The data set consists of information on 3600 full-time full-year workers. The highest educational achievement for each worker was either a high school diploma or a bachelor's degree. The worker's ages ranged from 25 to 45 years. The data set also containe information on the region of the country where the person lived, marital status, and number of children. For the purposes of these exercises, let HE= average hourly eamings (in 2005 dollars) College binary variable (1 if college, 0 high school) Female binary variable (1 if female, 0 if male) Age-age (in years) theast-binary variable (1 if Region Northeast 0 otherwise) idwest-binary variable (1 i Region Midwest 0 otherwise) South-binary variable (1 i Region=South, 0 otherwise) West-binary variable (1 if Region=West 0 otherwise) Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using Data from the Current Population Survey Dependent Variable: average hourly earnings (AHE). Regressor College (X₂) Female (X₂) Age (X₂) Northeast (X) Midwest (X) South (X) Intercept Summary Statistics (1) R n 5.13 (0.20) -2.48 (0.19) 11.93 (0.13) F-statistic for regional effects=0 SER 5.89 0.105 3000 (2) 5.15 (0.20) -2.48 (0.10) 0.27 (0.04) 4.14 (0.90) 5.85 0.179 3000 Using the regression results in column (1) The f-statistic for the college-high school earnings difference estimated from this regression is (Round your response to two decimal places) es the college-high school earnings difference estimated from this regression statistically significant at the 1% level? Since the absolute value of the I-statistic is (3) 5.11 (0.20) <-2.46 (0.19) 0.27 (0.04) 0.65 (0.28) 0.50 (0.26) -0.25 (0.24) 3.53 (1.00) 6.43 5.84 0.182 3600 than the critical value for 99% confidence, the college-high school earnings difference estimated from this regression statistically significant at the 1% level