here is the R output for multiple linear regression

Call: Im(formula = y ~ x1 + x2 + x3) Residuals : Min 10 Median 30 Max -3.4890 -2.4106 -0.7232 1.7503 6.3293 Coefficients: Estimate Std. Error t value Pr(>Itl) (Intercept) A 3.240609 10.700 2. 12e-11 x1 -0.017805 0.009375 -1.899 0. 0679 x2 -3.279044 1.327509 B 3. 0199 x3 0. 177724 1.484316 0.120 0.9055 Residual standard error: 2.967 on 28 degrees of freedom Multiple R-squared: C, Adjusted R-squared: D F-statistic: 33.29 on 3 and E DF, p-value: 2.25e-09 > anova(model) Analysis of Variance Table Response: y Of Sum Sq Mean Sq F value Pr(>F) x1 1 808.89 808.89 91. 8609 2.436e-10 x2 1 70.48 70.48 8. 0036 0. 008535 x3 1 0. 13 0. 13 0. 0143 0.905548 Residuals 28 246.56 8. 81 (a) (5 points) Calculate the missing values. (b) (5 points) Carry out a test for overall significance of regression at a = 0.05. State your null and alternative hypotheses explicitly. (c) (5 points) Investigate the contribution of introducing the variable ry to the model when I1 and 12 are already included by conducting a partial F-test. State your null and alternative hypotheses explicitly. What is your conclusion at a = 0.05? (d) (5 points) What is the point estimate for y when I1 = 180, 12 = 2.20 and 13 = 1