Solutions Manual to accompany Introduction to Linear Regression Analysis (eBook)

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). All three coauthors have published extensively in both journals and books. Solutions prepared by ANN G. RYAN.

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