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ONE Review of Simple Regression
TWO Introduction to Matrices
three Multiple Regression in Matrix Notation
17 other sections not shown
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adjusted analysis of variance balanced data BIOMASS biplot block cell means Chapter class variable collinearity collinearity problem column computed confidence interval confidence interval estimates correlation covariate defined degrees of freedom dependent eigenvalues estimable functions estimate of a2 example expectation F-test full model gives HERB independent variables interaction effects intercept joint confidence region least squares estimates least squares means least squares regression linear function linear model Linthurst marginal means method MS(Res NT NOH null hypothesis observations obtained ordinary least squares orthogonal ozone parameters partial regression coefficients partial sum plot polynomial model prediction PROC GLM quadratic form random variables reduced model regression equation regression results relationship reparameterization residual mean square residual sum ri ri ri ridge regression sample solution SS(Model SS(Regr SS(Res standard errors statistic subset sum of squares Table tests of significance transformation treatment means Type variance-covariance matrix vector weight X-space zero