An Introduction to Generalized Linear Models, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

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Bibliographic Details

Title: An Introduction to Generalized Linear Models, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
Publisher: Chapman and Hall/CRC
Publication Date: 2001
Language: English
ISBN 10: 1584881658
ISBN 13: 9781584881650
Binding: Paperback
Edition: 2nd Edition
Condition: Very Good

About this title

Synopsis

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.

About the Author

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

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