Overview
R functions are available for download from author's website
Includes an extensive bibliography
Problems are provided at the end of every chapter
Includes supplementary material: sn.pub/extras
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models.
There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response transformations for multiple linear regression or experimental design models.This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics.
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Bibliographic Information
Book Title: Linear Regression
Authors: David J. Olive
DOI: https://doi.org/10.1007/978-3-319-55252-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-55250-7Published: 25 April 2017
Softcover ISBN: 978-3-319-85608-7Published: 25 July 2018
eBook ISBN: 978-3-319-55252-1Published: 18 April 2017
Edition Number: 1
Number of Pages: XIV, 494
Number of Illustrations: 57 b/w illustrations
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs