Generalized Linear Models In R by Nathaniel Helwig
Generalized Linear Models In R - Table of Contents
- PART 1: OVERVIEW OF GLMS
- 1.1 Preliminaries
- 1.2 Exponential Dispersion GLM Families
- 1.3 GLM Model Evaluation and Deviance
- 1.4 Variable Selection Techniques
- 1.5 Binary Response Variable (Logistic Regression)
- 1.5.1 Binary Model Diagnostics
- 1.6 Count Response Variable
- 1.6.1 Quasi-Poisson Model
- 1.6.2 Negative Binomial Model
- 1.6.3 Count Model Diagnostics
- 1.7 Chapter Exercises
- PART 2: SOLUTIONS OF EXERCISES
- 2. Complete Solutions to Exercises
What You Will Learn in Generalized Linear Models In R
Generalized Linear Models With Examples In R by Nathaniel E. Helwig is an outstanding advanced statistical textbook designed to provide students and applied statisticians with a thorough grounding in GLM methodology. Expanding beyond standard ordinary least squares, this comprehensive text breaks down critical framework components including exponential dispersion families, link functions, maximum likelihood estimation, deviance analysis, and model diagnostic procedures into structured, computationally focused modules.
Designed for graduate and advanced undergraduate students in statistics, psychology, data science, biostatistics, and quantitative social sciences, this book balances formal mathematical proof with immediate practical execution in R. Dr. Helwig systematically leads readers through binary response models, multinomial classification, Poisson and negative binomial regression for count data, and gamma models for continuous skewed outcomes. Whether checking deviance residuals or evaluating overdispersion, researchers will find this resource indispensable.
Highly regarded for its mathematical precision, clear exposition, and integration of statistical computing, it stands as one of the best generalized linear models in R books pdf available for independent study. It systematically equips learners with necessary analytical tools for mastering non-normal regression modeling with confidence.
Book Details & Specifications
Title:
Generalized Linear Models In R by Nathaniel Helwig
Publisher:
University of Wisconsin
Year:
2021
Pages:
100
Type:
PDF
Language:
English
ISBN-10 #:
1441901175
ISBN-13 #:
978-1441901170
License:
External Educational Resource
Amazon:
Amazon
About the Author: Nathaniel E. Helwig
The author Nathaniel E. Helwig
Nathaniel E. Helwig is an Associate Professor in the Departments of Psychology and Statistics at the University of Minnesota, Twin Cities. He received his Ph.D. in Quantitative Psychology with a minor in Statistics from the University of Illinois at Urbana-Champaign.
An expert in computational statistics, psychometrics, and multivariate analysis, Professor Helwig has developed popular open-source R packages for smoothing, optimization, and factor analysis. His work on generalized linear models and applied statistical computing provides students worldwide with mathematically precise, accessible pedagogical resources.
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