9781584884248-158488424X-Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Chapman & Hall/CRC Texts in Statistical Science)

Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Chapman & Hall/CRC Texts in Statistical Science)

ISBN-13: 9781584884248
ISBN-10: 158488424X
Edition: 1
Author: Faraway, Julian J.
Publication date: 2005
Publisher: Chapman and Hall/CRC
Format: Hardcover 312 pages
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Book details

ISBN-13: 9781584884248
ISBN-10: 158488424X
Edition: 1
Author: Faraway, Julian J.
Publication date: 2005
Publisher: Chapman and Hall/CRC
Format: Hardcover 312 pages

Summary

Acknowledged authors Faraway, Julian J. wrote Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Chapman & Hall/CRC Texts in Statistical Science) comprising 312 pages back in 2005. Textbook and eTextbook are published under ISBN 158488424X and 9781584884248. Since then Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Chapman & Hall/CRC Texts in Statistical Science) textbook was available to sell back to BooksRun online for the top buyback price of $ 11.49 or rent at the marketplace.

Description

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies.

Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/

Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.

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