图书简介

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

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