Go to main content

Generalized linear models with examples in R / Peter K. Dunn, Gordon K. Smyth.

Dunn, Peter K. author.; Smyth, Gordon K. author.
2018
QA279
Available Online
Formats
Format
BibTeX
MARCXML
TextMARC
MARC
DublinCore
EndNote
NLM
RefWorks
RIS
Add to Basket
Cite

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
Generalized linear models with examples in R / Peter K. Dunn, Gordon K. Smyth.
Author
Dunn, Peter K. author.
ISBN
9781441901187 (electronic book)
1441901183 (electronic book)
9781441901170
DOI
https://doi.org/10.1007/978-1-4419-0118-7
Published
New York, NY : Springer, 2018.
Language
English
Description
1 online resource (xx, 562 pages) : illustrations.
Item Number
10.1007/978-1-4419-0118-7 doi
Call Number
QA279
Dewey Decimal Classification
519.5
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.-- Provided by publisher.
Bibliography, etc. Note
Includes bibliographical references and indexes.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed November 19, 2018).
Added Author
Smyth, Gordon K. author.
Series
Springer texts in statistics.
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export