Regression Analysis by Example, 4th Edition

ISBN: 978-0-470-05546-5

Apr 2006

416 pages

Select type: O-Book

Description

The essentials of regression analysis through practical applications

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.

This new edition features the following enhancements:

• Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis

• A new chapter entitled Further Topics discusses advanced areas of regression analysis

• Reorganized, expanded, and upgraded exercises appear at the end of each chapter

• A fully integrated Web page provides data sets

• Numerous graphical displays highlight the significance of visual appeal

Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Related Resources

Instructor

View Instructor Companion Site

Preface.

1. Introduction.

2. Simple Linear regression.

3. Multiple Linear Regression.

4. Regression Diagnostics: Detection of Model Violations.

5. Qualitative Variables as Predictors.

6. Transformation of Variables.

7. Weighted Least Squar45es.

8. The Problem of Correlated Errors.

9. Analysis of Collinear Data.

10. Biased Estimation of Regression Coefficients.

11. Variable Selection Procedures.

12. Logistic Regression.

13. Further Topics.

Appendix A: Statistical Tables.

References.

Index.

The new edition is expanded and modernized to reflect recent advances in the field, offering in-depth treatment of diagnostic plots, time series regression, multicollinearity, logistic regression, and robust regression and data mining (both at an elementary level).
• Each major topic in regression analysis is treated in a separate chapter.
• Exercises have been reorganized, expanded, and upgraded at the end of each chapter.
• A short introductory chapter allows smooth transition from previously learned, basic statistical concepts.
• A fully integrated web page is provided by the publisher with data sets and solutions to selected exercises.
• Numerous graphical displays are presented in order to highlight the significance of visual appeal.
• This volume is a realistic look at applications of regression to a variety of common problem situations.
• An extensive and greatly revised bibliography is provided at the rear of the book.
"This book is now well established as an excellent source of examples for regression analysis. It has been and still is readily readable and understandable to those with a minimum of data analytic experience.... It is an excellent source of information and example analyses concerning regression modeling for the beginning to moderately trained data analyst." (Journal of the American Statistical Association, March 2009)

"This book is now well established as an excellent source of examples for regression analysis.  It has been and still is readily readable and understandable to those with a minimum of data analytic experience … It is an excellent source of information and example analyses concerning regression modeling for the beginning to moderately trained data analyst." (Journal of the American Statistical Association, March 2009)

"I would like to have the new edition on my desk and suggest you do as well!" (Technometrics, May 2007)

"…I would recommend this book for all students…interested in regression modeling…" (MAA Reviews, December 12, 2006)

• Coverage of a variety of topics has been expanded and updated to reflect recent advances in the field, including in-depth treatment of diagnostic plots (including logistic regression), time series regression, one- and two-way analysis of variance, multicollinearity, model selection and model validation (such as Akaike's criterion), multinomial logistic regression, and elementary robust regression and data mining.
• Each major topic in regression analysis is treated in a separate chapter.
• Exercises have been reorganized, expanded, and upgraded at the end of each chapter.
• A short introductory chapter allows smooth transition from previously learned, basic statistical concepts.
• A fully integrated web page is provided by the publisher with data sets and solutions to selected exercises.
• Numerous graphical displays are presented in order to highlight the significance of visual appeal.
• This volume is a realistic look at applications of regression to a variety of common problem situations.
• An extensive and greatly revised bibliography is provided at the rear of the book.
• The brevity of the book allows for simplicity of comprehension. After three editions, the book is relatively clean of errors and needless ambiguities.