Generalized Linear Models With Examples in R

• Main Authors: Dunn, Peter K. (Author, http://id.loc.gov/vocabulary/relators/aut), Smyth, Gordon K. (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Springer Texts in Statistics,
• Subjects: Statistics .; Statistical Theory and Methods.; Statistics and Computing/Statistics Programs.

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 p...

Table of 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. .