Consider Example 12.3. Figure displays a SAS printout of an analysis of the model containing variables x1, x2, and x3. Focus on the confidence interval of the mean response μ y at the (x1, x2, x3) locations representing the 13 data points. Consider an item in the printout indicated by C.V. This is the coefficient of variation, which is defined by C.V. = a/y ∙ 100, where s = √s2 is the root mean squared error. The coefficient of variation is often used as yet another criterion for comparing competing models. It is a scale-free quantity which expresses the estimate of a, namely s, as a percent of the average response y. In competition for the "best" among a group of competing models, one strives for the model with a "small" value of C.V. Do a regression analysis of the data set shown in Example 12.3 but eliminate x3. Compare the full (x1, x2, x3) model with the restricted model (x1-x2) and focus on two criteria: (i) C.V.; (ii) the widths of the confidence intervals on μ y. For the second criterion you may want to use the average width. Comment