4. Suppose we have the following two multiple linear regression models: Yi = Bo+ BIXli+ B2Xzi + E; and Yi = Bot BIXli + B2Xzi + B3Xai + Eis where E; wiid N(0, 02). We first perform the analysis for model (1) in R: > fit12 = 1m(Y ~ X1 + X2) > summary (fit12) Call: Im (formula = Y ~ X1 + X2) Residuals: Min 1Q Median 3Q Max -1. 72610 -0.71385 0. 03204 0 . 62244 3.04 Coefficients: Estimate Std. Error t value (Intercept) -0. 002956 0 . 094429 -0. 031 X1 2. 171693 0. 108222 20. 067 X2 2.949736 0. 098936 29. 814 Residual standard error: 0.9428 on 97 degrees of freedom Multiple R-squared: 0.938, Adjusted R-squared: ? F-statistic: 733.5 on 2 and 97 DF, p-value: