Generalized Linear Models With Examples in R
Authors: Dunn, Peter, Smyth, Gordon
Free Preview This book eases students into GLMs and demonstrates the need for GLMs by starting with regression
 Shows how to implement the principles in R
 Clearly written and logically structured to aid understanding
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 About this Textbook

This textbook presents an introduction to multiple linear regression, providing realworld data sets and practice problems. A practical working knowledge of applied statistical practice is developed through the use of these data sets and numerous case studies. The authors include a set of practice problems both at the end of each chapter and at the end of the book. Each example in the text is crossreferenced with the relevant data set, so that readers can load the data and follow the analysis in their own R sessions. The balance between theory and practice is evident in the list of problems, which vary in difficulty and purpose.
This book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples. Focusing on the connections between generalized linear models (GLMs) and linear regression, the book also references advanced topics and tools that have not typically been included in introductions to GLMs to date, such as Tweedie family distributions with power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, and randomized quantile residuals. In addition, the authors introduce the new R code package, GLMsData, created specifically for this book. Generalized Linear Models with Examples in R balances theory with practice, making it ideal for both introductory and graduatelevel students who have a basic knowledge of matrix algebra, calculus, and statistics.
 About the authors

Peter K. Dunn is Associate Professor in the Faculty of Science, Health, Education and Engineering at the University of the Sunshine Coast. His work focuses on mathematical statistics, in particular generalized linear models. He has developed methods for accurate numerical evaluation of the densities of the Tweedie distributions, leading to a better understanding of these distributions. An engaging teacher, Dunn is the recipient of an Australian Office of Learning and Teaching citation. He has also won several conference paper prizes, including the EJ Pitman Prize at the Australian Statistics Conference. He is a member of the Statistical Society of Australia Inc. and the Australian Mathematics Society.
Gordon K. Smyth is Head of the Bioinformatics Division at the Walter and Eliza Hall Institute of Medical Research and Honorary Professor of Mathematics & Statistics at The University of Melbourne. He has published research on generalized linear models and statistical computing for over 30 years and is the author of several popular R packages. In recent years, he has particularly promoted the use of generalized linear models to model data from genomic sequencing technologies.
 Table of contents (13 chapters)


Chapter 1: Statistical Models
Pages 130

Chapter 2: Linear Regression Models
Pages 3191

Chapter 3: Linear Regression Models: Diagnostics and ModelBuilding
Pages 93164

Chapter 4: Beyond Linear Regression: The Method of Maximum Likelihood
Pages 165209

Chapter 5: Generalized Linear Models: Structure
Pages 211241

Table of contents (13 chapters)
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Bibliographic Information
 Bibliographic Information

 Book Title
 Generalized Linear Models With Examples in R
 Authors

 Peter Dunn
 Gordon Smyth
 Series Title
 Springer Texts in Statistics
 Copyright
 2018
 Publisher
 SpringerVerlag New York
 Copyright Holder
 Springer Science+Business Media, LLC, part of Springer Nature
 eBook ISBN
 9781441901187
 DOI
 10.1007/9781441901187
 Hardcover ISBN
 9781441901170
 Series ISSN
 1431875X
 Edition Number
 1
 Number of Pages
 XX, 562
 Number of Illustrations
 115 b/w illustrations
 Topics