CONTENTS
1.1 What Is Regression Analysis?
1.2 Publicly Available Data Sets
1.3 Selected Applications of Regression Analysis
1.3.2 Industrial and Labor Relations
1.4 Steps in Regression Analysis
1.4.1 Statement of the Problem
1.4.2 Selection of Potentially Relevant Variables
1.4.7 Model Criticism and Selection
1.4.8 Objectives of Regression Analysis
1.5 Scope and Organization of the Book
2.2 Covariance and Correlation Coefficient
2.3 Example: Computer Repair Data
2.4 The Simple Linear Regression Model
2.9 Measuring the Quality of Fit
2.10 Regression Line Through the Origin
2.11 Trivial Regression Models
3.2 Description of the Data and Model
3.3 Example: Supervisor Performance Data
3.5 Interpretations of Regression Coefficients
3.6 Properties of the Least Squares Estimators
3.7 Multiple Correlation Coefficient
3.8 Inference for Individual Regression Coefficients
3.9 Tests of Hypotheses in a Linear Model
3.9.1 Testing All Regression Coefficients Equal to Zero
3.9.2 Testing a Subset of Regression Coefficients Equal to Zero
3.9.3 Testing the Equality of Regression Coefficients
3.9.4 Estimating and Testing of Regression Parameters Under Constraints
Appendix: Multiple Regression in Matrix Notation
4 Regression Diagnostics: Detection of Model Violations
4.2 The Standard Regression Assumptions
4.3 Various Types of Residuals
4.5 Graphs Before Fitting a Model
4.6 Graphs After Fitting a Model
4.7 Checking Linearity and Normality Assumption
4.8 Leverage, Influence, and Outliers
4.8.1 Outliers in the Response Variable
4.8.2 Outliers in the Predictors
4.8.3 Masking and Swamping Problems
4.9.3 Hadi's Influence Measure
4.10 The Potential-Residual Plot
4.11 What to Do with the Outliers?
4.12 Role of Variables in a Regression Equation
4.12.2 Residual Plus Component Plot
4.13 Effects of an Additional Predictor
5 Qualitative Variables as Predictors
5.4 Systems of Regression Equations
5.4.1 Models with Different Slopes and Different Intercepts
5.4.2 Models with Same Slope and Different Intercepts
5.4.3 Models with Same Intercept and Different Slopes
5.5 Other Applications of Indicator Variables
5.7 Stability of Regression Parameters Over Time
6.2 Transformations to Achieve Linearity
6.3 Bacteria Deaths Due to X-Ray Radiation
6.3.1 Inadequacy of a Linear Model
6.3.2 Logarithmic Transformation for Achieving Linearity
6.4 Transformations to Stabilize Variance
6.5 Detection of Heteroscedastic Errors
6.6 Removal of Heteroscedasticity
6.8 Logarithmic Transformation of Data
7.4 Education Expenditure Data
7.5 Fitting a Dose-Response Relationship Curve
8 The Problem of Correlated Errors
8.1 Introduction: Autocorrelation
8.2 Consumer Expenditure and Money Stock
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
9.4 Detection of Multicollinearity
9.5.1 Centering and Scaling in Intercept Models
9.5.2 Scaling in No-Intercept Models
9.6 Principal Components Approach
9.8 Searching for Linear Functions of the β's
9.9 Computations Using Principal Components
Appendix: Principal Components
10 Biased Estimation of Regression Coefficients
10.2 Principal Components Regression
10.3 Removing Dependence Among the Predictors
10.4 Constraints on the Regression Coefficients
10.5 Principal Components Regression: A Caution
10.7 Estimation by the Ridge Method
10.8 Ridge Regression: Some Remarks
11 Variable Selection Procedures
11.2 Formulation of the Problem
11.3 Consequences of Variables Deletion
11.4 Uses of Regression Equations
11.4.1 Description and Model Building
11.4.2 Estimation and Prediction
11.5 Criteria for Evaluating Equations
11.5.3 Information Criteria: Akaike and Other Modified Forms
11.6 Multicollinearity and Variable Selection
11.7 Evaluating All Possible Equations
11.8 Variable Selection Procedures
11.8.1 Forward Selection Procedure
11.8.2 Backward Elimination Procedure
11.9 General Remarks on Variable Selection Methods
11.10 A Study of Supervisor Performance
11.11 Variable Selection With Collinear 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
Appendix: Effects of Incorrect Model Specifications
12.2 Modeling Qualitative Data
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
12.8.2 Example: Determining Chemical Diabetes
12.8.3 Ordered Response Category: Ordinal Logistic Regression
12.8.4 Example: Determining Chemical Diabetes Revisited
12.9 Classification Problem: Another Approach
13.4 Introduction of New Drugs
13.6 Fitting a Quadratic Model
13.7 Distribution of PCB in U.S. Bays