Regression Analysis By Example - 5th Edition

Praise for the Fourth Edition:"This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."-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 regression Fitting nonlinear models Errors in variables ANOVA for designed experiments 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, 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.Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.

List of contents

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 Introduction 1295.2 Salary Survey Data 1305.3 Interaction Variables 1335.4 Systems of Regression Equations 1365.5 Other Applications of Indicator Variables 1475.6 Seasonality 1485.7 Stability of Regression Parameters Over Time 149Exercises 1516 Transformation of Variables 1636.1 Introduction 1636.2 Transformations to Achieve Linearity 1656.3 Bacteria Deaths Due to XRay Radiation 1676.4 Transformations to Stabilize Variance 1716.5 Detection of Heteroscedastic Errors 1766.6 Removal of Heteroscedasticity 1786.7 Weighted Least Squares 1796.8 Logarithmic Transformation of Data 1806.9 Power Transformation 1816.10 Summary 185Exercises 1867 Weighted Least Squares 1917.1 Introduction 1917.2 Heteroscedastic Models 1927.3 Two-Stage Estimation 1957.4 Education Expenditure Data 1977.5 Fitting a Dose-Response Relationship Curve 206Exercises 2088 The Problem of Correlated Errors 2098.1 Introduction: Autocorrelation 2098.2 Consumer Expenditure and Money Stock 2108.3 Durbin-Watson Statistic 2128.4 Removal of Autocorrelation by Transformation 2148.5 Iterative Estimation With Autocorrelated Errors 2168.6 Autocorrelation and Missing Variables 2178.7 Analysis of Housing Starts 2188.8 Limitations of Durbin-Watson Statistic 2228.9 Indicator Variables to Remove Seasonality 2238.10 Regressing Two Time Series 226Exercises 2289 Analysis of Collinear Data 2339.1 Introduction 2339.2 Effects of Collinearity on Inference 2349.3 Effects of Collinearity on Forecasting 2409.4 Detection of Collinearity 245Exercises 25410 Working With Collinear Data 25910.1 Introduction 25910.2 Principal Components 25910.3 Computations Using Principal Components 26310.4 Imposing Constraints 26310.5 Searching for Linear Functions of the ²'s 26710.6 Biased Estimation of Regression Coefficients 27210.7 Principal Components Regression 27210.8 Reduction of Collinearity in the Estimation Data 27410.9 Constraints on the Regression Coefficients 27610.10 Principal Components Regression: A Caution 27710.11 Ridge Regression 28010.12 Estimation by the Ridge Method 28110.13 Ridge Regression: Some Remarks 28510.14 Summary 28710.15 Bibliographic Notes 288Exercises 288Appendix 10.A: Principal Components 291Appendix 10.B: Ridge Regression 294Appendix 10.C: Surrogate Ridge Regression 29711 Variable Selection Procedures 29911.1 Introduction 29911.2 Formulation of the Problem 30011.3 Consequences of Variables Deletion 30011.4 Uses of Regression Equations 30211.5 Criteria for Evaluating Equations 30311.6 Collinearity and Variable Selection 30611.7 Evaluating All Possible Equations 30611.8 Variable Selection Procedures 30711.9 General Remarks on Variable Selection Methods 30911.10 A Study of Supervisor Performance 31011.11 Variable Selection With Collinear Data 31411.12 The Homicide Data 31411.13 Variable Selection Using Ridge Regression 31711.14 Selection of Variables in an Air Pollution Study 31811.15 A Possible Strategy for Fitting Regression Models 32611.16 Bibliographic Notes 327Exercises 328Appendix: Effects of Incorrect Model Specifications 33212 Logistic Regression 33512.1 Introduction 33512.2 Modeling Qualitative Data 33612.3 The Logit Model 33612.4 Example: Estimating Probability of Bankruptcies 33812.5 Logistic Regression Diagnostics 34112.6 Determination of Variables to Retain 34212.7 Judging the Fit of a Logistic Regression 34512.8 The Multinomial Logit Model 34712.8.1 Multinomial Logistic Regression 34712.9 Classification Problem: Another Approach 354Exercises 35513 Further Topics 35913.1 Introduction 35913.2 Generalized Linear Model 35913.3 Poisson Regression Model 36013.4 Introduction of New Drugs 36113.5 Robust Regression 36313.6 Fitting a Quadratic Model 36413.7 Distribution of PCB in U.S. Bays 366Exercises 370Appendix A: Statistical Tables 371References 381Index 389

Report

"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)

Product details

• Authors: Chatterjee, S Chatterjee, Sampri Chatterjee, Samprit Chatterjee, Samprit Hadi Chatterjee, Samprit/ Hadi Chatterjee, CHATTERJEE SAMPRIT HADI ALI S, Hadi, Ali S Hadi, Ali S. Hadi, Hadi Ali S.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 05.10.2012
• EAN: 9780470905845
• ISBN: 978-0-470-90584-5
• No. of pages: 424
• Dimensions: 185 mm x 262 mm x 30 mm
• Series: Wiley Series in Probability and Statistics; Wiley Series in Probability an; Wiley Series in Probability and Statistics
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Regressionsanalyse, Statistics, Angew. Wahrscheinlichkeitsrechn. u. Statistik / Modelle, Applied Probability & Statistics - Models, Regression Analysis, Statistics - Text & Reference, Statistik / Lehr- u. Nachschlagewerke