As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of 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. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.
Table of Content
Preface xiii
1. Introduction 1
2. Simple Linear Regression 13
3. Multiple Linear Regression 67
4. Model Adequacy Checking 131
5. Transformations and Weighting to Correct Model Inadequacies 173
6. Diagnostics for Leverage and Influence 207
7. Polynomial Regression Models 221
8. Indicator Variables 265
9. Variable Selection and Model Building 291
10. Multicollinearity 325
11. Robust Regression 382
12. Introduction to Nonlinear Regression 414
13. Generalized Linear Models 443
14. Other Topics in the Use of Regression Analysis 488
15. Validation of Regression Models 529
Appendix A. Statistical Tables 549
Appendix B. Data Sets For Exercises 567
Appendix C. Supplemental Technical Material 582
References 621
Index 637
About the author
DOUGLAS C. MONTGOMERY, Ph D, 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, Ph D, is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia.
G. GEOFFREY VINING, Ph D, 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).
All three coauthors have published extensively in both journals and books. Solutions prepared by ANN G. RYAN.
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