A. consider the full model (regression of y on x1, x2, x3, x4, x5, x6). construct the x matrix. b. compute βˆ, s 2 e , and h of the full model. c. test the overall significance of the model using the f test for the general linear hypothesis: f = (cβˆ − γ) 0 c(x0x) −1c0 −1 (cβˆ − γ) ms2 e , d. use the f test for the general linear hypothesis to test: h0 : (β1, β3) 0 = 0 ha : (β1, β3) 0 6= 0 e. test the hypothesis of question (d) using the extra sum of squares principle. f. consider the full model. test the hypothesis that β4 = 0 using the t statistic. g. consider the following two linear constraints: h0 : β1 + β2 − 3β5 = 2, β3 + β5 + β6 = 3 ha : not true under h0 compute βˆc and the unbiased estimator of σ 2 (make sure you use the constrained residuals). h. refer to question (g). use the canonical form of the model to test the hypothesis with the method of extra sum of squares. i. repeat (h) using the f test for the general linear hypothesis. j. refer to question (g). construct the variance covariance matrix of βˆc.