Regression Diagnostics (eBook)

Series Editors Introduction
About the Author
Acknowledgements
Chapter 1. Introduction
Chapter 2. The Linear Regression Model: Review
The Normal Linear Regression Models
Least-Squares Estimation
Statistical Inference for Regression Coefficients
The Linear Regression Model in Matrix Forms
Chapter 3. Examining and Transforming Regression Data
Univariate Displays
Transformations for Symmetry
Transformations for Linearity
Transforming Nonconstant Variation
Interpreting Results When Variables are Transformed
Chapter 4. Unusual data
Measuring Leverage: Hatvalues
Detecting Outliers: Studentized Residuals
Measuring Influence: Cook’s Distance and Other Case-Deletion Diagnostics
Numerical Cutoffs for Noteworthy Case Diagnostics
Jointly Influential Cases: Added-Variable Plots
Should Unusual Data Be Discarded?
Unusual Data: Details
Chapter 5. Non-Normality and Nonconstant Error Variance
Detecting and Correcting Non-Normality
Detecting and Dealing With Nonconstant Error Variance
Robust Coefficient Standard Errors
Bootstrapping
Weighted Least Squares
Robust Standard Errors and Weighted Least Squares: Details
Chapter 6. Nonlinearity
Component-Plus-Residual Plots
Marginal Model Plots
Testing for Nonlinearity
Modeling Nonlinear Relationships with Regression Splines
Chapter 7. Collinearity
Collinearity and Variance Inflation
Visualizing Collinearity
Generalized Variance Inflation
Dealing With Collinearity
*Collinearity: Some Details
Chapter 8. Diagnostics for Generalized Linear Models
Generalized Linear Models: Review
Detecting Unusual Data in GLMs
Nonlinearity Diagnostics for GLMs
Diagnosing Collinearity in GLMs
Quasi-Likelihood Estimation of GLMs
*GLMs: Further Background
Chapter 9. Concluding Remarks
Complementary Reading
References
Index