Applied Regression Modeling, Second Edition

**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. Content: Chapter 1 Foundations (pages 1–33): Chapter 2 Simple Linear Regression (pages 35–82): Chapter 3 Multiple Linear Regression (pages 83–135): Chapter 4 Regression Model Building I (pages 137–188): Chapter 5 Regression Model Building II (pages 189–242): Chapter 6 Case Studies (pages 243–266): Chapter 7 Extensions (pages 267–283):

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.

Machine generated contents note: Preface xiAcknowledgments xviiIntroduction xvii1.1 Statistics in practice xvii1.2 Learning statistics xix1. Foundations 11.1 Identifying and summarizing data 11.2 Population distributions 51.3 Selecting individuals at random probability 91.4 Random sampling 111.5 Interval estimation 151.6 Hypothesis testing 191.7 Random errors and prediction 251.8 Chapter summary 28Problems 292. Simple linear regression 352.1 Probability model for X and Y 352.2 Least squares criterion 402.3 Model evaluation 452.4 Model assumptions 592.5 Model interpretation 662.6 Estimation and prediction 682.7 Chapter summary 72Problems 783. Multiple linear regression 833.1 Probability model for (X1; X2; : : : ) and Y 833.2 Least squares criterion 873.3 Model evaluation 923.4 Model assumptions 1183.5 Model interpretation 1243.6 Estimation and prediction 1263.7 Chapter summary 130Problems 1324. Regression model building I 1374.1 Transformations 1384.2 Interactions 1594.3 Qualitative predictors 1664.4 Chapter summary 182Problems 1845. Regression model building II 1895.1 Influential points 1895.2 Regression pitfalls 1995.3 Model building guidelines 2185.4 Model selection 2215.5 Model interpretation using graphics 2245.6 Chapter summary 231Problems 2346. Case studies 2436.1 Home prices 2436.2 Vehicle fuel efficiency 2536.3 Pharmaceutical patches 2617. Extensions 2677.1 Generalized linear models 2687.2 Discrete choice models 2757.3 Multilevel models 2787.4 Bayesian modeling 280Appendix A. Computer software help 285Appendix B. Critical values for t distributions 289Appendix C. Notation and formulas 293Appendix D. Mathematics refresher 297Appendix E. Answers to selected problems 299References 309Glossary 315Index 321. "This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The book's overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasizes major statistical software packages, including SPSS(r), Minitab(r), SAS(r), R, and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided on a specially prepared and maintained author web site. Select software output appears throughout the text. To help readers understand, analyze, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests)"-- Provided by publisher This revised and updated book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies.