Regression Analysis by Example

Samprit Chatterjee 지음

Wiley 2013년 05월 30일 출간

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Praise for the Fourth Edition:quot;This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable.quot;m-Journal of the American Statistical Association 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 judgment. 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.The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including:Surrogate ridge regressionFitting nonlinear modelsErrors in variablesANOVA for designed experimentsMethods 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, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.

목차

Preface xiv1 Introduction 11.1 What Is Regression Analysis? 11.2 Publicly Available Data Sets 21.3 Selected Applications of Regression Analysis 31.4 Steps in Regression Analysis 131.5 Scope and Organization of the Book 21Exercises 232 Simple Linear Regression 252.1 Introduction 252.2 Covariance and Correlation Coefficient 252.3 Example: Computer Repair Data 302.4 The Simple Linear Regression Model 322.5 Parameter Estimation 332.6 Tests of Hypotheses 362.7 Confidence Intervals 412.8 Predictions 412.9 Measuring the Quality of Fit 432.10 Regression Line Through the Origin 462.11 Trivial Regression Models 482.12 Bibliographic Notes 49Exercises 493 Multiple Linear Regression 573.1 Introduction 573.2 Description of the Data and Model 573.3 Example: Supervisor Performance Data 583.4 Parameter Estimation 613.5 Interpretations of Regression Coefficients 623.6 Centering and Scaling 643.7 Properties of the Least Squares Estimators 673.8 Multiple Correlation Coefficient 683.9 Inference for Individual Regression Coefficients 693.10 Tests of Hypotheses in a Linear Model 713.11 Predictions 813.12 Summary 82Exercises 82Appendix: Multiple Regression in Matrix Notation 894 Regression Diagnostics: Detection of Model Violations 934.1 Introduction 934.2 The Standard Regression Assumptions 944.3 Various Types of Residuals 964.4 Graphical Methods 984.5 Graphs Before Fitting a Model 1014.6 Graphs After Fitting a Model 1054.7 Checking Linearity and Normality Assumptions 1054.8 Leverage, Influence, and Outliers 1064.9 Measures of Influence 1114.10 The Potential-Residual Plot 1154.11 What to Do with the Outliers? 1164.12 Role of Variables in a Regression Equation 1174.13 Effects of an Additional Predictor 1224.14 Robust Regression 123Exercises 1235 Qualitative Variables as Predictors 1295.1 Introduc

출판사 서평

&“The text is suitable for anyone with an understanding of elementary statistics.”& (Zentralblatt MATH, 1 July 2013)“All in all, here we have a nice and valuable up-to-date book showing examples how the famous ever-lasting regression analysis works with the data. No doubt, this book will continue to be frequently used in statistics classrooms.”& (International Statistical Review, 15 February 2013)&

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저자(글) Samprit Chatterjee

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SAMPRIT CHATTERJEE, PhD, is Professor Emeritus of Statistics at New York University. A Fellow of the American Statistical Association, Dr. Chatterjee has been a Fulbright scholar in both Kazhakstan and Mongolia. He is the coauthor of Sensitivity Analysis in Linear Regression and A Casebook for a First Course in Statistics and Data Analysis, both published by Wiley.ALI S. HADI, PhD, is a Distinguished University Professor and former vice provost at the American University in Cairo (AUC). He is the founding Director of the Actuarial Science Program at AUC. He is also a Stephen H. Weiss Presidential Fellow and Professor Emeritus at Cornell University. Dr. Hadi is the author of four other books, a Fellow of the American Statistical Association, and an elected Member of the International Statistical Institute.