Consider a model of the following form: log(Wage) = Be + 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 Model Residual 25.4557561 114.281544 1 504 25.4557561 .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>It! [95% Conf. Interval] educ .0806731 .5997626 .0076139 .0978525 10.60 6.13 0.000 0.000 .0657142 .4075136 .0956321 .7920115 cons 1. Interpret the value of the slope parameter Education. What does the value of 1 mean? (1.5 marks) 2. Construct the 98% confidence interval for the hypothesis test Ho: Bi=0 H: H, is not true (The 95th, 98th, 99th, and 99.5th percentile of the tn-k-1 are 1.6479, 2.0591, 2.3338, and 2.5856 respectively.) Keep four digits after the decimal point. (1.5 marks) 3. Conduct the hypothesis test Ho: Bi = 0.1 H: Bi