Chatterjee S., Hadi A.S. Regression Analysis by Example

Wiley, 2006. — 366 p. — ISBN 978047174696-6.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:
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.The Related web page provides data sets and solutions to selected exercises.
Текст ссылкиSimple Linear regression.
Multiple Linear Regression.
Regression Diagnostics: Detection of Model Violations.
Qualitative Variables as Predictors.
Transformation of Variables.
Weighted Least Squar45es.
The Problem of Correlated Errors.
Analysis of Collinear Data.
Biased Estimation of Regression Coefficients.
Variable Selection Procedures.
Logistic Regression.
Further Topics.
Appendix A: Statistical Tables.