3. Suppose we have the following two multiple linear regression models: Yi = Bo+ BIXli + B2 Xzi + Ei and Yi = Bo + BIXli + B2Xz + BaXai + Ei, where E; viid N(0, g?). 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, F-statistic: 733.5 on 2 and 97 DF, p-value: