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To further develop the students knowledge of Linear Models.
Polynomials and factors in regression. Weighted least squares. Transformation of response and predictors, Box-Cox method, variance stabilizing transformation. Collinearity and variance inflation factors. Model selection: forwards, backwards and stepwise. All possible regressions, Mallow's Cp and PRESS. Cross-validation. Auto-correlation, Durbin-Watson test. AR(1) models. Linear mixed model theory, single random effects, multiple random effects, repeated measures, random coefficient models.
Lectures and tutorials as for ST465.