One general belief held by observers of the business world is that taller men carn more money than shorter men. In a University of Pittsburgh study, 30 MBA graduates, all about 30 years old, were polled and asked to report their annual incomes and their heights. A least squares regression line was fit to the data using Excel, with height (in inches) as the explanatory (independent) variable and income (in dollars) as the response (dependent) variable. A summary of the output is shown below: Dependent variable is Income Regression Statistics Multiple R 0.337193963 R Square 0.113699769 Adjusted R Square 0.082046189 Standard Error 2733.530939 Observations 30 df SS MS F Regression 1 26840137.62 26840137.62 3.592003497 Residual 28 209221359 7472191.395 Total 29 236061496.7 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 33836.05236 11873.58116 1.165280481 0.253736941-10485.90339 38158.0081 1 Height 357.0404355 170.4466664 2.0947340480.045339675-26.10412529 672.1849962 12. The predicted income of a 30 year-old male MBA graduate that is 70 inches tall is approximately: a. $44,547 b. $33,836 c. $24,993 d. $58,829 e. $80,876 Answer 13. The proportion of the variation in income that is explained by the linear relationship between height and income is approximately: a. .0820 b. .0684 c. .3372 d. .1137 e. .5500 Answer 14. For each additional inch in height, the additional income (in dollars) is: a. 33836.05 b. 2733.53 c. 3372 d. 170.45 c. 357.04