Regression Analysis by Example

Name: Regression Analysis by Example
Price: 13.02 USD
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ISBN: 9780470905845

ISBN-10: 0470905840

ISBN-13: 9780470905845

Edition: 5th 2012

Authors: Samprit Chatterjee, Ali S. Hadi

List price: $83.50

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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…

Book details

List price: $83.50
Edition: 5th
Copyright year: 2012
Publisher: John Wiley & Sons, Limited
Publication date: 10/5/2012
Binding: Hardcover
Pages: 424
Size: 7.25" wide x 10.50" long x 1.00" tall
Weight: 2.244
Language: English

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…



Preface



Introduction



What Is Regression Analysis?



Publicly Available Data Sets



Selected Applications of Regression Analysis



Steps in Regression Analysis



Scope and Organization of the Book


Exercises



Simple Linear Regression



Introduction



Covariance and Correlation Coefficient



Example: Computer Repair Data



The Simple Linear Regression Model



Parameter Estimation



Tests of Hypotheses



Confidence Intervals



Predictions



Measuring the Quality of Fit



Regression Line Through the Origin



Trivial Regression Models



Bibliographic Notes


Exercises



Multiple Linear Regression



Introduction



Description of the Data and Model



Example: Supervisor Performance Data



Parameter Estimation



Interpretations of Regression Coefficients



Centering and Scaling



Properties of the Least Squares Estimators



Multiple Correlation Coefficient



Inference for Individual Regression Coefficients



Tests of Hypotheses in a Linear Model



Predictions



Summary


Exercises


Appendix: Multiple Regression in Matrix Notation



Regression Diagnostics: Detection of Model Violations



Introduction



The Standard Regression Assumptions



Various Types of Residuals



Graphical Methods



Graphs Before Fitting a Model



Graphs After Fitting a Model



Checking Linearity and Normality Assumptions



Leverage, Influence, and Outliers



Measures of Influence



The Potential-Residual Plot



What to Do with the Outliers?



Role of Variables in a Regression Equation



Effects of an Additional Predictor



Robust Regression


Exercises



Qualitative Variables as Predictors



Introduction



Salary Survey Data



Interaction Variables



Systems of Regression Equations



Other Applications of Indicator Variables



Seasonality



Stability of Regression Parameters Over Time


Exercises



Transformation of Variables



Introduction



Transformations to Achieve Linearity



Bacteria Deaths Due to XRay Radiation



Transformations to Stabilize Variance



Detection of Heteroscedastic Errors



Removal of Heteroscedasticity



Weighted Least Squares



Logarithmic Transformation of Data



Power Transformation



Summary


Exercises



Weighted Least Squares



Introduction



Heteroscedastic Models



Two-Stage Estimation



Education Expenditure Data



Fitting a Dose-Response Relationship Curve


Exercises



The Problem of Correlated Errors



Introduction: Autocorrelation



Consumer Expenditure and Money Stock



Durbin-Watson Statistic



Removal of Autocorrelation by Transformation



Iterative Estimation With Autocorrelated Errors



Autocorrelation and Missing Variables



Analysis of Housing Starts



Limitations of Durbin-Watson Statistic



Indicator Variables to Remove Seasonality



Regressing Two Time Series


Exercises



Analysis of Collinear Data



Introduction



Effects of Collinearity on Inference



Effects of Collinearity on Forecasting



Detection of Collinearity


Exercises



Working With Collinear Data



Introduction



Principal Components



Computations Using Principal Components



Imposing Constraints



Searching for Linear Functions of the Î²ï¿½ï¿½ï¿½s



Biased Estimation of Regression Coefficients



Principal Components Regression



Reduction of Collinearity in the Estimation Data



Constraints on the Regression Coefficients



Principal Components Regression: A Caution



Ridge Regression



Estimation by the Ridge Method



Ridge Regression: Some Remarks



Summary



Bibliographic Notes


Exercises



Principal Components



Ridge Regression



Surrogate Ridge Regression



Variable Selection Procedures



Introduction



Formulation of the Problem



Consequences of Variables Deletion



Uses of Regression Equations



Criteria for Evaluating Equations



Collinearity and Variable Selection



Evaluating All Possible Equations



Variable Selection Procedures



General Remarks on Variable Selection Methods



A Study of Supervisor Performance



Variable Selection With Collinear Data



The Homicide Data



Variable Selection Using Ridge Regression



Selection of Variables in an Air Pollution Study



A Possible Strategy for Fitting Regression Models



Bibliographic Notes


Exercises


Appendix: Effects of Incorrect Model Specifications



Logistic Regression



Introduction



Modeling Qualitative Data



The Logit Model



Example: Estimating Probability of Bankruptcies



Logistic Regression Diagnostics



Determination of Variables to Retain



Judging the Fit of a Logistic Regression



The Multinomial Logit Model



Multinomial Logistic Regression



Classification Problem: Another Approach


Exercises



Further Topics



Introduction



Generalized Linear Model



Poisson Regression Model



Introduction of New Drugs



Robust Regression



Fitting a Quadratic Model



Distribution of PCB in U.S. Bays


Exercises



Statistical Tables


References


Index