Applied linear regression
Simple linear regression; Multiple regression; Drawing conclusions; Weighted least squares, testing for lack of fit, general F-tests, and confidence ellipsoids; Case analysis I: residuals and influence; Case analysis II: symptoms and remedies; Model building I: defining new variables; Model building II: collinearity and variable selection; Prediction; Incomplete data; Nonleast squares estimation.
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Simple linear regression
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algorithm analysis of variance assumptions Berkeley Guidance Study Chapter coefficient collinearity column computed confidence interval consider corresponding covariance data set defined degrees of freedom deleted depend DLIC dummy variables effect ellipsoid equation example F-test Figure fitted model fitted values Forbes full model function given in Table i i i i included independent variables lack of fit least squares estimates linear model linear regression matrix mean square measured methods multiple regression normally distributed observed data observed values obtained Old Faithful geyser outlier prediction interval predictors principal components procedure quantities random variables rankit relationship Residual plot residual sum ro ro ro sample correlation scale scatter plot silver iodide simple regression model SSreg straight line Studentized residuals subset model sum of squares Suppose tion transformation usual var(e versus weighted least squares zero