The data set consists of information on 3900 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 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 otherwise)

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 othenNise) 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 collegeihigh school earnings difference estimated from this regression is 25.48 . (Round your response to two decimal places.) Is the collegeihigh school earnings difference estimated from this regression statistically significant at the 5% level? Since the absolute value of the tstatistic is greater than the critical value for 95% confidence, the collegeihigh school earnings difference estimated from this regression is statistically signicant at the 5% level. Construct a confidence interval of 95% for the collegeihigh school earnings difference. The 95% confidence interval for the collegeihigh school earnings difference is ( , ). (Round your responses to two decimal places.) The t-statistic for the malefemale earnings difference estimated from this regression is -13.33'. (Round your response to two decimal places.) Is the male female earnings difference estimated from this regression statistically signicant at the 20% level? Since the t-statistic is greater than the critical value for 80% condence. the male female earnings difference estimated from this regression is ' statistically signicant at the 20% level' Construct a condence interval of 80% for the malefemale earnings difference. The 30% condence interval for the malefemale earnings difference is I ' 2.63'. ' 2.17'). (Round your responses (0 (W0 decimal places.)