Results: Binary Logistic Regression

The LOGISTIC Procedure

Model Information

Model Information
Data Set _TEMP0.CSVCOMPRESSEDWOBLANKS
Response Variable w2w3_combo_breakup
Number of Response Levels 2
Model binary logit
Optimization Technique Fisher's scoring

Observations Summary

Number of Observations Read 2632
Number of Observations Used 1856

Response Profile

Response Profile
Ordered
Value
w2w3_combo_breakup Total
Frequency
1 broke up 285
2 still together, or lost to follow-up, or partner deceased 1571

Probability modeled is w2w3_combo_breakup='still together, or lost to follow-up, or partner deceased'.

Note:776 observations were deleted due to missing values for the response or explanatory variables.

Class Level Information

Class Level Information
Class Value Design Variables
q32_internet 0 1 0      
  1 0 1      
q30 approve 1 0 0 0 0
  disapprove 0 1 0 0 0
  do not know 0 0 1 0 0
  neither app 0 0 0 1 0
  refused 0 0 0 0 1
ppeducat bachelor's degree or higher 1 0 0 0  
  high school 0 1 0 0  
  less than high school 0 0 1 0  
  some college 0 0 0 1  

Convergence Status

Model Convergence Status
Quasi-complete separation of data points detected.

Fit Statistics

Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 1593.810 1390.812
SC 1599.336 1440.548
-2 Log L 1591.810 1372.812

Global Tests

Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 218.9977 8 <.0001
Score 281.5949 8 <.0001
Wald 196.5925 8 <.0001

Type 3 Tests

Type 3 Analysis of Effects
Effect DF Wald
Chi-Square
Pr > ChiSq
q32_internet 1 11.3379 0.0008
q30 4 163.0901 <.0001
ppeducat 3 23.5292 <.0001

Parameter Estimates

HereODD
Analysis of Maximum Likelihood Estimates
Parameter   DF Estimate Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept   1 10.8993 196.6 0.0031 0.9558
q32_internet 0 1 0.6440 0.1912 11.3379 0.0008
q32_internet 1 0 0 . . .
q30 approve 1 -9.7187 196.6 0.0024 0.9606
q30 disapprove 1 -10.2306 196.6 0.0027 0.9585
q30 do not know 1 -12.4250 196.6 0.0040 0.9496
q30 neither app 1 -10.7983 196.6 0.0030 0.9562
q30 refused 0 0 . . .
ppeducat bachelor's degree or higher 1 0.7626 0.1650 21.3589 <.0001
ppeducat high school 1 0.5100 0.1900 7.2022 0.0073
ppeducat less than high school 1 0.7420 0.3150 5.5471 0.0185
ppeducat some college 0 0 . . .

Odds Ratios

Odds Ratio Estimates
Effect Point Estimate 95% Wald
Confidence Limits
q32_internet 0 vs 1 1.904 1.309 2.770
q30 approve vs refused <0.001 <0.001 >999.999
q30 disapprove vs refused <0.001 <0.001 >999.999
q30 do not know vs refused <0.001 <0.001 >999.999
q30 neither app vs refused <0.001 <0.001 >999.999
ppeducat bachelor's degree or higher vs some college 2.144 1.551 2.962
ppeducat high school vs some college 1.665 1.147 2.417
ppeducat less than high school vs some college 2.100 1.133 3.894

Odds Ratios with 95% Wald Confidence Limits

 Odds Ratio Estimate = 2.1001 
 COMPBL(_EFFECT) = ppeducat less than high school vs some college  Odds Ratio Estimate = 1.6652 
 COMPBL(_EFFECT) = ppeducat high school vs some college  Odds Ratio Estimate = 2.1438 
 COMPBL(_EFFECT) = ppeducat bachelor's degree or higher vs some college  Odds Ratio Estimate = .00002 
 COMPBL(_EFFECT) = q30 neither app vs refused  Odds Ratio Estimate = 4E-6 
 COMPBL(_EFFECT) = q30 do not know vs refused  Odds Ratio Estimate = 3.6E-5 
 COMPBL(_EFFECT) = q30 disapprove vs refused  Odds Ratio Estimate = .00006 
 COMPBL(_EFFECT) = q30 approve vs refused  Odds Ratio Estimate = 1.904 
 COMPBL(_EFFECT) = q32_internet 0 vs 1  COMPBL(_EFFECT) = ppeducat less than high school vs some college 
 Upper = 3.894 
 Lower = 1.1326  COMPBL(_EFFECT) = ppeducat high school vs some college 
 Upper = 2.4167 
 Lower = 1.1474  COMPBL(_EFFECT) = ppeducat bachelor's degree or higher vs some college 
 Upper = 2.9624 
 Lower = 1.5514  COMPBL(_EFFECT) = q30 neither app vs refused 
 Upper = 13E153 
 Lower = -396.1  COMPBL(_EFFECT) = q30 neither app vs refused 
 Upper = 13E153 
 Lower = -396.1  COMPBL(_EFFECT) = q30 neither app vs refused 
 Upper = 13E153 
 Lower = -396.1  COMPBL(_EFFECT) = q30 neither app vs refused 
 Upper = 13E153 
 Lower = -396.1  COMPBL(_EFFECT) = q30 do not know vs refused 
 Upper = 13E153 
 Lower = -397.7  COMPBL(_EFFECT) = q30 do not know vs refused 
 Upper = 13E153 
 Lower = -397.7  COMPBL(_EFFECT) = q30 do not know vs refused 
 Upper = 13E153 
 Lower = -397.7  COMPBL(_EFFECT) = q30 do not know vs refused 
 Upper = 13E153 
 Lower = -397.7  COMPBL(_EFFECT) = q30 disapprove vs refused 
 Upper = 13E153 
 Lower = -395.5  COMPBL(_EFFECT) = q30 disapprove vs refused 
 Upper = 13E153 
 Lower = -395.5  COMPBL(_EFFECT) = q30 disapprove vs refused 
 Upper = 13E153 
 Lower = -395.5  COMPBL(_EFFECT) = q30 disapprove vs refused 
 Upper = 13E153 
 Lower = -395.5  COMPBL(_EFFECT) = q30 approve vs refused 
 Upper = 13E153 
 Lower = -395  COMPBL(_EFFECT) = q30 approve vs refused 
 Upper = 13E153 
 Lower = -395  COMPBL(_EFFECT) = q30 approve vs refused 
 Upper = 13E153 
 Lower = -395  COMPBL(_EFFECT) = q30 approve vs refused 
 Upper = 13E153 
 Lower = -395  COMPBL(_EFFECT) = q32_internet 0 vs 1 
 Upper = 2.7699 
 Lower = 1.3088  X = 1  COMPBL(_EFFECT) = ppeducat less than high school vs some college  COMPBL(_EFFECT) = ppeducat high school vs some college  COMPBL(_EFFECT) = ppeducat bachelor's degree or higher vs some college  COMPBL(_EFFECT) = q30 neither app vs refused  COMPBL(_EFFECT) = q30 do not know vs refused  COMPBL(_EFFECT) = q30 disapprove vs refused  COMPBL(_EFFECT) = q30 approve vs refused  COMPBL(_EFFECT) = q32_internet 0 vs 1
Plot of Odds Ratios with 95% Wald Confidence Limits

Association Statistics

Association of Predicted Probabilities and Observed Responses
Percent Concordant 67.1 Somers' D 0.452
Percent Discordant 21.9 Gamma 0.507
Percent Tied 10.9 Tau-a 0.118
Pairs 447735 c 0.726

ROC Curve

 Model = Model  (0.7260) 
 Sensitivity = 1 
 1 - Specificity = 1 
 Frequency = 9 
 Positive Predictive Value = 0.8464 
 Negative Predictive Value = . 
 Probability Level = 0.1786  Model = Model  (0.7260) 
 Sensitivity = 0.9981 
 1 - Specificity = 0.9789 
 Frequency = 3 
 Positive Predictive Value = 0.8489 
 Negative Predictive Value = 0.6667 
 Probability Level = 0.2659  Model = Model  (0.7260) 
 Sensitivity = 0.9981 
 1 - Specificity = 0.9789 
 Frequency = 3 
 Positive Predictive Value = 0.8489 
 Negative Predictive Value = 0.6667 
 Probability Level = 0.2659  Model = Model  (0.7260) 
 Sensitivity = 0.9981 
 1 - Specificity = 0.9684 
 Frequency = 27 
 Positive Predictive Value = 0.8503 
 Negative Predictive Value = 0.75 
 Probability Level = 0.2928  Model = Model  (0.7260) 
 Sensitivity = 0.9981 
 1 - Specificity = 0.9684 
 Frequency = 27 
 Positive Predictive Value = 0.8503 
 Negative Predictive Value = 0.75 
 Probability Level = 0.2928  Model = Model  (0.7260) 
 Sensitivity = 0.9924 
 1 - Specificity = 0.9053 
 Frequency = 3 
 Positive Predictive Value = 0.858 
 Negative Predictive Value = 0.6923 
 Probability Level = 0.3135  Model = Model  (0.7260) 
 Sensitivity = 0.9924 
 1 - Specificity = 0.9053 
 Frequency = 3 
 Positive Predictive Value = 0.858 
 Negative Predictive Value = 0.6923 
 Probability Level = 0.3135  Model = Model  (0.7260) 
 Sensitivity = 0.9917 
 1 - Specificity = 0.8982 
 Frequency = 16 
 Positive Predictive Value = 0.8589 
 Negative Predictive Value = 0.6905 
 Probability Level = 0.318  Model = Model  (0.7260) 
 Sensitivity = 0.9917 
 1 - Specificity = 0.8982 
 Frequency = 16 
 Positive Predictive Value = 0.8589 
 Negative Predictive Value = 0.6905 
 Probability Level = 0.318  Model = Model  (0.7260) 
 Sensitivity = 0.9879 
 1 - Specificity = 0.8632 
 Frequency = 11 
 Positive Predictive Value = 0.8632 
 Negative Predictive Value = 0.6724 
 Probability Level = 0.4081  Model = Model  (0.7260) 
 Sensitivity = 0.9879 
 1 - Specificity = 0.8632 
 Frequency = 11 
 Positive Predictive Value = 0.8632 
 Negative Predictive Value = 0.6724 
 Probability Level = 0.4081  Model = Model  (0.7260) 
 Sensitivity = 0.9847 
 1 - Specificity = 0.8421 
 Frequency = 10 
 Positive Predictive Value = 0.8657 
 Negative Predictive Value = 0.6522 
 Probability Level = 0.4651  Model = Model  (0.7260) 
 Sensitivity = 0.9847 
 1 - Specificity = 0.8421 
 Frequency = 10 
 Positive Predictive Value = 0.8657 
 Negative Predictive Value = 0.6522 
 Probability Level = 0.4651  Model = Model  (0.7260) 
 Sensitivity = 0.9828 
 1 - Specificity = 0.8175 
 Frequency = 33 
 Positive Predictive Value = 0.8689 
 Negative Predictive Value = 0.6582 
 Probability Level = 0.4703  Model = Model  (0.7260) 
 Sensitivity = 0.9828 
 1 - Specificity = 0.8175 
 Frequency = 33 
 Positive Predictive Value = 0.8689 
 Negative Predictive Value = 0.6582 
 Probability Level = 0.4703  Model = Model  (0.7260) 
 Sensitivity = 0.9739 
 1 - Specificity = 0.7509 
 Frequency = 11 
 Positive Predictive Value = 0.8773 
 Negative Predictive Value = 0.6339 
 Probability Level = 0.5252  Model = Model  (0.7260) 
 Sensitivity = 0.9739 
 1 - Specificity = 0.7509 
 Frequency = 11 
 Positive Predictive Value = 0.8773 
 Negative Predictive Value = 0.6339 
 Probability Level = 0.5252  Model = Model  (0.7260) 
 Sensitivity = 0.9707 
 1 - Specificity = 0.7298 
 Frequency = 3 
 Positive Predictive Value = 0.88 
 Negative Predictive Value = 0.626 
 Probability Level = 0.6482  Model = Model  (0.7260) 
 Sensitivity = 0.9707 
 1 - Specificity = 0.7298 
 Frequency = 3 
 Positive Predictive Value = 0.88 
 Negative Predictive Value = 0.626 
 Probability Level = 0.6482  Model = Model  (0.7260) 
 Sensitivity = 0.9701 
 1 - Specificity = 0.7228 
 Frequency = 5 
 Positive Predictive Value = 0.8809 
 Negative Predictive Value = 0.627 
 Probability Level = 0.6612  Model = Model  (0.7260) 
 Sensitivity = 0.9701 
 1 - Specificity = 0.7228 
 Frequency = 5 
 Positive Predictive Value = 0.8809 
 Negative Predictive Value = 0.627 
 Probability Level = 0.6612  Model = Model  (0.7260) 
 Sensitivity = 0.9675 
 1 - Specificity = 0.7193 
 Frequency = 68 
 Positive Predictive Value = 0.8812 
 Negative Predictive Value = 0.6107 
 Probability Level = 0.6781  Model = Model  (0.7260) 
 Sensitivity = 0.9675 
 1 - Specificity = 0.7193 
 Frequency = 68 
 Positive Predictive Value = 0.8812 
 Negative Predictive Value = 0.6107 
 Probability Level = 0.6781  Model = Model  (0.7260) 
 Sensitivity = 0.9389 
 1 - Specificity = 0.6386 
 Frequency = 12 
 Positive Predictive Value = 0.8902 
 Negative Predictive Value = 0.5176 
 Probability Level = 0.7034  Model = Model  (0.7260) 
 Sensitivity = 0.9389 
 1 - Specificity = 0.6386 
 Frequency = 12 
 Positive Predictive Value = 0.8902 
 Negative Predictive Value = 0.5176 
 Probability Level = 0.7034  Model = Model  (0.7260) 
 Sensitivity = 0.9332 
 1 - Specificity = 0.6281 
 Frequency = 2 
 Positive Predictive Value = 0.8912 
 Negative Predictive Value = 0.5024 
 Probability Level = 0.7647  Model = Model  (0.7260) 
 Sensitivity = 0.9332 
 1 - Specificity = 0.6281 
 Frequency = 2 
 Positive Predictive Value = 0.8912 
 Negative Predictive Value = 0.5024 
 Probability Level = 0.7647  Model = Model  (0.7260) 
 Sensitivity = 0.9325 
 1 - Specificity = 0.6246 
 Frequency = 48 
 Positive Predictive Value = 0.8917 
 Negative Predictive Value = 0.5023 
 Probability Level = 0.7651  Model = Model  (0.7260) 
 Sensitivity = 0.9325 
 1 - Specificity = 0.6246 
 Frequency = 48 
 Positive Predictive Value = 0.8917 
 Negative Predictive Value = 0.5023 
 Probability Level = 0.7651  Model = Model  (0.7260) 
 Sensitivity = 0.9096 
 1 - Specificity = 0.5825 
 Frequency = 48 
 Positive Predictive Value = 0.8959 
 Negative Predictive Value = 0.4559 
 Probability Level = 0.7782  Model = Model  (0.7260) 
 Sensitivity = 0.9096 
 1 - Specificity = 0.5825 
 Frequency = 48 
 Positive Predictive Value = 0.8959 
 Negative Predictive Value = 0.4559 
 Probability Level = 0.7782  Model = Model  (0.7260) 
 Sensitivity = 0.8873 
 1 - Specificity = 0.5368 
 Frequency = 15 
 Positive Predictive Value = 0.9011 
 Negative Predictive Value = 0.4272 
 Probability Level = 0.788  Model = Model  (0.7260) 
 Sensitivity = 0.8873 
 1 - Specificity = 0.5368 
 Frequency = 15 
 Positive Predictive Value = 0.9011 
 Negative Predictive Value = 0.4272 
 Probability Level = 0.788  Model = Model  (0.7260) 
 Sensitivity = 0.8784 
 1 - Specificity = 0.5333 
 Frequency = 2 
 Positive Predictive Value = 0.9008 
 Negative Predictive Value = 0.4105 
 Probability Level = 0.8039  Model = Model  (0.7260) 
 Sensitivity = 0.8784 
 1 - Specificity = 0.5333 
 Frequency = 2 
 Positive Predictive Value = 0.9008 
 Negative Predictive Value = 0.4105 
 Probability Level = 0.8039  Model = Model  (0.7260) 
 Sensitivity = 0.8771 
 1 - Specificity = 0.5333 
 Frequency = 4 
 Positive Predictive Value = 0.9007 
 Negative Predictive Value = 0.408 
 Probability Level = 0.8071  Model = Model  (0.7260) 
 Sensitivity = 0.8771 
 1 - Specificity = 0.5333 
 Frequency = 4 
 Positive Predictive Value = 0.9007 
 Negative Predictive Value = 0.408 
 Probability Level = 0.8071  Model = Model  (0.7260) 
 Sensitivity = 0.8759 
 1 - Specificity = 0.5263 
 Frequency = 9 
 Positive Predictive Value = 0.9017 
 Negative Predictive Value = 0.4091 
 Probability Level = 0.8156  Model = Model  (0.7260) 
 Sensitivity = 0.8759 
 1 - Specificity = 0.5263 
 Frequency = 9 
 Positive Predictive Value = 0.9017 
 Negative Predictive Value = 0.4091 
 Probability Level = 0.8156  Model = Model  (0.7260) 
 Sensitivity = 0.8714 
 1 - Specificity = 0.5193 
 Frequency = 66 
 Positive Predictive Value = 0.9024 
 Negative Predictive Value = 0.4041 
 Probability Level = 0.8187  Model = Model  (0.7260) 
 Sensitivity = 0.8714 
 1 - Specificity = 0.5193 
 Frequency = 66 
 Positive Predictive Value = 0.9024 
 Negative Predictive Value = 0.4041 
 Probability Level = 0.8187  Model = Model  (0.7260) 
 Sensitivity = 0.8339 
 1 - Specificity = 0.4947 
 Frequency = 19 
 Positive Predictive Value = 0.9028 
 Negative Predictive Value = 0.3556 
 Probability Level = 0.8443  Model = Model  (0.7260) 
 Sensitivity = 0.8339 
 1 - Specificity = 0.4947 
 Frequency = 19 
 Positive Predictive Value = 0.9028 
 Negative Predictive Value = 0.3556 
 Probability Level = 0.8443  Model = Model  (0.7260) 
 Sensitivity = 0.823 
 1 - Specificity = 0.4877 
 Frequency = 13 
 Positive Predictive Value = 0.9029 
 Negative Predictive Value = 0.3443 
 Probability Level = 0.8609  Model = Model  (0.7260) 
 Sensitivity = 0.823 
 1 - Specificity = 0.4877 
 Frequency = 13 
 Positive Predictive Value = 0.9029 
 Negative Predictive Value = 0.3443 
 Probability Level = 0.8609  Model = Model  (0.7260) 
 Sensitivity = 0.8167 
 1 - Specificity = 0.4772 
 Frequency = 369 
 Positive Predictive Value = 0.9042 
 Negative Predictive Value = 0.341 
 Probability Level = 0.8611  Model = Model  (0.7260) 
 Sensitivity = 0.8167 
 1 - Specificity = 0.4772 
 Frequency = 369 
 Positive Predictive Value = 0.9042 
 Negative Predictive Value = 0.341 
 Probability Level = 0.8611  Model = Model  (0.7260) 
 Sensitivity = 0.6162 
 1 - Specificity = 0.2877 
 Frequency = 5 
 Positive Predictive Value = 0.9219 
 Negative Predictive Value = 0.2519 
 Probability Level = 0.8724  Model = Model  (0.7260) 
 Sensitivity = 0.6162 
 1 - Specificity = 0.2877 
 Frequency = 5 
 Positive Predictive Value = 0.9219 
 Negative Predictive Value = 0.2519 
 Probability Level = 0.8724  Model = Model  (0.7260) 
 Sensitivity = 0.6136 
 1 - Specificity = 0.2842 
 Frequency = 67 
 Positive Predictive Value = 0.9225 
 Negative Predictive Value = 0.2515 
 Probability Level = 0.8747  Model = Model  (0.7260) 
 Sensitivity = 0.6136 
 1 - Specificity = 0.2842 
 Frequency = 67 
 Positive Predictive Value = 0.9225 
 Negative Predictive Value = 0.2515 
 Probability Level = 0.8747  Model = Model  (0.7260) 
 Sensitivity = 0.5761 
 1 - Specificity = 0.2561 
 Frequency = 3 
 Positive Predictive Value = 0.9254 
 Negative Predictive Value = 0.2415 
 Probability Level = 0.8864  Model = Model  (0.7260) 
 Sensitivity = 0.5761 
 1 - Specificity = 0.2561 
 Frequency = 3 
 Positive Predictive Value = 0.9254 
 Negative Predictive Value = 0.2415 
 Probability Level = 0.8864  Model = Model  (0.7260) 
 Sensitivity = 0.5748 
 1 - Specificity = 0.2526 
 Frequency = 21 
 Positive Predictive Value = 0.9262 
 Negative Predictive Value = 0.2418 
 Probability Level = 0.8885  Model = Model  (0.7260) 
 Sensitivity = 0.5748 
 1 - Specificity = 0.2526 
 Frequency = 21 
 Positive Predictive Value = 0.9262 
 Negative Predictive Value = 0.2418 
 Probability Level = 0.8885  Model = Model  (0.7260) 
 Sensitivity = 0.5627 
 1 - Specificity = 0.2456 
 Frequency = 321 
 Positive Predictive Value = 0.9266 
 Negative Predictive Value = 0.2384 
 Probability Level = 0.9117  Model = Model  (0.7260) 
 Sensitivity = 0.5627 
 1 - Specificity = 0.2456 
 Frequency = 321 
 Positive Predictive Value = 0.9266 
 Negative Predictive Value = 0.2384 
 Probability Level = 0.9117  Model = Model  (0.7260) 
 Sensitivity = 0.3736 
 1 - Specificity = 0.1614 
 Frequency = 92 
 Positive Predictive Value = 0.9273 
 Negative Predictive Value = 0.1954 
 Probability Level = 0.9287  Model = Model  (0.7260) 
 Sensitivity = 0.3736 
 1 - Specificity = 0.1614 
 Frequency = 92 
 Positive Predictive Value = 0.9273 
 Negative Predictive Value = 0.1954 
 Probability Level = 0.9287  Model = Model  (0.7260) 
 Sensitivity = 0.3176 
 1 - Specificity = 0.1474 
 Frequency = 537 
 Positive Predictive Value = 0.9224 
 Negative Predictive Value = 0.1848 
 Probability Level = 0.93  Model = Model  (0.7260) 
 Sensitivity = 0.3176 
 1 - Specificity = 0.1474 
 Frequency = 537 
 Positive Predictive Value = 0.9224 
 Negative Predictive Value = 0.1848 
 Probability Level = 0.93  Model = Model  (0.7260) 
 Sensitivity = 0.0025 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1539 
 Probability Level = 1  Model = Model  (0.7260) 
 Sensitivity = 0.0025 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1539 
 Probability Level = 1  Model = Model  (0.7260) 
 Sensitivity = 0.0019 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1538 
 Probability Level = 1  Model = Model  (0.7260) 
 Sensitivity = 0.0019 
 1 - Specificity = 0 
 Frequency = 2 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1538 
 Probability Level = 1  Model = Model  (0.7260) 
 Sensitivity = .00064 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1536 
 Probability Level = 1  Model = Model  (0.7260) 
 Sensitivity = .00064 
 1 - Specificity = 0 
 Frequency = 1 
 Positive Predictive Value = 1 
 Negative Predictive Value = 0.1536 
 Probability Level = 1  Model = Model  (0.7260) 
 Sensitivity = 0 
 1 - Specificity = 0 
 Frequency = 0 
 Positive Predictive Value = . 
 Negative Predictive Value = 5.5123 
 Probability Level = 1  Slope = 1 
 Y = 0
ROC Curve for Model

Influence Plots

Pearson Residuals

 Y = 0
Plot of Pearson Chi-Square Residuals by Case Number.

Deviance Residuals

 Y = 0
Plot of Deviance Residuals by Case Number.

Leverage

Plot of Leverage by Case Number.

C

Plot of Confidence Interval Displacements C by Case Number.

CBar

Plot of Confidence Interval Displacements CBar by Case Number.

Chi-Square Differences

Plot of Pearson Chi-square Deletion Differences by Case Number.

Deviance Differences

Plot of Deviance Deletion Differences by Case Number.