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Series Editor's Introduction | ||
1 | Introduction | 1 |
Analysis of a Two-Way Table | 3 | |
Chi-Squared as a Goodness-of-Fit Test | 5 | |
Measuring the Effects of Predictors | 6 | |
2 | Logit Models: Theoretical Background | 7 |
The Loglinear Model for Two-Way Tables | 7 | |
The Loglinear Model for Three-Way Tables | 15 | |
The Logit Model for a Three-Way Table | 18 | |
Parameter Interpretation in Terms of Odds Ratios | 23 | |
Pairwise Comparisons of Odds | 24 | |
Predictive Efficacy | 26 | |
3 | Logit Models for Multidimensional Tables | 28 |
Model Fitting | 29 | |
Interpretation of First-Order Interaction | 33 | |
Modeling Ordinal Predictors | 37 | |
Sample Size Considerations | 41 | |
4 | Logistic Regression | 42 |
Logistic Regression for a Binary Response | 43 | |
The Model | 44 | |
Estimation Via Maximum Likelihood | 45 | |
Logistic Regression for Cities Data | 46 | |
Predictive Efficacy in Logistic Regression | 53 | |
Evaluating the Effects of Intervening Variables | 56 | |
5 | Advanced Topics in Logistic Regression | 60 |
Polytomous Logistic Regression | 61 | |
Ordered Logit Modeling | 71 | |
Concluding Comments | 78 | |
Appendix: Computer Routines for Chapters 2-5 | 79 | |
Notes | 83 | |
References | 85 | |
About the Author | 87 |
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