Regression Analysis by Example

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John Wiley & Sons, Oct 20, 2006 - Mathematics - 416 pages
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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.

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Looks good. Realistically grapples with outliers, violations of assumptions, and other thorny issues.

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About the author (2006)

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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