Applied Linear Regression

Simple linear regression; Multiple regression; Drawing conclusions; Weighted least squares, testing for lack of fit, general F-tests, and confidence ellipsoids; Diagnostics I, residuals and influence; Diagnostics II, symptoms and remedies; Model building I, defining new predictors; Model building I, collinearity and variable selection; Prediction; Incomplete data; Contents; Nonleast squares estimation; Generalizations of linear regression.