The data set consists of information on 4100 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 4100 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 othenNise) Midwest = binary variable (1 if Region = Midwest, 0 othenNise) South = binary variable (1 if 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 (1 ) (2) (3) College (X1) 5.95 5.97 5.93 (0.23) (0.23) (0.23) Female (X2) 2.88 2.86 2.86 (0.22) (0.22) (0.22) Age (X3) 0.32 0.32 (0.03) (0.03) Northeast (X4) 075 (0.33) Midwest (X5) 0.65 (0.31) South (X6) 0.29 (0.28) Intercept 13.83 4.80 4.09 (0.15) (1.14) (1.16) Summary Statistics Fstatistic for regional effects = 0 6.11 SER 6.83 6.78 6.77 R2 0.192 0.207 0.211 n 4100 4100 4100 Using the regression results in column (2): The tstatistic for the coefcient on Age is 10.67 . (Round your response to two decimal places.) The pvalue for the preceding tstatistic is 0.0000 . (Round your response to four decimal places.) Does this imply that age is an important determinant of earnings? Yes, age is an important determinant of earnings because the high pvalue implies that the coefficient on age is statistically signicant at the 5% level. V Yes, age is an important determinant of earnings because the low pvalue implies that the coefficient on age is statistically significant at the 1% level. No, age is not an important determinant of earnings because the low pvalue implies that the coefficient on age is not statistically signicant at the 5% level. No, age is not an important determinant of earnings because the high pvalue implies that the coefficient on age is not statistically significant at the 1% level. Using the regression results in column (3): Sally is a 30yearold female college graduate. Betsy is a 43-yearold female college graduate. Construct a confidence interval of 95% for the expected difference between their earnings. The 95% confidence interval for the expected difference between their earnings is ( , ). (Round your responses to two decimal places.)