Online class on Tuesday March 23, from 14h30 to 17h00

[Slides 183-221] The partial F test: hypotheses and their meaning; the test statistic (in two alternative expressions); the distribution under H0; outline of the proof. Examples: Exercise 9 (brix data). The selection of submodels: full searches using the leaps and bounds algorithm (the leaps package); heuristic algorithms using stepwise selection. The Akaike Information Criterion (AIC): its interpretation and the illustration of a backward elimination algorithm using the AIC; the R function step. Model checking: the distribution of residuals in a Linear Model: its proof and its use in checking the validity of the model assumptions. Three kinds of residuals. Plots of (usual) residuals vs. fitted values; qq.plots of standardized residuals; their interpretation. The R command plot for objects of class lm. Other diagnostics: the notion of outliers. Leverage and leverage points: properties and interpretation.