Umbri

1A. We can assume that the AVC is relatively accurate. Using the R^2 value of 0.8618, we can assume that about 86 percent of the total variability is explained.

2B. AVC = 21.03512951 + (-0.01745206) + (0.00000798^2)

2C. Overall, I am fairly confident about this regression’s overall ability to predict UI’s AVC. Using the summary output, the R^2 measures at 0.86184679. Therefore, the overall one can assume that about 86 percent of the total variability is explained. However, since R^2 does not have a standard we can also use the significance of F value to see the overall goodness of fit. In this case, the significance of F is 0.00000000. Therefore, with the regression having such a low significance of F value, one can be confident in the regressions overall ability to predict UI’s AVC.

2D. For each individual coefficient, (intercept, Q, Q^2) I am very confident in its ability to estimate for the intercept term. Each individual coefficient is very reliable considering the P-Values. The intercept has a P-Value of 0.00000000, which means its accuracy is 100%. The Q coefficient has a P-Value of 0.00000000, which means its accuracy is also 100%. The Q^2 coefficient has a P-Value of 0.00000001, which means its accuracy is 99.99999999%.

2E. TVC = 21.03512951Q + -0.01745206Q^2 + 0.00000798Q^3

2F. TC = 21.03512951Q - 0.01745206Q^2 + 0.00000798Q^3 + 4,500

2G. Marginal Costs = 21.03512951 - 0.03490412Q + 0.00002394Q^2

3B. Umbri D.D = 2170.45385907 - 93.72690948Pumbri + 0.02864759Income + 26.25345805Ptiki

3C. Yes, the Qd regression summary does suggest that the overall estimate of Qd is accurate. The R^2 value of 0.97580119 suggests that about 98 percent of the total variability is explained by the regression. So yes, overall the regression seems to fit the data.

3D. Yes, I believe that the four individual coefficients (Intercept, Pumbri, Income, Ptiki) are all accurate. Using the P-value we can estimate the accuracy of the coefficients. For the intercept, the...