3. Given a response 3] and two candidate predictors X1 and 1:23 we have the following outputs for the four regression models, namely ywl, ywxl, ymx2J and y~x1+x2. lm(formula = y " 1) Estimate Std. Error t value Pr(>|t|) (Intercept) 0.9676 0.1596 6.063 1.346-06 Residual standard error: 0.8742 lm(formula = y " x1) Estimate Std. Error t value Pr(>|t|) (Intercept) 0.9781 0.1405 6.963 1.43e07 x1 -0.3204 0.1042 -3.076 0.00465 Residual standard error: 0 7691 on 28 degrees of freedom lm(formula = y " x2) Estimate Std. Error t value Pr(>|t|) (Intercept) 0.9742 0.1549 6.288 8.45e-07 x2 0.3841 0.2295 1.673 0.105 Residual standard error: 0 8482 on 28 degrees of freedom lm(formula = y " x1 + x2) Estimate Std. Error t value Pr(>|t|) (Intercept) 0.98498 0.13363 7.371 6.28e-08 x1 0.32333 0.09907 3.264 0.00298 X2 0.39379 0.19797 1.989 0.05690 Residual standard error: 0 7315 on 27 degrees of freedom on 29 degrees of freedom \f