Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models. Thoroughly revised and updated, An Introduction to Generalized Linear Models, Second Edition continues to initiate intermediate students of statistics, and the many other disciplines that use statistics, in the practical use of these models and methods. The new edition incorporates many of the important developments of the last decade, including survival analysis, nominal and ordinal logistic regression, generalized estimating equations, and multi-level models. It also includes modern methods for checking model adequacy and examples from an even wider range of application. Statistics can appear to the uninitiated as a collection of unrelated tools. An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and the principles of statistical modelling.
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.
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.