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.
Cuprins
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.
Despre autor
SAMPRIT CHATTERJEE, PHD, is Professor of Health Policy at
Mount Sinai School of Medicine. He is also Professor Emeritus of
Statistics at New York University. A well-known research scientist
and Fulbright scholar, Dr. Chatterjee has co-authored Sensitivity
Analysis in Linear Regression (with Dr. Hadi) and A Casebook for a
First Course in Statistics and Data Analysis, both published by
Wiley.
ALI S. HADI, PHD, is Vice Provost and Professor of
Mathematical, Statistical, and Computing Sciences at The American
University in Cairo. He is also a Stephen H. Weiss Presidential
Fellow and Professor Emeritus at Cornell University. Dr. Hadi is
the author/co-author of four other books, a Fellow of the American
Statistical Association, and an elected member of the International
Statistical Institute.
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