Consider the data. X; 3 12 6 20 14 y; 55 35 60 15 25 The estimated regression equation for these data is y = 68.25 - 2.75x. (a) Compute SSE, SST, and SSR using equations SSE = E(y, - > ) , SST = >(y; - y)?, and SSR = >(; - >)2. SSE = SST = SSR = (b) Compute the coefficient of determination . (Round your answer to three decimal places.) 12 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)