This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities.
Other features include:
Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals
Nearly 100 data sets in the companion R package GLMsData
Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session
| ISBN: | 9781441901170 |
| Publication date: | 11th November 2018 |
| Author: | Peter K Dunn, Gordon K Smyth |
| Publisher: | Springer an imprint of Springer New York |
| Format: | Hardback |
| Pagination: | 562 pages |
| Series: | Springer Texts in Statistics |
This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities.
Other features include:
Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals
Nearly 100 data sets in the companion R package GLMsData
Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session
Generalized Linear Models With Examples in R is available in Hardback
Generalized Linear Models With Examples in R was written by Peter K Dunn, Gordon K Smyth and published by Springer an imprint of Springer New York
Generalized Linear Models With Examples in R has 562 pages
Yes it is part of Springer Texts in Statistics series
£98.99