An Introduction to Generalized Linear Models, First Edition (Chapman & Hall/CRC Texts in Statistical Science)

Hardcover

ISBN 10: 0412311100 ISBN 13: 9780412311109

Publisher: Chapman and Hall/CRC, 1990

An undergraduate-level introduction to the topic of generalized linear models
An Introduction to Generalized Linear Models-a new edition of An Introduction to Statistical Modelling-demonstrates how generalized linear models provide a unifying framework for many commonly used multivariate statistical methods, including multiple regression and analysis of variance or covariance for continuous response data, logistic regression for binary responses, and log-linear models for counted responses. The theory for these models is developed using the exponential family of distributions, maximum likelihood estimation, and likelihood ration tests. Chapters on each of the main types of generalized linear models are included. The statistical computing program GLIM , developed to fit these models to data, is used extensively. Other programs, particularly MINITAB, are used to illustrate particular issues. The student is assumed to have a working knowledge of basic statistical concepts and methods, at the level of most introductory statistics courses, and some acquaintance with calculus and matrix algebra. Methods described in this text are widely applicable for analyzing data from the fields of medicine, agriculture, biology, engineering, industrial experimentation, and the social sciences.

About the Author:

Dobson, Department of Statistics and Centre for Clinical Epidemiology and Biostatistics, University of Newcastle, Australia.

Review:

... a unique and useful text for intermediate undergraduate teaching.
-Times Higher Education Supplement

...I liked Dobson's basic and relatively brief presentation...Thanks go to the publisher for the softcover edition and attendant modest price, another of the book's virtues besides its brevity. These attributes make this book a recommended purchase for those who need a book on logistic regression. It is a good place to start.
-Technometrics, November 2002