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.