Applied Regression Modeling, 2nd Edition

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Praise for the First Edition

"The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful." —The American Statistician

Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications.

The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling.

In addition, the Second Edition features clarification and expansion of challenging topics, such as:

  • Transformations, indicator variables, and interaction
  • Testing model assumptions
  • Nonconstant variance
  • Autocorrelation
  • Variable selection methods
  • Model building and graphical interpretation

Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos.

With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.

Preface xi

Acknowledgments xvii

Introduction xvii

1.1   Statistics in practice xvii

1.2   Learning statistics xix

1. Foundations 1

1.1 Identifying and summarizing data 1

1.2 Population distributions 5

1.3 Selecting individuals at random—probability 9

1.4 Random sampling 11

1.5 Interval estimation 15

1.6 Hypothesis testing 19

1.7 Random errors and prediction 25

1.8 Chapter summary 28

Problems 29

2. Simple linear regression 35

2.1 Probability model for X and Y 35

2.2 Least squares criterion 40

2.3 Model evaluation 45

2.4 Model assumptions 59

2.5 Model interpretation 66

2.6 Estimation and prediction 68

2.7 Chapter summary 72

Problems 78

3. Multiple linear regression 83

3.1 Probability model for (X1; X2; : : : ) and Y 83

3.2 Least squares criterion 87

3.3 Model evaluation 92

3.4 Model assumptions 118

3.5 Model interpretation 124

3.6 Estimation and prediction 126

3.7 Chapter summary 130

Problems 132

4. Regression model building I 137

4.1 Transformations 138

4.2 Interactions 159

4.3 Qualitative predictors 166

4.4 Chapter summary 182

Problems 184

5. Regression model building II 189

5.1 Influential points 189

5.2 Regression pitfalls 199

5.3 Model building guidelines 218

5.4 Model selection 221

5.5 Model interpretation using graphics 224

5.6 Chapter summary 231

Problems 234

6. Case studies 243

6.1 Home prices 243

6.2 Vehicle fuel efficiency 253

6.3 Pharmaceutical patches 261

7. Extensions 267

7.1 Generalized linear models 268

7.2 Discrete choice models 275

7.3 Multilevel models 278

7.4 Bayesian modeling 280

Appendix A. Computer software help 285

Appendix B. Critical values for t distributions 289

Appendix C. Notation and formulas 293

Appendix D. Mathematics refresher 297

Appendix E. Answers to selected problems 299

References 309

Glossary 315

Index 321

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