Consider a model of the following form: log(Wage) = Bo + B.Education + where Wage measures the wage in pounds per hours, and Education denotes years of schooling. Using a random sample of worker we estimate this SLR model through OLS and obtain the following results: Source SS df MS II II Model Residual 25.4557561 114.281544 1 25.4557561 504 .226749096 Number of obs F(1, 504) Prob > F R-squared Adj R-squared Root MSE 506 112.26 0.0000 0.1822 0.1805 .47618 Total 139.7373 505 .276707525 logwage Coef. Std. Err. t P>t [95% Conf. Interval] educ _cons .0806731 .5997626 .0076139 .0978525 10.60 6.13 0.000 0.000 .0657142 .4075136 .0956321 .7920115 6. When we conduct the F-test, we need to compute F-statistic. Can F-statistic take negative values? Explain. (1.5 marks) 7. Consider the following regression model y= Bo + B{x1 + Suppose you are given a random sample {ti,yi}"=1. Give an estimator of @1. Is your estimator an unbiased estimator of B1? Explain. (1.5 marks) Consider a model of the following form: log(Wage) = Bo + B.Education + where Wage measures the wage in pounds per hours, and Education denotes years of schooling. Using a random sample of worker we estimate this SLR model through OLS and obtain the following results: Source SS df MS II II Model Residual 25.4557561 114.281544 1 25.4557561 504 .226749096 Number of obs F(1, 504) Prob > F R-squared Adj R-squared Root MSE 506 112.26 0.0000 0.1822 0.1805 .47618 Total 139.7373 505 .276707525 logwage Coef. Std. Err. t P>t [95% Conf. Interval] educ _cons .0806731 .5997626 .0076139 .0978525 10.60 6.13 0.000 0.000 .0657142 .4075136 .0956321 .7920115 6. When we conduct the F-test, we need to compute F-statistic. Can F-statistic take negative values? Explain. (1.5 marks) 7. Consider the following regression model y= Bo + B{x1 + Suppose you are given a random sample {ti,yi}"=1. Give an estimator of @1. Is your estimator an unbiased estimator of B1? Explain. (1.5 marks)