Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The aut
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This book presents a well-structured introduction to both general linear models and generalized linear models. ... I would recommend the book as a suitable text for senior undergraduate or postgraduate students studying statistics or a reference for researchers in areas of statistics and its applications.
-Shuangzhe Liu, International Statistical Review, 2012
This book is targeted to undergraduates in statistics but can be used by researchers as a reference manual as well. It is well written, easy to read and the discussion of the examples is clear. As a complement there is a collection of slides for an introductory course on general, generalized, and mixed effects models in the homepage cited in the preface of this book. This book has a good set of references ... I recommend this book as one of the textbooks to be discussed in a course for model building.
-Clarice G.B. Demétrio, Biometrics, February 2012
-Shuangzhe Liu, International Statistical Review, 2012
This book is targeted to undergraduates in statistics but can be used by researchers as a reference manual as well. It is well written, easy to read and the discussion of the examples is clear. As a complement there is a collection of slides for an introductory course on general, generalized, and mixed effects models in the homepage cited in the preface of this book. This book has a good set of references ... I recommend this book as one of the textbooks to be discussed in a course for model building.
-Clarice G.B. Demétrio, Biometrics, February 2012