Generalized Linear Models With Examples in R / by Peter K. Dunn, Gordon K. Smyth.

Format:

Book

Author/Creator:

Dunn, Peter K., author.

Smyth, Gordon K., author.

Contributor:

SpringerLink (Online service)

Series:

Mathematics and Statistics (Springer-11649)

Springer texts in statistics 1431-875X

Springer Texts in Statistics, 1431-875X

Language:

English

Subjects (All):

Statistics.

Statistical Theory and Methods.

Statistics and Computing/Statistics Programs.

Local Subjects:

Statistical Theory and Methods.

Statistics and Computing/Statistics Programs.

Physical Description:

1 online resource (XX, 562 pages) : 115 illustrations.

Edition:

First edition 2018.

Contained In:

Springer eBooks

Place of Publication:

New York, NY : Springer New York : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

This textbook presents an introduction to multiple linear regression, providing real-world 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 cross-referenced 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 graduate-level students who have a basic knowledge of matrix algebra, calculus, and statistics. .

Contents:

Statistical models

Linear regression models

Linear regression models: diagnostics and model-building

Beyond linear regression: the method of maximum likelihood

Generalized linear models: structure

Generalized linear models: estimation

Generalized linear models: inference

Generalized linear models: diagnostics

Models for proportions: binomial GLMs

Models for counts: Poisson and negative binomial GLMs

Positive continuous data: gamma and inverse Gaussian GLMs

Tweedie GLMs

Extra problems

Appendix A: Using R for data analysis

Appendix B: The GLMsData package

Index: Data sets

Index: R commands

Index: General Topics. .

Other Format:

Printed edition:

ISBN:

978-1-4419-0118-7

9781441901187

Access Restriction:

Restricted for use by site license.