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Step 1: The estimated regression is $\hat{Y}_i = 9.2903 + 0.6378X_i$ Step 2: The residuals obtained from this regression are: $\hat{u}_i$ $\hat{u}_i^2$ $p_i$ -5.31307 28.22873 0.358665 -8.06876 65.10494 0.827201 6.49801 42.22407 0.536485 0.55339 0.30624 0.003891 -6.82445 46.57318 0.591743 1.36447 1.86177 0.023655 5.79770 33.61333 0.427079 -3.58015 12.81744 0.162854 0.98662 0.97342 0.012368 8.30908 69.04085 0.877209 -2.25769 5.09715 0.064763 -1.33584 1.78446 0.022673 8.04201 64.67391 0.821724 10.47524 109.73066 1.3942 6.23093 38.82451 0.493291 -9.09153 82.65588 1.050197 -12.79183 163.63099 2.079039 -16.84722 283.82879 3.606231 -17.35860 301.32104 3.828481 2.71955 7.39595 0.09397 2.39709 5.74604 0.073007 0.77494 0.60052 0.00763 9.45248 89.34930 1.135241 4.88571 23.87014 0.303286 4.53063 20.52658 0.260804 -0.03614 0.00131 1.66E-05 -0.30322 0.09194 0.001168 9.50786 90.39944 1.148584 -18.98076 360.26909 4.577455 20.26355 410.61159 5.217089 The estimated $\tilde{\sigma}^2$ is $\frac{\sum u_i^2}{n} = \frac{2361.15325}{30} = 78.7051$. 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