A multiple linear regression model of the following form is fitted to a data set Yi = β0 + β1xi1 + β2xi2 + εi , εi ∼ N(0, σ2 )i. i.d. The model is fitted using SAS and the following output is obtained: Sum of mean Source DF Squares Square F Value Pr> F Model 2 28.119 14.059 ? 0.0039 Error 3 0.715 ? Corrected Total 5 28.833 Parameter Standard Variable Estimate Error t Value P r > |t| Intercept 2.105 0.422 4.99 0.0155 x1 1.242 0.119 10.42 0.0019 x2 -0.195 0.439 ? ? (a) Find the sample size. (b) Find mean squared errors (MSE). (c) Find R2 . (d) Find the value for the F-test statistic. (e) What hypotheses the above F test for? Given α = 0.05, what conclusion will you have? (f) One wants to test H0 : β2 = 0 vs Ha : β2 6= 0. Find the value of test statistic and make your conclusion at α = .05.