Introduction to Linear Regression Analysis, 3rd Edition (Hardcover)

by Douglas C. Montgomery (Author), Elizabeth A. Peck (Author), G. Geoffrey Vining (Author) "Regression analysis is a statistical technique for investigating and modeling the relationship between variables..." (more)
Key Phrases: delivery time data, acetylene data, propellant data, Appendix Table, National Football League, Corrected Total (more...)

Editorial Reviews

Review
"I highly recommend the third edition of Introduction to Linear Regression Analysis to practitioners, students, and teachers of regression analysis." -- Technometrics, May 2002

"an excellent text" -- Zentrablatt Math, Vol. 980, No. 05, 2002

“…an excellent textbook…I would certainly recommend it for educational as well as for applied research purposes” (Statistical Methods in Medical Research, No.13, 2004)

"...[the authors] describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research." (SciTech Book News Vol. 25, No. 2 June 2001)

"an excellent text" (Zentrablatt Math, Vol. 980, No. 05, 2002)

"...a very good book..." (The American Statistician, Vol. 57, No. 1, February 2003)

Product Description
A comprehensive and thoroughly up-to-date look at regression analysis-still the most widely used technique in statistics today
As basic to statistics as the Pythagorean theorem is to geometry, regression analysis is a statistical technique for investigating and modeling the relationship between variables. With far-reaching applications in almost every field, regression analysis is used in engineering, the physical and chemical sciences, economics, management, life and biological sciences, and the social sciences.
Clearly balancing theory with applications, Introduction to Linear Regression Analysis describes conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. Succeeding chapters include detailed coverage of:
* Indicator variables, making the connection between regression and analysis-of-variance modelss
* Variable selection and model-building techniques
* The multicollinearity problem, including its sources, harmful effects, diagnostics, and remedial measures
* Robust regression techniques, including M-estimators, Least Median of Squares, and S-estimation
* Generalized linear models
The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation. Topics not usually found in a linear regression textbook, such as nonlinear regression and generalized linear models, yet critical to engineering students and professionals, have also been included. The new critical role of the computer in regression analysis is reflected in the book's expanded discussion of regression diagnostics, where major analytical procedures now available in contemporary software packages, such as SAS, Minitab, and S-Plus, are detailed. The Appendix now includes ample background material on the theory of linear models underlying regression analysis. Data sets from the book, extensive problem solutions, and software hints are available on the ftp site.

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Product Details

• Hardcover: 672 pages
• Publisher: Wiley-Interscience; 3 edition (April 2, 2001)
• Language: English
• ISBN-10: 0471315656
• ISBN-13: 978-0471315650
• Product Dimensions: 9.5 x 6.3 x 1.4 inches
• Shipping Weight: 2.6 pounds
• Average Customer Review: 3.8 out of 5 stars See all reviews (4 customer reviews)
• Amazon.com Sales Rank: #327,920 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

13 of 16 people found the following review helpful:

5.0 out of 5 stars Excellent introduction to linear regression, January 11, 2005

By	Dr. Jeffrey O. Katz - See all my reviews (REAL NAME)

If you have a desire or need to develop regression models, whether for prediction or classification, this is a great place to start climbing the learning curve. The book covers all the essentials, such as how to fit a model to a set of data, how to evaluate the quality of the fit, and how to detect influential data points. It also does a good job with some of the issues involved in fitting a regression (most notably colinearity, overfitting, outliers, and deviations from normality) and discusses ridge regression, principal components regression, and other so-called "robust" methods for dealing with such issues. Even if you plan to use nonlinear modelling techniques like polynomial regression or feed-forward neural networks, this book is worth reading: many of the same issues that are involved when developing linear regression models arise in the context of nonlinear models. I use multivariate polynomial regression models for pricing options, and cite this book in my own recent work on that subject--"Advanced Option Pricing Models" (McGraw Hill, Feb 2005).

Jeffrey Owen Katz, Ph.D.
Author (with Donna L. McCormick) of "The Encyclopedia of Trading Strategies" (McGraw Hill, 2000).

2 of 2 people found the following review helpful:

4.0 out of 5 stars A good book with industrial applications, January 9, 2007

By	Xiaobo Wang (La Crosse, WI USA) - See all my reviews (REAL NAME)

This review is from: Introduction to Linear Regression Analysis (Wiley Series in Probability and Statistics) (Hardcover)

very useful for industrial applications. There are quite a few printing mistakes and that would be a problem for those reader they are not very strong in statistics.

4.0 out of 5 stars Good book, June 28, 2009

By	M. Francois - See all my reviews (REAL NAME)

This review is from: Introduction to Linear Regression Analysis (Wiley Series in Probability and Statistics) (Hardcover)

This is a good book with good exercises in the end of the chapters, but a little hard to read.