Applied Linear Regression 4E

By Sanford Weisberg

Book Overview

Praise for the Third Edition

"...this is an excellent book which could easily be used as a course text..."
--International Statistical Institute

The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples.

Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition, Applied Linear Regression, Fourth Edition features:

Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics Newly added material on topics including testing, ANOVA, and variance assumptions Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods

Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.

• Format: Hardcover
• Language: English
• ISBN: 1118386086
• ISBN13: 9781118386088
• Release Date: December 2013
• Publisher: Wiley
• Length: 368 Pages
• Weight: 1.44 lbs.
• Dimensions: 0.9" x 6.2" x 9.3"

Customer Reviews

Good overview

Weisberg's book is a good introduction/overview of the principal techniques used in linear models. However, in many situations, he leaves out many details and derivations and either directs the reader to the references or says nothing. I would have liked it if he had put in a few more mathematical derivations and had been more thorough in the discussion about collinearity and variable selection.

An excellent introduction to linear regression

This book provides an excellent introduction to the application of linear regression models. Starting with the idea of studying "relationships between measurable variables", it develops the concepts behind simple and multiple linear regression, discusses interpretations of the output, proscribes diagnostics, discusses model building techniques, and touches other topics. It covers most of the material contained in Draper & Smith's _Applied Regression Analysis_, but in half the number of pages. I would wholeheartedly endorse this book as a primary or supplemental text to an introductory course, or as a text for someone who wants to learn about linear regression on their own.