Logistic Regression Using the SAS System: Theory and Application

REFERENCES

Acknowledgments

Chapter 1: Introduction

1.1 What This Book Is About
1.2 What This Book Is Not About
1.3 What You Need to Know
1.4 Computing
1.5 References

Chapter 2: Binary Logit Analysis: Basics

2.1 Introduction
2.2 Dichotomous Dependent Variables: Example
2.3 Problems with Ordinary Linear Regression
2.4 Odds and Odds Ratios
2.5 The Logit Model
2.6 Estimation of the Logit Model: General Principles
2.7 Maximum Likelihood Estimation with PROC LOGISTIC
2.8 Maximum Likelihood Estimation with PROC GENMOD
2.9 Interpreting Coefficients

Chapter 3: Binary Logit Analysis: Details and Options

3.1 Introduction
3.2 Confidence Intervals
3.3 Details of Maximum Likelihood Estimation
3.4 Convergence Problems
3.5 Multicollinearity
3.6 Goodness-of-Fit Statistics
3.7 Statistics Measuring Predictive Power
3.8 Predicted Values, Residuals, and Influence Statistics
3.9 Latent Variables and Standardized Coefficients
3.10 Probit and Complementary Log-Log Models
3.11 Unobserved Heterogeneity
3.13 Sampling on the Dependent Variable

Chapter 4: Logit Analysis of Contingency Tables

4.1 Introduction
4.2 A Logit Model for a 2 X 2 Table
4.3 A Three-Way Table
4.4 A Four-Way Table
4.5 A Four-Way Table with Ordinal Explanatory Variables
4.6 Overdispersion

Chapter 5: Multinomial Logit Analysis

5.1 Introduction
5.2 Example
5.3 A Model for Three Categories
5.4 Estimation with CATMOD
5.5 Estimation with a Binary Logit Procedure
5.6 General Form of the Model
5.7 Contingency Table Analysis
5.8 CATMOD Coding of Categorical Variables
5.9 Problems of Interpretation

Chapter 6: Logit Analysis for Ordered Categories

6.1 Introduction
6.2 Cumulative Logit Model: Example
6.3 Cumulative Logit Model: Explanation
6.4 Cumulative Logit Model: Practical Considerations
6.5 Cumulative Logit Model: Contingency Tables
6.6 Adjacent Categories Model
6.7 Continuation Ratio Model

Chapter 7: Discrete Choice Analysis

7.1 Introduction
7.2 Chocolate Example
7.3 Model and Estimation
7.4 Travel Example
7.5 Other Applications
7.6 Ranked Data

Chapter 8: Logit Analysis of Longitudinal and Other Clustered Data

8.1 Introduction
8.2 Longitudinal Example
8.3 GEE Estimation
8.4 Fixed-Effects with Conditional Logit Analysis
8.5 Postdoctoral Training Example
8.6 Matching
8.7 Mixed Logit Models
8.8 Comparison of Methods
8.9 A Hybrid Method

Chapter 9: Poisson Regression

9.1 Introduction
9.2 The Poisson Regression Model
9.3 Scientific Productivity Example
9.4 Overdispersion
9.5 Negative Binomial Regression
9.6 Adjustment for Varying Time Spans

Chapter 10: Loglinear Analysis of Contingency Tables

10.1 Introduction
10.2 A Loglinear Model for a 2 X 2 Table
10.3 Loglinear Models for a Four-Way Table
10.4 Fitting the Adjacent Categories Model as a Loglinear Model
10.5 Loglinear Models for Square, Ordered Tables
10.6 Marginal Tables
10.7 The Problem of Zeros
10.8 GENMOD versus CATMOD

Appendix

References

Index