Regression Diagnostics

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