About the Book

Regression Analysis by Example, Fifth 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. 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 packages R, SPSS, SAS, and Minitab. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.

About the Author

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

Table of Contents:
Preface 1 Introduction 1.1 What Is Regression Analysis? 1.2 Publicly Available Data Sets 1.3 Selected Applications of Regression Analysis 1.4 Steps in Regression Analysis 1.5 Scope and Organization of the Book Exercises 2 Simple Linear Regression 2.1 Introduction 2.2 Covariance and Correlation Coefficient 2.3 Example: Computer Repair Data 2.4 The Simple Linear Regression Model 2.5 Parameter Estimation 2.6 Tests of Hypotheses 2.7 Confidence Intervals 2.8 Predictions 2.9 Measuring the Quality of Fit 2.10 Regression Line Through the Origin 2.11 Trivial Regression Models 2.12 Bibliographic Notes Exercises 3 Multiple Linear Regression 3.1 Introduction 3.2 Description of the Data and Model 3.3 Example: Supervisor Performance Data 3.4 Parameter Estimation 3.5 Interpretations of Regression Coefficients 3.6 Centering and Scaling 3.7 Properties of the Least Squares Estimators 3.8 Multiple Correlation Coefficient 3.9 Inference for Individual Regression Coefficients 3.10 Tests of Hypotheses in a Linear Model 3.11 Predictions 3.12 Summary Exercises Appendix: Multiple Regression in Matrix Notation 4 Regression Diagnostics: Detection of Model Violations 4.1 Introduction 4.2 The Standard Regression Assumptions 4.3 Various Types of Residuals 4.4 Graphical Methods 4.5 Graphs Before Fitting a Model 4.6 Graphs After Fitting a Model 4.7 Checking Linearity and Normality Assumptions 4.8 Leverage, Influence, and Outliers 4.9 Measures of Influence 4.10 The Potential-Residual Plot 4.11 What to Do with the Outliers? 4.12 Role of Variables in a Regression Equation 4.13 Effects of an Additional Predictor 4.14 Robust Regression Exercises 5 Qualitative Variables as Predictors 5.1 Introduction 5.2 Salary Survey Data 5.3 Interaction Variables 5.4 Systems of Regression Equations 5.5 Other Applications of Indicator Variables 5.6 Seasonality 5.7 Stability of Regression Parameters Over Time Exercises 6 Transformation of Variables 6.1 Introduction 6.2 Transformations to Achieve Linear 6.3 Bacteria Deaths Due to XRay Radiation 6.4 Transformations to Stabilize Variance 6.5 Detection of Heteroscedastic Errors 6.6 Removal of Heteroscedasticity 6.7 Weighted Least Squares 6.8 Logarithmic Transformation of Data 6.9 Power Transformation 6.10 Summary Exercises 7 Weighted Least Squares 7.1 Introduction 7.2 Heteroscedastic Models 7.3 Two-Stage Estimation 7.4 Education Expenditure Data 7.5 Fitting a Dose-Response Relationship Curve Exercises 8 The Problem of Correlated Errors 8.1 Introduction: Autocorrelation 8.2 Consumer Expenditure and Money Stock 8.3 Durbin-Watson Statistic 8.4 Removal of Autocorrelation by Transformation 8.5 Iterative Estimation With Autocorrelated Errors 8.6 Autocorrelation and Missing Variables 8.7 Analysis of Housing Starts 8.8 Limitations of Durbin-Watson Statistic 8.9 Indicator Variables to Remove Seasonality 8.10 Regressing Two Time Series Exercises 9 Analysis of Collinear Data 9.1 Introduction 9.2 Effects of Collinearity on Inference 9.3 Effects of Collinearity on Forecasting 9.4 Detection of Collinearity Exercises 10 Working With Collinear Data 10.1 Introduction 10.2 Principal Components 10.3 Computations Using Principal Components 10.4 Imposing Constraints 10.5 Searching for Linear Functions of the 03b2's 10.6 Biased Estimation of Regression Coefficients 10.7 Principal Components Regression 10.8 Reduction of Collinearity in the Estimation Data 10.9 Constraints on the Regression Coefficients 10.10 Principal Components Regression: A Caution 10.11 Ridge Regression 10.12 Estimation by the Ridge Method 10.13 Ridge Regression: Some Remarks 10.14 Summary 10.15 Bibliographic Notes Exercises Appendix 10.A: Principal Components Appendix 10.B: Ridge Regression Appendix 10.C: Surrogate Ridge Regression 11 Variable Selection Procedures 11.1 Introduction 11.2 Formulation of the Problem 11.3 Consequences of Variables Deletion 11.4 Uses of Regression Equations 11.5 Criteria for Evaluating Equations 11.6 Collinearity and Variable Selection 11.7 Evaluating All Possible Equations 11.8 Variable Selection Procedures 11.9 General Remarks on Variable Selection Methods 11.10 A Study of Supervisor Performance 11.11 Variable Selection With Collinear Data 11.12 The Homicide Data 11.13 Variable Selection Using Ridge Regression 11.14 Selection of Variables in an Air Pollution Study 11.15 A Possible Strategy for Fitting Regression Models 11.16 Bibliographic Notes Exercises Appendix: Effects of Incorrect Model Specifications 12 Logistic Regression 12.1 Introduction 12.2 Modeling Qualitative Data 12.3 The Logit Model 12.4 Example: Estimating Probability of Bankruptcies 12.5 Logistic Regression Diagnostics 12.6 Determination of Variables to Retain 12.7 Judging the Fit of a Logistic Regression 12.8 The Multinomial Logit Model 12.8.1 Multinomial Logistic Regression 12.9 Classification Problem: Another Approach Exercises 13 Further Topics 13.1 Introduction 13.2 Generalized Linear Model 13.3 Poisson Regression Model 13.4 Introduction of New Drugs 13.5 Robust Regression 13.6 Fitting a Quadratic Model 13.7 Distribution of PCB in U.S. Bays Exercises Appendix A: Statistical Tables References Index