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Treats linear regression diagnostics as a tool for application of linear regression models to real-life data. Presentation makes extensive use of examples to illustrate theory. Assesses the effect of measurement errors on the estimated coefficients, which is not accounted for in a standard least squares estimate but is important where regression coefficients are used to apportion effects due to different variables. Also assesses qualitatively and numerically the robustness of the regression fit.
The Prediction Matrix.
Role of Variables in a Regression Equation.
Effects of an Observation on a Regression Equation.
Assessing the Influence of Multiple Observations.
Joint Impact of a Variable and an Observation.
Assessing the Effect of Errors of Measurements.
A Study of Model Sensitivity by the Generalized Linear Model Approach.
Appendix: Summary of Vector and Matrix Norms, Proofs of Three Theorems.