Introduction to Linear Regression Analysis / Edition 4

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Overview

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.

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Editorial Reviews

From the Publisher
"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)

SciTech Book
...[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
...[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.
Booknews
New edition of a text on regression analysis, a statistical technique for investigating and modeling the relationship between variables. Montgomery (industrial engineering, Arizona State U.), Elizabeth A. Peck (logistics modeling specialist, Coca-Cola Co.) and G. Geoffrey Vining (statistics, Virginia Tech) 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. Beginning with a general introduction, they outline a host of technical tools including basic inference procedures and introductory aspects of model adequacy checking, simple and multiple linear regression, model adequacy checking, transformations and weighting to correct model inadequacies, diagnostics for leverage and influence, polynomial regression models, indicator variables, variable selection and model building, multicollinearity, robust regression, generalized linear models, nonlinear regression, validation of regression models, and other topics. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780471754954
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 7/21/2006
  • Series: Wiley Series in Probability and Statistics Series , #615
  • Edition description: Revised Edition
  • Edition number: 4
  • Pages: 640
  • Product dimensions: 7.00 (w) x 10.00 (h) x 1.38 (d)

Meet 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.

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Table of Contents

Preface.

1. Introduction.

2. Simple Linear Regression.

3. Multiple Linear Regression.

4. Model Adequacy Checking.

5. Transformations and Weighting to Correct Model Inadequacies.

6. Diagnostics for Leverage and Influence.

7. Polynomial Regression Models.

8. Indicator Variables.

9. Variable Selection and Model Building.

10. Validation of Regression Models.

11. Multicollinearity.

12. Robust Regression.

13. Introduction to Nonlinear Regression.

14. Generalized Linear Models.

15. Other Topics in the Use of Regression Analysis.

Appendix A: Statistical Tables.

Appendix B: Data Sets For Exercises.

Appendix C: Supplemental Technical Material.

Appendix D: Introduction to SAS.

References.

Index.

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