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Tables of Contents for Applied Logistic Regression Analysis
Chapter/Section Title
Page #
Page Count
Series Editor's Introduction
v
Author's Introduction to the Second Edition
vii
Linear Regression and the Logistic Regression Model
1
16
Regression Assumptions
4
7
Nonlinear Relationships and Variable Transformations
11
1
Probabilities, Odds, Odds Ratios, and the Logit Transformation for Dichotomous Dependent Variables
12
2
Logistic Regression: A First Look
14
3
Summary Statistics for Evaluating the Logistic Regression Model
17
24
R2, F, and Sums of squared Errors
18
2
Goodness of Fit: GM, R2L, and the Log Likelihood
20
7
Predictive Efficiency: λp, τp, φp, and the Binomial Test
27
9
Examples: Assessing the Adequacy of Logistic Regression Models
36
5
Conclusion: Summary Measures for Evaluating the Logistic Regression Model
41
1
Interpreting the Logistic Regression Coefficients
41
26
Statistical Significance in Logistic Regression Analysis
43
5
Interpreting Unstandardized Logistic Regression Coefficients
48
3
Substantive Significance and Standardized Coefficients
51
5
Exponentiated Coefficients or Odds Ratios
56
1
More on Categorical Predictors: Contrasts and Interpretation
57
4
Interaction Effects
61
2
Stepwise Logistic Regression
63
4
An Introduction to Logistic Regression Diagnostics
67
24
Specification Error
67
8
Collinearity
75
3
Numerical Problems: Zero Cells and Complete Separation
78
2
Analysis of Residuals
80
9
Overdispersion and Underdispersion
89
1
A Suggested Protocol for Logistic Regression Diagnostics
90
1
Polytomous Logistic Regression and Alternatives to Logistic Regression
91
12
Polytomous Nominal Dependent Variables
94
3
Polytomous or Multinomial Ordinal Dependent Variables
97
4
Conclusion
101
2
Notes
103
4
Appendix: Probabilities
107
1
References
108
3
About the Author
111