An Introduction to Generalized Linear Models

by George H. Dunteman

• ISBN13: 9780761920847
• ISBN10: 0761920846
• eBook ISBN(s): 9781506320748
• Edition: 1st
• Format: Paperback
• Copyright: 2005-09-22
• Publisher: Sage Publications, Inc

Summary

Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets. The book provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation; includes discussion on checking model adequacy and description on how to use SAS to fit GLM; and describes the connection between survival analysis and GLM. It is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

Author Biography

Moon-Ho R. Ho is Assistant Professor of Psychology at McGill University in Canada and Assistant Professor in the School of Humanities and Social Sciences at Nanyang Technological University and the Center for Research in Pedagogy and Practice at the National Institute of Education in Singapore. George H. Dunteman was Chief Scientist at the Research Triangle Institute.

Table of Contents

List of Figures and Tables	vii
Series Editor's Introduction	viii
Acknowledgments	x
1. Generalized Linear Models	1	(5)
2. Some Basic Modeling Concepts	6	(4)
Categorical Independent Variables	7	(2)
Essential Components of Regression Modeling	9	(1)
3. Classical Multiple Regression Model	10	(8)
Assumptions and Modeling Approach	12	(1)
Results of Regression Analysis	13	(1)
Multiple Correlation	14	(1)
Testing Hypotheses	15	(3)
4. Fundamentals of Generalized Linear Modeling	18	(5)
Exponential Family of Distributions	20	(1)
Classical Normal Regression	21	(1)
Logistic Regression	22	(1)
Poisson Regression	22	(1)
Proportional Hazards Survival Model	23	(1)
5. Maximum Likelihood Estimation	23	(8)
6. Deviance and Goodness of Fit	31	(4)
Using Deviances to Test Statistical Hypotheses	32	(1)
Goodness of Fit	33	(1)
Assessing Goodness of Fit by Residual Analysis	33	(2)
7. Logistic Regression	35	(8)
Example of Logistic Regression	39	(4)
8. Poisson Regression	43	(9)
Example of Poisson Regression Model	45	(7)
9. Survival Analysis	52	(10)
Survival Time Distributions	53	(2)
Exponential Survival Model	55	(4)
Example of Exponential Survival Model	59	(3)
Conclusions	62	(1)
Appendix	63	(6)
References	69	(1)
Index	70	(2)
About the Authors	72