The data set consists of information on 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) 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 () () () () Female () () () () Age () () () Northeast () () Midwest () () South () () Intercept () () () Summary Statistics F-statistic for regional effects = 0 SER n Part 2 Using the regression results in column (1): The t-statistic for the collegehigh school earnings difference estimated from this regression is enter your response here. (Round your response to two decimal places.) Is the collegehigh school earnings difference estimated from this regression statistically significant at the % level? Since the absolute value of the t-statistic is greater less than the critical value for % confidence, the collegehigh school earnings difference estimated from this regression is is not statistically significant at the % level