Using the regression results in column (2):
(a) Is age an important determinant of earnings? Use an appropriate statistical test and/or confidence interval to explain your answer.
(b) Sally is a 29-year-old female college graduate. Betsy is a 34-year-old female college graduate. Construct a 95% confidence interval for the expected difference between their earnings.

The data set consists of information on 4000 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 34 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 1998 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)

Results of Regressions of Average Hourly Eamings on Gender and Education Binary Variables and Other Characteristics Using 1998 Data from the Current Population Survey Dependent Variable: average hourly earnings (AHD. College (X) Female (X) Age (%) Northeast (X Midwest (Xs) South (X) 5.48 (021) -262 (020) 0.29 (004) 5.46 (021) 5.44 (0.21) -2.62 (0.20) 0.29 (0.04) 0.69 (0.30) 0.60 (0.28) -0.27 (0.26) .75 -2.64 (0.20 12.69 (0.14) Intercept 4.40 Summary Statistics and Joint Tests F-statistic for regional effects =0 SER R2 6.27 0.176 4000 6.22 0.190 4000 6.10 6.21 0.194 4000