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=averagehourlyearnings(in2005dollars)College=binaryvariable(1ifcollege,0ifhighschool)Female=binaryvariable(1iffemale,0ifmale)Age=age(inyears)Ntheast=binaryvariable(1ifRegion=Northeast,0otherwise)Midwest=binaryvariable(1ifRegion=Midwest,0otherwise)South=binaryvariable(1ifRegion=South,0otherwise)West=binaryvariable(1ifRegion=West,0otherwise) Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using Data from the Current Population Survey Using the regression results in column (2): On average, a worker earns $ per hour for each year that he or she ages. Is age an important determinant of earnings? A. No, because the difference in wages is minimal. B. No, because older workers do not earn significantly more than younger workers. C. Yes, because younger workers earn more than older workers. D. Yes, because wages are not consistent across different age groups. Sally is a 31-year-old female college graduate. Betsy is a 35-year-old female college graduate. Predict Sally's and Betsy's earnings. Sally's earnings prediction is $ per hour. (Round your response to two decimal places.) Betsy's earnings prediction is $ per hour. (Round your response to two decimal places.) The expected difference in earnings between Sally and Betsy is $ per hour. (Round your response to two decimal places.)