Introduction to Linear Regression Analysis

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John Wiley & Sons, Apr 9, 2012 - Mathematics - 645 pages
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Praise for the Fourth Edition

"As with previous editions, the authors have produced a leading textbook on regression."
Journal of the American Statistical Association

A comprehensive and up-to-date introduction to the fundamentals of regression analysis

Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today's cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.

Following 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 then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including:

  • A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models

  • Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model

  • Tests on individual regression coefficients and subsets of coefficients

  • Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data

In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material, and a related FTP site features the presented data sets, extensive problem solutions, software hints, and PowerPoint slides to facilitate instructional use of the book.

Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

 

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Contents

SIMPLE LINEAR REGRESSION
12
3
67
Problems
121
MODEL ADEQUACY CHECKING
129
TRANSFORMATIONS AND WEIGHTING
171
DIAGNOSTICS FOR LEVERAGE AND INFLUENCE
211
7
223
8
260
REGRESSION ANALYSIS OF TIME SERIES DATA
474
OTHER TOPICS IN THE USE OF REGRESSION ANALYSIS
500
APPENDIX A STATISTICAL TABLES
541
5
549
APPENDIX B DATA SETS FOR EXERCISES
553
SUPPLEMENTAL TECHNICAL MATERIAL
574
INTRODUCTION TO SAS
613
APPENDIX E INTRODUCTION TO R TO PERFORM LINEAR REGRESSION ANALYSIS
623

9
285
VARIABLE SELECTION AND MODEL BUILDING
327
Problems
367
Problems
386
Problems
416
GENERALIZED LINEAR MODELS
421
260
457
3
465
Basic Data Entry
624
Brief Comments on Other Functionality in R
626
R Commander
627
REFERENCES
628
INDEX
642
285
644
Copyright

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About the author (2012)

DOUGLAS C. MONTGOMERY, PhD, is Regents Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery is a Fellow of the American Statistical Association, the American Society for Quality, the Royal Statistical Society, and the Institute of Industrial Engineers and has more than thirty years of academic and consulting experience. He has devoted his research to engineering statistics, specifically the design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. Dr. Montgomery is the coauthor of Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition and Introduction to Time Series Analysis and Forecasting, both published by Wiley.

ELIZABETH A. PECK, PhD, is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia.

G. GEOFFREY VINING, PhD, is Professor in the Department of Statistics at Virginia Polytechnic and State University. He has published extensively in his areas of research interest, which include experimental design and analysis for quality improvement, response surface methodology, and statistical process control. A Fellow of the American Statistical Association and the American Society for Quality, Dr. Vining is the coauthor of Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition (Wiley).