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A comprehensive and up-to-date introduction to the fundamentals of regression analysis
The Fourth Edition of Introduction to Linear Regression Analysis describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. This popular book blends both theory and application to equip the reader with an understanding of the basic principles necessary to apply regression model-building techniques in a wide variety of application environments. It assumes a working knowledge of basic statistics and a familiarity with hypothesis testing and confidence intervals, as well as the normal, t, x2, and F distributions.
Illustrating all of the major procedures employed by the contemporary software packages MINITAB(r), SAS(r), and S-PLUS(r), the Fourth Edition begins with a general introduction to regression modeling, including typical applications. A host of technical tools are outlined, such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. Subsequent chapters discuss:
* Indicator variables and the connection between regression and analysis-of-variance models
* Variable selection and model-building techniques and strategies
* The multicollinearity problem--its sources, effects, diagnostics, and remedial measures
* Robust regression techniques such as M-estimators, and properties of robust estimators
* The basics of nonlinear regression
* Generalized linear models
* Using SAS(r) for regression problems
This book is a robust resource that offers solid methodology for statistical practitioners and professionals in the fields of engineering, physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Both the accompanying FTP site, which contains data sets, extensive problem solutions, software hints, and PowerPoint(r) slides, as well as the book's revised presentation of topics in increasing order of complexity, facilitate its use in a classroom setting.
With its new exercises and structure, this book is highly recommended for upper-undergraduate and beginning graduate students in mathematics, engineering, and natural sciences. Scientists and engineers will find the book to be an excellent choice for reference and self-study.
- Sales Rank: #903901 in Books
- Published on: 2006-07-21
- Original language: English
- Number of items: 1
- Dimensions: 10.10" h x 1.61" w x 7.26" l, 2.71 pounds
- Binding: Hardcover
- 640 pages
Review
"This book represents a very competent and very comprehensive monograph on regression analysis. It can highly be recommended to anyone who wants to perform a regression analysis for a given set of data." (Stat Papers, 2010)
"As with previous editions, the authors have produced a leading textbook on regression." (Journal of the American Statistical Association, December 2007)
"…written by the best in the field and I strongly recommend it both as a textbook and as a handy reference…" (Technometrics, May 2007)
"…an excellent reference and…self-teaching text for anyone with a basic level of statistical knowledge." (MAA Reviews, August 21, 2006)
From the Back Cover
A comprehensive and up-to-date introduction to the fundamentals of regression analysis
The Fourth Edition of Introduction to Linear Regression Analysis describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. This popular book blends both theory and application to equip the reader with an understanding of the basic principles necessary to apply regression model-building techniques in a wide variety of application environments. It assumes a working knowledge of basic statistics and a familiarity with hypothesis testing and confidence intervals, as well as the normal, t, x2, and F distributions.
Illustrating all of the major procedures employed by the contemporary software packages MINITAB®, SAS®, and S-PLUS®, the Fourth Edition begins with a general introduction to regression modeling, including typical applications. A host of technical tools are outlined, such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. Subsequent chapters discuss:
- Indicator variables and the connection between regression and analysis-of-variance models
- Variable selection and model-building techniques and strategies
- The multicollinearity problem—its sources, effects, diagnostics, and remedial measures
- Robust regression techniques such as M-estimators, and properties of robust estimators
- The basics of nonlinear regression
- Generalized linear models
- Using SAS® for regression problems
This book is a robust resource that offers solid methodology for statistical practitioners and professionals in the fields of engineering, physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Both the accompanying FTP site, which contains data sets, extensive problem solutions, software hints, and PowerPoint® slides, as well as the book's revised presentation of topics in increasing order of complexity, facilitate its use in a classroom setting.
With its new exercises and structure, this book is highly recommended for upper-undergraduate and beginning graduate students in mathematics, engineering, and natural sciences. Scientists and engineers will find the book to be an excellent choice for reference and self-study.
About the Author
DOUGLAS C. MONTGOMERY is ASU Foundation Professor of Engineering and Professor of Statistics at 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 at Virginia Polytechnic Institute and State University. All three authors have published extensively in both journals and books.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Very good.
By Isaac M.M.
This book has not only information about the mathematics in the linear regression analysis (LR), but also a lot of information on best practices, much of what was an eyeopener to me. In this regard, IMO, the best parts of the book are the sections dealing with:
* Qualitative variates in a regression.- The best is the use of "indicators" and not "allocated codes". I'd have used allocated codes as far as my intuition goes. This book explains well why indicators are the best choice.
* Multicollinearity.- How to detect it and deal with it. What are the implications of having strong correlation between predictors.
* LR in time series data.- Correlation in the errors often appears when applying LR to time series data. The author explains this is mainly due to a predictor missing in the model -adding that predictor would solve it. When not possible -and believe me, many times it's not possible, you have to incorporate the correlation structure in the model. In my experience, correlations in time series data are just ubiquitous, they even appear depending on such a things as the sampling rate of your instruments! So, this chapter was the most useful for me.
There are also other topics covered that I've already seen in other books, including variable selection, variance stabilization, evaluation of the model, etc.
The flaw: In the chapter of Polynomial regression, when the author talk about the possibility of using orthogonal polynomials, there is no sound theory on the construction of orthogonal polynomials, and the reader is referred to Seber's Linear Regression Analysis. Seber's book has the info (Chebyshev polynomials, and so on) yet it feels quite a dry read to me. So, the fact you have to go to other book to apply some technique described in Montgomery's book is not good, this is the reason for 4/5 stars. I still prefer Montgomery's book.
6 of 7 people found the following review helpful.
Wiley needs a proofreader fantastically
By Gregory Quenell
This was used as the textbook in a course in Linear Regression
Analysis that I recently attended as an auditor. I'm a mathematician,
not a statistician, so much of the material, and the authors' ways of
looking at it, were not familiar to me. My statistician colleagues
assure me that the techniques in the book are correct, useful, and
mostly up to date. And I believe them.
Alas, the book is poorly edited, and in just the few chapters we
covered, I found a score of errors, including misstated formulas,
misplaced graphics, multiplication where there should be division, and
even some numerical errors. If you, as an instructor, decide to adopt
this book for your course, be prepared to do a lot of proofreading
(the publisher apparently didn't bother) and to distribute textbook
corrections to your students. Also note that this book, like so many
Wiley textbooks, is overpriced.
4 of 5 people found the following review helpful.
A good book with industrial applications
By Xiaobo Wang
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.
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