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*** Please see p. 2 for Question 2 *** Question 2 (7 points) The following Excel output shows the outcome of a linear regression of individuals%u2019 wage per hour (in dollars) on the number of years they attended school (in years). SUMMARY OUTPUT Regression Statistics Multiple R 0.381932619 R Square 0.145872525 Adjusted R Square 0.144267022 Standard Error 4.753758428 Observations 534 ANOVA df SS MS F Significance F Regression 1 2053.22554 2053.22554 90.8578469 5.45998E-20 Residual 532 12022.25261 22.59821919 Total 533 14075.47815 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Upper 95.0% Intercept -0.745942699 1.045403804 -0.71354504 0.475821452 -2.799566599 1.307681201 1.307681201 Years of School 0.750448943 0.078729942 9.531938255 0.000545998 0.595789385 0.9051085 0.9051085 Part (a) (1 point) What is the value of the estimated slope %u201Cb%u201D? Part (b) (2 points) Interpret the estimated value of the slope (i.e., explain what the number means in this regression). Part (c) (1 point) Is the estimate of the slope statistically significant? Please answer %u201Cyes%u201D or %u201Cno%u201D and explain how you can tell. Part (d) (2 points) Explain why we want to be able to reject the null hypothesis H0: %u03B2 = 0. Part (e) (1 point) How much of the total variation in wages can be explained by individuals%u2019 education? 