Regression analysis provides 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, Third Edition explains the principles underlying exploratory data analysis, emphasizing data analysis rather than statistical theory. This is not just another edition of the book; it is a major rewriting and reorganization of the previous edition. The new edition is expanded and updated to reflect recent advances in the field, offering in-depth treatment of diagnostic plots, time series regression, multicollinearity, and logistic regression.
Suitable for anyone with an understanding of elementary statistics, Regression Analysis by Example, Third Edition illustrates methods of regression analysis, with examples containing the types of irregularities commonly encountered in the real world. 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. Each of the methods described can be carried out with most currently available statistical software packages.
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"A textbook for students who have some background in statistics but are not specialists in it, and who use regression analysis quite extensively in their work. Students who are specializing in statistics might find it a useful balance..." ( SciTech Book News, Vol. 24, No. 4, December 2000)
Overall, the material that is covered is an excellent introduction to a substantial collection of diagnostic tools that aid in uncovering hidden structures in one′s data. I would recommend the book as an addition to any applied statistician′s library. (Technometrics, Vol. 42, No. 4, May 2001)
This book is a thorough overview and introduction to regression analysis that is sensibly arranged and easy to read. Care has been taken to provide verbal explanations for quanitative and algebraic expressions, and to provide interpretations for statistical results and insights. (The Statistician, 50 (1), 2001)
"This book is a major rewriting and reorganization of previous editions. New material is added to reflect advances in the field." (Zentralblatt MATH, Vol. 946, No. 21)
"...a thorough overview of diagnostic tools for regression models that are sensibly arranged and easily understood..." (Statistical Methods in Medical Research, Vol.12, No.1, 2003)
...excellent introduction to a substantial collection of diagnostic tools that aid in uncovering hidden structures in one′s data. (Technometrics, Vol. 42, No. 4, May 2001)
[...] a thorough overview and introduction to regression analysis that is sensibly arranged and easy to read. (The Statistician, 50 (1), 2001)
"This book is a major rewriting and reorganization of previous editions. New material is added to reflect advances in the field." (Zentralblatt MATH, Vol. 946, No. 21)
"...a thorough overview of diagnostic tools for regression models that are sensibly arranged and easily understood..." (Statistical Methods in Medical Research, Vol.12, No.1, 2003)
SAMPRIT CHATTERJEE, PhD, is Professor of Statistics at New York University. A well-known research scientist and Fulbright scholar, Dr. Chatterjee has coauthored Sensitivity Analysis in Linear Regression (with Dr. Hadi) and A Casebook for a First Course in Statistics and Data Analysis, both available from Wiley. ALI S. HADI, PhD, is the Stephen H. Weiss Presidential Fellow and Professor of Statistical and Computing Sciences at Cornell University. The author/coauthor of three other books, Dr. Hadi is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute. BERTRAM PRICE, PhD, is President of Price Associates, a consulting firm in Washington D.C. Dr. Price's experience spans both academia and business, including work as a business scientist at IBM, and he has served as an associate editor of The American Statistician.
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