This photo is from an example exam. Can someone explain how the answer is 219.36 (in the box at the bottom)? How was this calculated, what formula(s) were used? Thank you.

Call: Im(formula = y ~ x1 + x2) Residuals: 2 3 4 5 6 -0.014679 0.165842 -0.213645 -0.004698 -0.012184 -0.021923 7 0.101287 Coefficients: Estimate Std. Error t value Pr(>|t[) (Intercept) 1.16359 0.32065 3.629 0.02218* x1 0.09887 0.03637 2.718 0.05309 X2 0.44151 0.09175 4.812 0.00857 * Signif. codes: 0 '*** 0.001 '** 0.01 '* 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1451 on 4 degrees of freedom Multiple R-squared: 0.9876, Adjusted R-squared: 0.9815 F-statistic: 159.8 on 2 and 4 DF, p-value: 0.0001528 Based on this output, which variable(s) do you suggest we keep in the model and which co possibly be discarded? Explain. I would suggest keeping variable x2 based on the p-values of the t-tests for the Bs. Although, as mentioned by Dr. Jones, this is not the best determinate for which variable to keep with the small number of predictor variables, it would appear to be the best wa to go. Using the p-values, it would seem that x1 is not significant (0.05309> a) and coul be discarded. Also, going by the p-value, it would appear that x2 has some possible significance (0.00857