Suppose that a researcher, using wage data on 235 randomly selected male workers and 263 female workers, estimates the OLS regression g? = 11.769 +1993 x Male, R2 = 0.07, SER = 3.9, (0.2162) (0.3384) where Wage is measured in dollars per hour and Male is a binary variable that is equal to 1 if the person is a male and 0 if the person is a female. Define the wage gender gap as the difference in mean earnings between men and women. What is the estimated gender gap? The estimated gender gap equals $ per hour. (Round your response to three decimal places.) The null and alternative hypotheses are Ho: B1 = 0 versus H1 : B, # 0. The t-statistic for testing the null hypothesis that there is no gender gap is . (Round your response to two decimal places.) The p-value for testing the null hypothesis that there is no gender gap is . (Round your response to four decimal places.) The estimated effect of gender gap is statistically significant at the: 1. 5% level 11. 1% level III. 0.01% level O A. Ill only. O B. I only. O C. I, II, and III. O D. I and II. Construct a 95% confidence interval for the effect of gender gap. The 95% confidence interval for the effect of gender gap is ( ], |). ( Round your responses to two decimal places.) From the sample, the average wage of women is $ per hour. (Round your response to three decimal places.) From the sample, the average wage of men is $ per hour. (Round your response to three decimal places.) Another researcher uses these same data but regresses Wages on Female, a variable that is equal to 1 if the person is female and 0 if the person a male. What are the regression estimates calculated from this regression? Wage = Yo + 71 x Female, R2, SER. Yo = [. (Round your response to three decimal places.) 71 = [. (Round your response to three decimal places.)