 
 Negative Predictive Value = 0.3158 
 Probability Level = 0.8675  Model = Model  (0.8428) 
 Sensitivity = 0.775 
 1 - Specificity = 0.2 
 Frequency = 1 
 Positive Predictive Value = 0.9688 
 Negative Predictive Value = 0.3077 
 Probability Level = 0.8686  Model = Model  (0.8428) 
 Sensitivity = 0.775 
 1 - Specificity = 0.2 
 Frequency = 1 
 Positive Predictive Value = 0.9688 
 Negative Predictive Value = 0.3077 
 Probability Level = 0.8686  Model = Model  (0.8428) 
 Sensitivity = 0.7667 
 1 - Specificity = 0.2 
 Frequency = 2 
 Positive Predictive Value = 0.9684 
 Negative Predictive Value = 0.3 
 Probability Level = 0.8755  Model = Model  (0.8428) 
 Sensitivity = 0.7667 
 1 - Specificity = 0.2 
 Frequency = 2 
 Positive Predictive Value = 0.9684 
 Negative Predictive Value = 0.3 
 Probability Level = 0.8755  Model = Model  (0.8428) 
 Sensitivity = 0.75 
 1 - Specificity = 0.2 
 Frequency = 1 
 Positive Predictive Value = 0.9677 
 Negative Predictive Value = 0.2857 
 Probability Level = 0.8862  Model = Model  (0.8428) 
 Sensitivity = 0.75 
 1 - Specificity = 0.2 
 Frequency = 1 
 Positive Predictive Value = 0.9677 
 Negative Predictive Value = 0.2857 
 Probability Level = 0.8862  Model = Model  (0.8428) 
 Sensitivity = 0.75 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9783 
 Negative Predictive Value = 0.3023 
 Probability Level = 0.8881  Model = Model  (0.8428) 
 Sensitivity = 0.75 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9783 
 Negative Predictive Value = 0.3023 
 Probability Level = 0.8881  Model = Model  (0.8428) 
 Sensitivity = 0.7417 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.978 
 Negative Predictive Value = 0.2955 
 Probability Level = 0.8934  Model = Model  (0.8428) 
 Sensitivity = 0.7417 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.978 
 Negative Predictive Value = 0.2955 
 Probability Level = 0.8934  Model = Model  (0.8428) 
 Sensitivity = 0.7333 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9778 
 Negative Predictive Value = 0.2889 
 Probability Level = 0.8992  Model = Model  (0.8428) 
 Sensitivity = 0.7333 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9778 
 Negative Predictive Value = 0.2889 
 Probability Level = 0.8992  Model = Model  (0.8428) 
 Sensitivity = 0.725 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9775 
 Negative Predictive Value = 0.2826 
 Probability Level = 0.9014  Model = Model  (0.8428) 
 Sensitivity = 0.725 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9775 
 Negative Predictive Value = 0.2826 
 Probability Level = 0.9014  Model = Model  (0.8428) 
 Sensitivity = 0.7167 
 1 - Specificity = 0.1333 
 Frequency = 3 
 Positive Predictive Value = 0.9773 
 Negative Predictive Value = 0.2766 
 Probability Level = 0.9032  Model = Model  (0.8428) 
 Sensitivity = 0.7167 
 1 - Specificity = 0.1333 
 Frequency = 3 
 Positive Predictive Value = 0.9773 
 Negative Predictive Value = 0.2766 
 Probability Level = 0.9032  Model = Model  (0.8428) 
 Sensitivity = 0.6917 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9765 
 Negative Predictive Value = 0.26 
 Probability Level = 0.9064  Model = Model  (0.8428) 
 Sensitivity = 0.6917 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9765 
 Negative Predictive Value = 0.26 
 Probability Level = 0.9064  Model = Model  (0.8428) 
 Sensitivity = 0.6833 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9762 
 Negative Predictive Value = 0.2549 
 Probability Level = 0.9082  Model = Model  (0.8428) 
 Sensitivity = 0.6833 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9762 
 Negative Predictive Value = 0.2549 
 Probability Level = 0.9082  Model = Model  (0.8428) 
 Sensitivity = 0.675 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9759 
 Negative Predictive Value = 0.25 
 Probability Level = 0.9123  Model = Model  (0.8428) 
 Sensitivity = 0.675 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9759 
 Negative Predictive Value = 0.25 
 Probability Level = 0.9123  Model = Model  (0.8428) 
 Sensitivity = 0.6667 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9756 
 Negative Predictive Value = 0.2453 
 Probability Level = 0.9147  Model = Model  (0.8428) 
 Sensitivity = 0.6667 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9756 
 Negative Predictive Value = 0.2453 
 Probability Level = 0.9147  Model = Model  (0.8428) 
 Sensitivity = 0.6583 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9753 
 Negative Predictive Value = 0.2407 
 Probability Level = 0.9201  Model = Model  (0.8428) 
 Sensitivity = 0.6583 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9753 
 Negative Predictive Value = 0.2407 
 Probability Level = 0.9201  Model = Model  (0.8428) 
 Sensitivity = 0.65 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.975 
 Negative Predictive Value = 0.2364 
 Probability Level = 0.926  Model = Model  (0.8428) 
 Sensitivity = 0.65 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.975 
 Negative Predictive Value = 0.2364 
 Probability Level = 0.926  Model = Model  (0.8428) 
 Sensitivity = 0.6417 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9747 
 Negative Predictive Value = 0.2321 
 Probability Level = 0.9297  Model = Model  (0.8428) 
 Sensitivity = 0.6417 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9747 
 Negative Predictive Value = 0.2321 
 Probability Level = 0.9297  Model = Model  (0.8428) 
 Sensitivity = 0.6333 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9744 
 Negative Predictive Value = 0.2281 
 Probability Level = 0.9315  Model = Model  (0.8428) 
 Sensitivity = 0.6333 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9744 
 Negative Predictive Value = 0.2281 
 Probability Level = 0.9315  Model = Model  (0.8428) 
 Sensitivity = 0.625 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.974 
 Negative Predictive Value = 0.2241 
 Probability Level = 0.9349  Model = Model  (0.8428) 
 Sensitivity = 0.625 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.974 
 Negative Predictive Value = 0.2241 
 Probability Level = 0.9349  Model = Model  (0.8428) 
 Sensitivity = 0.6167 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9737 
 Negative Predictive Value = 0.2203 
 Probability Level = 0.9422  Model = Model  (0.8428) 
 Sensitivity = 0.6167 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9737 
 Negative Predictive Value = 0.2203 
 Probability Level = 0.9422  Model = Model  (0.8428) 
 Sensitivity = 0.6083 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9733 
 Negative Predictive Value = 0.2167 
 Probability Level = 0.9443  Model = Model  (0.8428) 
 Sensitivity = 0.6083 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9733 
 Negative Predictive Value = 0.2167 
 Probability Level = 0.9443  Model = Model  (0.8428) 
 Sensitivity = 0.6 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.973 
 Negative Predictive Value = 0.2131 
 Probability Level = 0.9446  Model = Model  (0.8428) 
 Sensitivity = 0.6 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.973 
 Negative Predictive Value = 0.2131 
 Probability Level = 0.9446  Model = Model  (0.8428) 
 Sensitivity = 0.5917 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9726 
 Negative Predictive Value = 0.2097 
 Probability Level = 0.9457  Model = Model  (0.8428) 
 Sensitivity = 0.5917 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9726 
 Negative Predictive Value = 0.2097 
 Probability Level = 0.9457  Model = Model  (0.8428) 
 Sensitivity = 0.5833 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9722 
 Negative Predictive Value = 0.2063 
 Probability Level = 0.9491  Model = Model  (0.8428) 
 Sensitivity = 0.5833 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9722 
 Negative Predictive Value = 0.2063 
 Probability Level = 0.9491  Model = Model  (0.8428) 
 Sensitivity = 0.575 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9718 
 Negative Predictive Value = 0.2031 
 Probability Level = 0.9497  Model = Model  (0.8428) 
 Sensitivity = 0.575 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9718 
 Negative Predictive Value = 0.2031 
 Probability Level = 0.9497  Model = Model  (0.8428) 
 Sensitivity = 0.5667 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9714 
 Negative Predictive Value = 0.2 
 Probability Level = 0.9506  Model = Model  (0.8428) 
 Sensitivity = 0.5667 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9714 
 Negative Predictive Value = 0.2 
 Probability Level = 0.9506  Model = Model  (0.8428) 
 Sensitivity = 0.5583 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.971 
 Negative Predictive Value = 0.197 
 Probability Level = 0.951  Model = Model  (0.8428) 
 Sensitivity = 0.5583 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.971 
 Negative Predictive Value = 0.197 
 Probability Level = 0.951  Model = Model  (0.8428) 
 Sensitivity = 0.55 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9706 
 Negative Predictive Value = 0.194 
 Probability Level = 0.9546  Model = Model  (0.8428) 
 Sensitivity = 0.55 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9706 
 Negative Predictive Value = 0.194 
 Probability Level = 0.9546  Model = Model  (0.8428) 
 Sensitivity = 0.5417 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9701 
 Negative Predictive Value = 0.1912 
 Probability Level = 0.9546  Model = Model  (0.8428) 
 Sensitivity = 0.5417 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9701 
 Negative Predictive Value = 0.1912 
 Probability Level = 0.9546  Model = Model  (0.8428) 
 Sensitivity = 0.5333 
 1 - Specificity = 0.1333 
 Frequency = 2 
 Positive Predictive Value = 0.9697 
 Negative Predictive Value = 0.1884 
 Probability Level = 0.9555  Model = Model  (0.8428) 
 Sensitivity = 0.5333 
 1 - Specificity = 0.1333 
 Frequency = 2 
 Positive Predictive Value = 0.9697 
 Negative Predictive Value = 0.1884 
 Probability Level = 0.9555  Model = Model  (0.8428) 
 Sensitivity = 0.5167 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9688 
 Negative Predictive Value = 0.1831 
 Probability Level = 0.9577  Model = Model  (0.8428) 
 Sensitivity = 0.5167 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9688 
 Negative Predictive Value = 0.1831 
 Probability Level = 0.9577  Model = Model  (0.8428) 
 Sensitivity = 0.5083 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9683 
 Negative Predictive Value = 0.1806 
 Probability Level = 0.9585  Model = Model  (0.8428) 
 Sensitivity = 0.5083 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9683 
 Negative Predictive Value = 0.1806 
 Probability Level = 0.9585  Model = Model  (0.8428) 
 Sensitivity = 0.5 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9677 
 Negative Predictive Value = 0.1781 
 Probability Level = 0.9598  Model = Model  (0.8428) 
 Sensitivity = 0.5 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9677 
 Negative Predictive Value = 0.1781 
 Probability Level = 0.9598  Model = Model  (0.8428) 
 Sensitivity = 0.4917 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9672 
 Negative Predictive Value = 0.1757 
 Probability Level = 0.9602  Model = Model  (0.8428) 
 Sensitivity = 0.4917 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9672 
 Negative Predictive Value = 0.1757 
 Probability Level = 0.9602  Model = Model  (0.8428) 
 Sensitivity = 0.4833 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9667 
 Negative Predictive Value = 0.1733 
 Probability Level = 0.9619  Model = Model  (0.8428) 
 Sensitivity = 0.4833 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9667 
 Negative Predictive Value = 0.1733 
 Probability Level = 0.9619  Model = Model  (0.8428) 
 Sensitivity = 0.475 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9661 
 Negative Predictive Value = 0.1711 
 Probability Level = 0.9626  Model = Model  (0.8428) 
 Sensitivity = 0.475 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9661 
 Negative Predictive Value = 0.1711 
 Probability Level = 0.9626  Model = Model  (0.8428) 
 Sensitivity = 0.4667 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9655 
 Negative Predictive Value = 0.1688 
 Probability Level = 0.9627  Model = Model  (0.8428) 
 Sensitivity = 0.4667 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9655 
 Negative Predictive Value = 0.1688 
 Probability Level = 0.9627  Model = Model  (0.8428) 
 Sensitivity = 0.4583 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9649 
 Negative Predictive Value = 0.1667 
 Probability Level = 0.9628  Model = Model  (0.8428) 
 Sensitivity = 0.4583 
 1 - Specificity = 0.1333 
 Frequency = 1 
 Positive Predictive Value = 0.9649 
 Negative Predictive Value = 0.1667 
 Probability Level = 0.9628  Model = Model  (0.8428) 
 Sensitivity = 0.4583 
 1 - Specificity = 0.0667 
 Frequency = 1 
 Positive Predictive Value = 0.9821 
 Negative Predictive Value = 0.1772 
 Probability Level = 0.9634  Model = Model  (0.8428) 
 Sensitivity = 0.4583 
 1 - Specificity = 0.0667 
 Frequency = 1 
 Positive Predictive Value = 0.9821 
 Negative Predictive Value = 0.1772 
 Probability Level = 0.9634  Model = Model  (0.8428) 
 Sensitivity = 0.45 
 1 - Specificity = 0.0667 
 Frequency = 1 
 Positive Predictive Value = 0.9818 
 Negative Predictive Value = 0.175 
 Probability Level = 0.9638  Model = Model  (0.8428) 
 Sensitivity = 0.45 
 1 - Specificity = 0.0667 
 Frequency = 1 
 Positive Predictive Value = 0.9818 
 Negative Predictive Value = 0.175 
 Probability Level = 0.9638  Model = Model  (0.8428) 
 Sensitivity = 0.4417 
 1 - Specificity = 0.0667 
 Frequency = 1 
 Positive Predictive Value = 0.9815 
 Negative Predictive Value = 0.1728 
 Probability Level = 0.965  Model = Model  (0.8428) 
 Sensitivity = 0.4417 
 1 - Specificity = 0.0667 
 Frequency = 1 
 Positive Predictive Value = 0.9815 
 Negative Predictive Value = 0.1728 
 Probability Level = 0.965  Model = Model  (0.8428) 
 Sensitivity = 0.4417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1829 
 Probability Level = 0.9671  Model = Model  (0.8428) 
 Sensitivity = 0.4417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1829 
 Probability Level = 0.9671  Model = Model  (0.8428) 
 Sensitivity = 0.4333 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1807 
 Probability Level = 0.9688  Model = Model  (0.8428) 
 Sensitivity = 0.4333 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1807 
 Probability Level = 0.9688  Model = Model  (0.8428) 
 Sensitivity = 0.4167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1765 
 Probability Level = 0.9699  Model = Model  (0.8428) 
 Sensitivity = 0.4167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1765 
 Probability Level = 0.9699  Model = Model  (0.8428) 
 Sensitivity = 0.4083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1744 
 Probability Level = 0.9707  Model = Model  (0.8428) 
 Sensitivity = 0.4083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1744 
 Probability Level = 0.9707  Model = Model  (0.8428) 
 Sensitivity = 0.4 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1724 
 Probability Level = 0.9735  Model = Model  (0.8428) 
 Sensitivity = 0.4 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1724 
 Probability Level = 0.9735  Model = Model  (0.8428) 
 Sensitivity = 0.3917 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1705 
 Probability Level = 0.9736  Model = Model  (0.8428) 
 Sensitivity = 0.3917 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1705 
 Probability Level = 0.9736  Model = Model  (0.8428) 
 Sensitivity = 0.3833 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1685 
 Probability Level = 0.9786  Model = Model  (0.8428) 
 Sensitivity = 0.3833 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1685 
 Probability Level = 0.9786  Model = Model  (0.8428) 
 Sensitivity = 0.375 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1667 
 Probability Level = 0.979  Model = Model  (0.8428) 
 Sensitivity = 0.375 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1667 
 Probability Level = 0.979  Model = Model  (0.8428) 
 Sensitivity = 0.3667 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1648 
 Probability Level = 0.9795  Model = Model  (0.8428) 
 Sensitivity = 0.3667 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1648 
 Probability Level = 0.9795  Model = Model  (0.8428) 
 Sensitivity = 0.35 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1613 
 Probability Level = 0.9797  Model = Model  (0.8428) 
 Sensitivity = 0.35 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1613 
 Probability Level = 0.9797  Model = Model  (0.8428) 
 Sensitivity = 0.3417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1596 
 Probability Level = 0.9821  Model = Model  (0.8428) 
 Sensitivity = 0.3417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1596 
 Probability Level = 0.9821  Model = Model  (0.8428) 
 Sensitivity = 0.3333 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1579 
 Probability Level = 0.9826  Model = Model  (0.8428) 
 Sensitivity = 0.3333 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1579 
 Probability Level = 0.9826  Model = Model  (0.8428) 
 Sensitivity = 0.325 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1563 
 Probability Level = 0.9831  Model = Model  (0.8428) 
 Sensitivity = 0.325 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1563 
 Probability Level = 0.9831  Model = Model  (0.8428) 
 Sensitivity = 0.3167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1546 
 Probability Level = 0.9835  Model = Model  (0.8428) 
 Sensitivity = 0.3167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1546 
 Probability Level = 0.9835  Model = Model  (0.8428) 
 Sensitivity = 0.3083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1531 
 Probability Level = 0.9836  Model = Model  (0.8428) 
 Sensitivity = 0.3083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1531 
 Probability Level = 0.9836  Model = Model  (0.8428) 
 Sensitivity = 0.3 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1515 
 Probability Level = 0.9843  Model = Model  (0.8428) 
 Sensitivity = 0.3 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1515 
 Probability Level = 0.9843  Model = Model  (0.8428) 
 Sensitivity = 0.2917 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.15 
 Probability Level = 0.9848  Model = Model  (0.8428) 
 Sensitivity = 0.2917 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.15 
 Probability Level = 0.9848  Model = Model  (0.8428) 
 Sensitivity = 0.2833 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1485 
 Probability Level = 0.9855  Model = Model  (0.8428) 
 Sensitivity = 0.2833 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1485 
 Probability Level = 0.9855  Model = Model  (0.8428) 
 Sensitivity = 0.275 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1471 
 Probability Level = 0.9856  Model = Model  (0.8428) 
 Sensitivity = 0.275 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1471 
 Probability Level = 0.9856  Model = Model  (0.8428) 
 Sensitivity = 0.2667 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1456 
 Probability Level = 0.9862  Model = Model  (0.8428) 
 Sensitivity = 0.2667 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1456 
 Probability Level = 0.9862  Model = Model  (0.8428) 
 Sensitivity = 0.2583 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1442 
 Probability Level = 0.9866  Model = Model  (0.8428) 
 Sensitivity = 0.2583 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1442 
 Probability Level = 0.9866  Model = Model  (0.8428) 
 Sensitivity = 0.25 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1429 
 Probability Level = 0.9871  Model = Model  (0.8428) 
 Sensitivity = 0.25 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1429 
 Probability Level = 0.9871  Model = Model  (0.8428) 
 Sensitivity = 0.2417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1415 
 Probability Level = 0.9875  Model = Model  (0.8428) 
 Sensitivity = 0.2417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1415 
 Probability Level = 0.9875  Model = Model  (0.8428) 
 Sensitivity = 0.2333 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1402 
 Probability Level = 0.9897  Model = Model  (0.8428) 
 Sensitivity = 0.2333 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1402 
 Probability Level = 0.9897  Model = Model  (0.8428) 
 Sensitivity = 0.225 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1389 
 Probability Level = 0.9898  Model = Model  (0.8428) 
 Sensitivity = 0.225 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1389 
 Probability Level = 0.9898  Model = Model  (0.8428) 
 Sensitivity = 0.2167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1376 
 Probability Level = 0.9912  Model = Model  (0.8428) 
 Sensitivity = 0.2167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1376 
 Probability Level = 0.9912  Model = Model  (0.8428) 
 Sensitivity = 0.2083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1364 
 Probability Level = 0.9912  Model = Model  (0.8428) 
 Sensitivity = 0.2083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1364 
 Probability Level = 0.9912  Model = Model  (0.8428) 
 Sensitivity = 0.2 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1351 
 Probability Level = 0.9921  Model = Model  (0.8428) 
 Sensitivity = 0.2 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1351 
 Probability Level = 0.9921  Model = Model  (0.8428) 
 Sensitivity = 0.1917 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1339 
 Probability Level = 0.9927  Model = Model  (0.8428) 
 Sensitivity = 0.1917 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1339 
 Probability Level = 0.9927  Model = Model  (0.8428) 
 Sensitivity = 0.1833 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1327 
 Probability Level = 0.9928  Model = Model  (0.8428) 
 Sensitivity = 0.1833 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1327 
 Probability Level = 0.9928  Model = Model  (0.8428) 
 Sensitivity = 0.175 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1316 
 Probability Level = 0.9928  Model = Model  (0.8428) 
 Sensitivity = 0.175 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1316 
 Probability Level = 0.9928  Model = Model  (0.8428) 
 Sensitivity = 0.1667 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1304 
 Probability Level = 0.9936  Model = Model  (0.8428) 
 Sensitivity = 0.1667 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1304 
 Probability Level = 0.9936  Model = Model  (0.8428) 
 Sensitivity = 0.1583 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1293 
 Probability Level = 0.994  Model = Model  (0.8428) 
 Sensitivity = 0.1583 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1293 
 Probability Level = 0.994  Model = Model  (0.8428) 
 Sensitivity = 0.15 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1282 
 Probability Level = 0.9946  Model = Model  (0.8428) 
 Sensitivity = 0.15 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1282 
 Probability Level = 0.9946  Model = Model  (0.8428) 
 Sensitivity = 0.1417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1271 
 Probability Level = 0.9948  Model = Model  (0.8428) 
 Sensitivity = 0.1417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1271 
 Probability Level = 0.9948  Model = Model  (0.8428) 
 Sensitivity = 0.1333 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1261 
 Probability Level = 0.9954  Model = Model  (0.8428) 
 Sensitivity = 0.1333 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1261 
 Probability Level = 0.9954  Model = Model  (0.8428) 
 Sensitivity = 0.125 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.125 
 Probability Level = 0.9961  Model = Model  (0.8428) 
 Sensitivity = 0.125 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.125 
 Probability Level = 0.9961  Model = Model  (0.8428) 
 Sensitivity = 0.1167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.124 
 Probability Level = 0.9968  Model = Model  (0.8428) 
 Sensitivity = 0.1167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.124 
 Probability Level = 0.9968  Model = Model  (0.8428) 
 Sensitivity = 0.1083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.123 
 Probability Level = 0.9968  Model = Model  (0.8428) 
 Sensitivity = 0.1083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.123 
 Probability Level = 0.9968  Model = Model  (0.8428) 
 Sensitivity = 0.1 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.122 
 Probability Level = 0.997  Model = Model  (0.8428) 
 Sensitivity = 0.1 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.122 
 Probability Level = 0.997  Model = Model  (0.8428) 
 Sensitivity = 0.0917 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.121 
 Probability Level = 0.9972  Model = Model  (0.8428) 
 Sensitivity = 0.0917 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.121 
 Probability Level = 0.9972  Model = Model  (0.8428) 
 Sensitivity = 0.075 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.119 
 Probability Level = 0.9977  Model = Model  (0.8428) 
 Sensitivity = 0.075 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.119 
 Probability Level = 0.9977  Model = Model  (0.8428) 
 Sensitivity = 0.0667 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1181 
 Probability Level = 0.998  Model = Model  (0.8428) 
 Sensitivity = 0.0667 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1181 
 Probability Level = 0.998  Model = Model  (0.8428) 
 Sensitivity = 0.05 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1163 
 Probability Level = 0.9987  Model = Model  (0.8428) 
 Sensitivity = 0.05 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1163 
 Probability Level = 0.9987  Model = Model  (0.8428) 
 Sensitivity = 0.0417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1154 
 Probability Level = 0.9989  Model = Model  (0.8428) 
 Sensitivity = 0.0417 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1154 
 Probability Level = 0.9989  Model = Model  (0.8428) 
 Sensitivity = 0.0333 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1145 
 Probability Level = 0.9989  Model = Model  (0.8428) 
 Sensitivity = 0.0333 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1145 
 Probability Level = 0.9989  Model = Model  (0.8428) 
 Sensitivity = 0.025 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1136 
 Probability Level = 0.9991  Model = Model  (0.8428) 
 Sensitivity = 0.025 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1136 
 Probability Level = 0.9991  Model = Model  (0.8428) 
 Sensitivity = 0.0167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1128 
 Probability Level = 0.9994  Model = Model  (0.8428) 
 Sensitivity = 0.0167 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1128 
 Probability Level = 0.9994  Model = Model  (0.8428) 
 Sensitivity = 0.0083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1119 
 Probability Level = 0.9997  Model = Model  (0.8428) 
 Sensitivity = 0.0083 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1119 
 Probability Level = 0.9997  Model = Model  (0.8428) 
 Sensitivity = 0 
 1 - Specificity = 0 
 Frequency = 0 
 Positive Predictive Value = . 
 Negative Predictive Value = 8 
 Probability Level = 1  Slope = 1 
 Y = 0
ROC Curve for Model

Influence Plots

Pearson Residuals

 Pearson Residual = 0.0734 
 Case Number = 135 
 Price = 1  Pearson Residual = 0.0852 
 Case Number = 134 
 Price = 1  Pearson Residual = 0.1439 
 Case Number = 133 
 Price = 1  Pearson Residual = 0.1971 
 Case Number = 132 
 Price = 1  Pearson Residual = 0.2271 
 Case Number = 131 
 Price = 1  Pearson Residual = -0.517 
 Case Number = 130 
 Price = 0  Pearson Residual = 0.2046 
 Case Number = 129 
 Price = 1  Pearson Residual = 0.1649 
 Case Number = 128 
 Price = 1  Pearson Residual = -2.016 
 Case Number = 127 
 Price = 0  Pearson Residual = 0.4683 
 Case Number = 126 
 Price = 1  Pearson Residual = 0.7516 
 Case Number = 125 
 Price = 1  Pearson Residual = 0.4688 
 Case Number = 124 
 Price = 1  Pearson Residual = 0.0528 
 Case Number = 123 
 Price = 1  Pearson Residual = 0.045 
 Case Number = 122 
 Price = 1  Pearson Residual = 0.3909 
 Case Number = 121 
 Price = 1  Pearson Residual = 0.697 
 Case Number = 120 
 Price = 1  Pearson Residual = 0.1313 
 Case Number = 119 
 Price = 1  Pearson Residual = 0.1349 
 Case Number = 118 
 Price = 1  Pearson Residual = 0.1466 
 Case Number = 117 
 Price = 1  Pearson Residual = 0.0528 
 Case Number = 116 
 Price = 1  Pearson Residual = 0.0778 
 Case Number = 115 
 Price = 1  Pearson Residual = 0.2826 
 Case Number = 114 
 Price = 1  Pearson Residual = -2.046 
 Case Number = 113 
 Price = 0  Pearson Residual = 0.0943 
 Case Number = 112 
 Price = 1  Pearson Residual = 0.1125 
 Case Number = 111 
 Price = 1  Pearson Residual = 0.2302 
 Case Number = 110 
 Price = 1  Pearson Residual = 0.4732 
 Case Number = 109 
 Price = 1  Pearson Residual = 0.4683 
 Case Number = 108 
 Price = 1  Pearson Residual = 0.1166 
 Case Number = 107 
 Price = 1  Pearson Residual = 0.389 
 Case Number = 106 
 Price = 1  Pearson Residual = 0.1017 
 Case Number = 105 
 Price = 1  Pearson Residual = 0.0721 
 Case Number = 104 
 Price = 1  Pearson Residual = 0.275 
 Case Number = 103 
 Price = 1  Pearson Residual = 1.1215 
 Case Number = 102 
 Price = 1  Pearson Residual = 0.2639 
 Case Number = 101 
 Price = 1  Pearson Residual = 0.1211 
 Case Number = 100 
 Price = 1  Pearson Residual = 0.045 
 Case Number = 99 
 Price = 1  Pearson Residual = 0.1762 
 Case Number = 98 
 Price = 1  Pearson Residual = 0.1794 
 Case Number = 97 
 Price = 1  Pearson Residual = 0.3307 
 Case Number = 96 
 Price = 1  Pearson Residual = 0.1645 
 Case Number = 95 
 Price = 1  Pearson Residual = -2.019 
 Case Number = 94 
 Price = 0  Pearson Residual = 0.1446 
 Case Number = 93 
 Price = 1  Pearson Residual = 0.9999 
 Case Number = 92 
 Price = 1  Pearson Residual = -2.424 
 Case Number = 91 
 Price = 0  Pearson Residual = 0.0805 
 Case Number = 90 
 Price = 1  Pearson Residual = -1.166 
 Case Number = 89 
 Price = 0  Pearson Residual = 0.0894 
 Case Number = 88 
 Price = 1  Pearson Residual = 0.208 
 Case Number = 87 
 Price = 1  Pearson Residual = 0.1446 
 Case Number = 86 
 Price = 1  Pearson Residual = -0.665 
 Case Number = 85 
 Price = 0  Pearson Residual = 0.5145 
 Case Number = 84 
 Price = 1  Pearson Residual = 0.482 
 Case Number = 83 
 Price = 1  Pearson Residual = 0.0622 
 Case Number = 82 
 Price = 1  Pearson Residual = 0.446 
 Case Number = 81 
 Price = 1  Pearson Residual = 0.2317 
 Case Number = 80 
 Price = 1  Pearson Residual = 0.228 
 Case Number = 79 
 Price = 1  Pearson Residual = 0.4343 
 Case Number = 78 
 Price = 1  Pearson Residual = 0.3273 
 Case Number = 77 
 Price = 1  Pearson Residual = -5.088 
 Case Number = 76 
 Price = 0  Pearson Residual = -2.19 
 Case Number = 75 
 Price = 0  Pearson Residual = -0.378 
 Case Number = 74 
 Price = 0  Pearson Residual = 0.3771 
 Case Number = 73 
 Price = 1  Pearson Residual = 0.4954 
 Case Number = 72 
 Price = 1  Pearson Residual = 0.1479 
 Case Number = 71 
 Price = 1  Pearson Residual = 0.318 
 Case Number = 70 
 Price = 1  Pearson Residual = 0.3454 
 Case Number = 69 
 Price = 1  Pearson Residual = 0.6398 
 Case Number = 68 
 Price = 1  Pearson Residual = 0.7708 
 Case Number = 67 
 Price = 1  Pearson Residual = -1.072 
 Case Number = 66 
 Price = 0  Pearson Residual = 0.2713 
 Case Number = 65 
 Price = 1  Pearson Residual = 0.3055 
 Case Number = 64 
 Price = 1  Pearson Residual = 0.4285 
 Case Number = 63 
 Price = 1  Pearson Residual = 0.5234 
 Case Number = 62 
 Price = 1  Pearson Residual = 0.0241 
 Case Number = 61 
 Price = 1  Pearson Residual = 0.2037 
 Case Number = 60 
 Price = 1  Pearson Residual = 0.0307 
 Case Number = 59 
 Price = 1  Pearson Residual = 0.5409 
 Case Number = 58 
 Price = 1  Pearson Residual = 0.1265 
 Case Number = 57 
 Price = 1  Pearson Residual = 0.1292 
 Case Number = 56 
 Price = 1  Pearson Residual = 0.1949 
 Case Number = 55 
 Price = 1  Pearson Residual = 0.3349 
 Case Number = 54 
 Price = 1  Pearson Residual = -5.253 
 Case Number = 53 
 Price = 0  Pearson Residual = 0.6201 
 Case Number = 52 
 Price = 1  Pearson Residual = -2.538 
 Case Number = 51 
 Price = 0  Pearson Residual = 0.0944 
 Case Number = 50 
 Price = 1  Pearson Residual = 0.3273 
 Case Number = 49 
 Price = 1  Pearson Residual = 0.446 
 Case Number = 48 
 Price = 1  Pearson Residual = 0.1844 
 Case Number = 47 
 Price = 1  Pearson Residual = 0.1736 
 Case Number = 46 
 Price = 1  Pearson Residual = 0.0565 
 Case Number = 45 
 Price = 1  Pearson Residual = 0.017 
 Case Number = 44 
 Price = 1  Pearson Residual = 0.0361 
 Case Number = 43 
 Price = 1  Pearson Residual = 0.1794 
 Case Number = 42 
 Price = 1  Pearson Residual = 0.3771 
 Case Number = 41 
 Price = 1  Pearson Residual = 0.3101 
 Case Number = 40 
 Price = 1  Pearson Residual = 0.2429 
 Case Number = 39 
 Price = 1  Pearson Residual = 0.0548 
 Case Number = 38 
 Price = 1  Pearson Residual = 0.8017 
 Case Number = 37 
 Price = 1  Pearson Residual = 0.1184 
 Case Number = 36 
 Price = 1  Pearson Residual = 0.7304 
 Case Number = 35 
 Price = 1  Pearson Residual = 0.3214 
 Case Number = 34 
 Price = 1  Pearson Residual = 0.1991 
 Case Number = 33 
 Price = 1  Pearson Residual = 0.2476 
 Case Number = 32 
 Price = 1  Pearson Residual = 0.5899 
 Case Number = 31 
 Price = 1  Pearson Residual = 0.2396 
 Case Number = 30 
 Price = 1  Pearson Residual = 0.5767 
 Case Number = 29 
 Price = 1  Pearson Residual = 0.3933 
 Case Number = 28 
 Price = 1  Pearson Residual = 0.0565 
 Case Number = 27 
 Price = 1  Pearson Residual = 0.0678 
 Case Number = 26 
 Price = 1  Pearson Residual = 0.1213 
 Case Number = 25 
 Price = 1  Pearson Residual = 0.2421 
 Case Number = 24 
 Price = 1  Pearson Residual = 0.3273 
 Case Number = 23 
 Price = 1  Pearson Residual = 0.1331 
 Case Number = 22 
 Price = 1  Pearson Residual = 0.355 
 Case Number = 21 
 Price = 1  Pearson Residual = 0.2947 
 Case Number = 20 
 Price = 1  Pearson Residual = 0.2181 
 Case Number = 19 
 Price = 1  Pearson Residual = 0.0857 
 Case Number = 18 
 Price = 1  Pearson Residual = 0.0334 
 Case Number = 17 
 Price = 1  Pearson Residual = 0.4343 
 Case Number = 16 
 Price = 1  Pearson Residual = 0.1297 
 Case Number = 15 
 Price = 1  Pearson Residual = 0.1939 
 Case Number = 14 
 Price = 1  Pearson Residual = 0.0854 
 Case Number = 13 
 Price = 1  Pearson Residual = 0.2159 
 Case Number = 12 
 Price = 1  Pearson Residual = -2.791 
 Case Number = 11 
 Price = 0  Pearson Residual = 0.102 
 Case Number = 10 
 Price = 1  Pearson Residual = 0.2101 
 Case Number = 9 
 Price = 1  Pearson Residual = 0.2182 
 Case Number = 8 
 Price = 1  Pearson Residual = 0.1969 
 Case Number = 7 
 Price = 1  Pearson Residual = 0.048 
 Case Number = 6 
 Price = 1  Pearson Residual = 0.2159 
 Case Number = 5 
 Price = 1  Pearson Residual = -1.297 
 Case Number = 4 
 Price = 0  Pearson Residual = 0.1145 
 Case Number = 3 
 Price = 1  Pearson Residual = 0.1244 
 Case Number = 2 
 Price = 1  Pearson Residual = 0.0331 
 Case Number = 1 
 Price = 1  Y = 0
Plot of Pearson Chi-Square Residuals by Case Number.

Deviance Residuals

 Deviance Residual = 0.1036 
 Case Number = 135 
 Price = 1  Deviance Residual = 0.1203 
 Case Number = 134 
 Price = 1  Deviance Residual = 0.2025 
 Case Number = 133 
 Price = 1  Deviance Residual = 0.276 
 Case Number = 132 
 Price = 1  Deviance Residual = 0.3171 
 Case Number = 131 
 Price = 1  Deviance Residual = -0.688 
 Case Number = 130 
 Price = 0  Deviance Residual = 0.2864 
 Case Number = 129 
 Price = 1  Deviance Residual = 0.2317 
 Case Number = 128 
 Price = 1  Deviance Residual = -1.801 
 Case Number = 127 
 Price = 0  Deviance Residual = 0.6297 
 Case Number = 126 
 Price = 1  Deviance Residual = 0.9464 
 Case Number = 125 
 Price = 1  Deviance Residual = 0.6304 
 Case Number = 124 
 Price = 1  Deviance Residual = 0.0746 
 Case Number = 123 
 Price = 1  Deviance Residual = 0.0636 
 Case Number = 122 
 Price = 1  Deviance Residual = 0.5333 
 Case Number = 121 
 Price = 1  Deviance Residual = 0.8899 
 Case Number = 120 
 Price = 1  Deviance Residual = 0.1849 
 Case Number = 119 
 Price = 1  Deviance Residual = 0.19 
 Case Number = 118 
 Price = 1  Deviance Residual = 0.2062 
 Case Number = 117 
 Price = 1