Regression Analysis by Example, Fourth Edition

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 analysisA new chapter entitled Further Topics discusses advanced areas of regression analysisReorganized, expanded, and upgraded exercises appear at the end of each chapterA fully integrated Web page provides data setsNumerous 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. Content: Chapter 1 Introduction (pages 1–19): Chapter 2 Simple Linear Regression (pages 21–51): Chapter 3 Multiple Linear Regression (pages 53–84): Chapter 4 Regression Diagnostics: Detection of Model Violations (pages 85–120): Chapter 5 Qualitative Variables as Predictors (pages 121–150): Chapter 6 Transformation of Variables (pages 151–177): Chapter 7 Weighted Least Squares (pages 179–196): Chapter 8 The Problem of Correlated Errors (pages 197–219): Chapter 9 Analysis of Collinear Data (pages 221–258): Chapter 10 Biased Estimation of Regression Coefficients (pages 259–279): Chapter 11 Variable Selection Procedures (pages 281–315): Chapter 12 Logistic Regression (pages 317–340): Chapter 13 Further Topics (pages 341–352):