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An Introduction to Generalized Linear Models, First Edition
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.
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.
176 pages, Hardcover
First published February 1, 1990
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Displaying 1 - 3 of 3 reviews
December 23, 2019
Nearly 20 years since I learnt about GLMs with the first edition of this as a text. I just re-read it and while naturally dated with regard to software and recent developments (regularisation etc) it is still an excellent, clear introduction. I haven't seen recent editions but am sure they are good too. Some linear algebra and calculus required.
February 18, 2023
A very simple and good book, which introduce the GLMs in a very easy way. Very recomendable.
October 4, 2014
An entry level for generalized linear models. The books covers all the essential things you should know about GLM but ignores many necessary details for the beginners. It is a good choice for beginners who want a quick survey about GLM.
Displaying 1 - 3 of 3 reviews