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Introduction to Linear Regression Analysis, 3rd Edition, by Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining
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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. For other Wiley books by Doug Montgomery, visit our website at www.wiley.com/college/montgomery.
- Sales Rank: #239146 in Books
- Published on: 2001-04-02
- Original language: English
- Number of items: 1
- Dimensions: 10.26" h x 1.35" w x 6.42" l, 1.10 pounds
- Binding: Hardcover
- 672 pages
Review
“…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)
From the Back Cover
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. For other Wiley books by Doug Montgomery, visit our website at www.wiley.com/college/montgomery.
About the Author
DOUGLAS C. MONTGOMERY is Professor in the Department of Industrial Engineering, Arizona State University.
ELIZABETH A. PECK is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia.
G. GEOFFREY VINING is Professor and Head of the Department of Statistics, Virginia Tech.
Most helpful customer reviews
19 of 22 people found the following review helpful.
Excellent introduction to linear regression
By Dr. Jeffrey O. Katz
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).
7 of 7 people found the following review helpful.
For Self Study Get An Earlier Edition
By Railbird
I have access to this, the third edition and the latest, the fourth edition, through my company's library. There is really no material difference in the content and I was able to save about 80% of the purchase price by buying a used copy of the third edition, vs. new copy of fourth edition.
Wonderful book for self study. You will benefit most if you have a good background in probability theory and linear algebra and want to understand the details and language of linear regression. Even without that background chapters one through three will teach you more than you will ever learn in most survey courses in statistics. To fully appreciate the whole book I think you need a one semester course in linear algebra and one or two semesters of probability theory.
0 of 0 people found the following review helpful.
Five Stars
By Mr. Smith
helpful
See all 4 customer reviews...
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