Table of Contents

List of Figures and Tables
Series Editor’s Introduction
Acknowledgments
1. Generalized Linear Models
2. Some Basic Modeling Concepts
Categorical Independent Variables
Essential Components of Regression Modeling
3. Classical Multiple Regression Model
Assumptions and Modeling Approach
Results of Regression Analysis
Multiple Correlation
Testing Hypotheses
4. Fundamentals of Generalized Linear Modeling
Exponential Family of Distributions
Classical Normal Regression
Logistic Regression
Poisson Regression
Proportional Hazards Survival Model
5. Maximum Likelihood Estimation
6. Deviance and Goodness of Fit
Using Deviances to Test Statistical Hypotheses
Goodness of Fit
Assessing Goodness of Fit by Residual Analysis
7. Logistic Regression
Example of Logistic Regression
8. Poisson Regression
Example of Poisson Regression Model
9. Survival Analysis
Survival Time Distributions
Exponential Survival Model
Example of Exponential Survival Model
Conclusions
Appendix
References
Index
About the Authors

About the Author

Prof. Ho′s research concerns with the development and application of quantitative methods in the neural and behavioral sciences. His current research interests include effective connectivity analysis in fMRI experiments, social network analysis, statistical approach for testing mathematical axioms, diagnostics in nonlinear SEM.