Regression Analysis by Example, 5th Edition

Praise for the Fourth Edition:

“This book is …an excellent source of examples forregression analysis. It has been and still is readily readable andunderstandable.”

Journal of the American StatisticalAssociation Regression analysis is a conceptually simplemethod for investigating relationships among variables. Carryingout a successful application of regression analysis, however,requires a balance of theoretical results, empirical rules, andsubjective judgment. Regression Analysis by Example, FifthEdition has been expanded and thoroughly updated to reflectrecent advances in the field. The emphasis continues to be onexploratory data analysis rather than statistical theory. The bookoffers in-depth treatment of regression diagnostics,transformation, multicollinearity, logistic regression, and robustregression.

The book now includes a new chapter on the detection andcorrection of multicollinearity, while also showcasing the use ofthe discussed methods on newly added data sets from the fields ofengineering, medicine, and business. The Fifth Edition alsoexplores additional topics, including: * Surrogate ridge regression * Fitting nonlinear models * Errors in variables * ANOVA for designed experiments

Methods of regression analysis are clearly demonstrated, andexamples containing the types of irregularities commonlyencountered in the real world are provided. Each example isolatesone or two techniques and features detailed discussions, therequired assumptions, and the evaluated success of each technique.Additionally, methods described throughout the book can be carriedout with most of the currently available statistical softwarepackages, such as the software package R.

Regression Analysis by Example, Fifth Edition is suitablefor anyone with an understanding of elementary statistics.

Table of Contents

Chapter 1: Introduction
Chapter 2: Simple Linear Regression
Chapter 3: Multiple Linear Regression
Chapter 4: Regression Diagnostics: Detection Of Model Violations
Chapter 5: Qualitative Variables As Predictors
Chapter 6: Transformation Of Variables
Chapter 7: Weighted Least Squares
Chapter 8: The Problem Of Correlated Errors
Chapter 9: Analysis Of Collinear Data
Chapter 10: Working With Collinear Data
Chapter 11: Variable Selection Procedures
Chapter 12: Logistic Regression
Chapter 13: Further Topics
Appendix A: Statistical Tables