The data set consists of information on 3900 full-time fullyear 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 contained information on the region of the country where the person lived, marital status, and number of children. For the purposes of these exercises, let AHE = average hourly earnings (in 2005 dollars) College = binary variable (1 if college, 0 if high school) Female = binary variable (1 if female, 0 if male) Age = age (in years) Ntheast = binary variable (1 if Region = Northeast, 0 otherwise) Midwest = binary variable (1 if Region = Midwest, 0 otherwise) South = binary variable (1 if Region = South, 0 otherwise) West = binary variable (1 if Region = West, 0 othen/vise) 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 (1) (2) (3) College (X1) 5.35 5.37 5.33 (0.21) (0.21) (0.21) Female (X2) - 2.59 - 2.57 - 2.57 (0.20) (0.20) (0.20) Age (X3) 0.28 0.28 (0.04) (0.04) Northeast (X4) 0.68 (0.29) Midwest (X5) 0.59 (0.27) South (X6) 0.26 (0.25) Intercept 12.44 4.31 3.68 (0.14) (1.03) (1.04) Summary Statistics Fstatistic for regional effects = 0 6.22 SER 6.14 6.10 6.09 R2 0.172 0.186 0.190 n 3900 3900 3900 Using the regression results in column (1): The tstatistic for the collegeehigh school earnings difference estimated from this regression is . (Round your response to two decimal places.) Is the collegeAhigh school earnings difference estimated from this regression statistically significant at the 5% level? Since the absolute value of the tstatistic is V than the critical value for 95% confidence, the collegeAhigh school earnings difference estimated from this regression signicant at the 5% level. statistically