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Table of contents

  1. Front Matter
    Pages i-xx
  2. Peter K. Dunn, Gordon K. Smyth
    Pages 1-30
  3. Peter K. Dunn, Gordon K. Smyth
    Pages 31-91
  4. Peter K. Dunn, Gordon K. Smyth
    Pages 93-164
  5. Peter K. Dunn, Gordon K. Smyth
    Pages 165-209
  6. Peter K. Dunn, Gordon K. Smyth
    Pages 211-241
  7. Peter K. Dunn, Gordon K. Smyth
    Pages 243-263
  8. Peter K. Dunn, Gordon K. Smyth
    Pages 265-296
  9. Peter K. Dunn, Gordon K. Smyth
    Pages 297-331
  10. Peter K. Dunn, Gordon K. Smyth
    Pages 333-369
  11. Peter K. Dunn, Gordon K. Smyth
    Pages 371-424
  12. Peter K. Dunn, Gordon K. Smyth
    Pages 425-456
  13. Peter K. Dunn, Gordon K. Smyth
    Pages 457-490
  14. Peter K. Dunn, Gordon K. Smyth
    Pages 491-501
  15. Back Matter
    Pages 503-562

About this book

Introduction

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.  

Keywords

generalized linear models linear regression Tweedie family distribution Saddlepoint approximation likelihood score tests Randomized quantile residuals

Authors and affiliations

  • Peter K. Dunn
    • 1
  • Gordon K.  Smyth
    • 2
  1. 1.Faculty of Science, Health, Education and EngineeringSchool of Health of Sport Science, University of the Sunshine CoastAustralia
  2. 2.Bioinformatics DivisionWalter and Eliza Hall Institute of Medical ResearchParkvilleAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-0118-7
  • Copyright Information Springer Science+Business Media, LLC, part of Springer Nature 2018
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-0117-0
  • Online ISBN 978-1-4419-0118-7
  • Series Print ISSN 1431-875X
  • Series Online ISSN 2197-4136
  • Buy this book on publisher's site