Regression Analysis by Example

ISBN-10: 0470905840
ISBN-13: 9780470905845
Edition: 5th 2012
List price: $83.50 Buy it from $23.83
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Description: 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  More...

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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: 1.958
Language: English

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.

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.

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 ��s
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

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