Consider the two variable regression model: Y; = Bo + B, Eduli + B2 EXP2i + Uj, where Y denotes the average monthly income, Edu denotes the number of years of education, Exp denotes the number of years of experience, and u; denotes the error term. Suppose the researcher wants to test whether the effect of education on average monthly income and the effect of experience on the average monthly income of an individual are the same or not. So, the test the researcher wants to conduct is Ho: B1 = B2 VS. H1: B1 # 2. The hypotheses can be tested by modifying the original regression equation to turn the restriction into a restriction on a single regression coefficient. Suppose the regression function is modified in the following way: Y; = Po + 1 Edu1; + B2 W1; + u;, where y1 = B1 - B2 and W; = Eduli + Exp2i . Since 71 = 1 - 2, the test the researcher wants to conduct will now be Ho: y= 0 vs. H1 : y# 0. Let y, and SE(y, ), denote the estimated slope coefficient of y, and the standard error of y1 , respectively. If SE(71 ) is 0.50 and y, is 1.25, then the 95% confidence interval for the difference between the coefficients, B, - 2, denoted by 71, is (], ). (Round your answer to two decimal places. Enter a minus sign if your answer is negative.)Based on the calculated confidence interval, we can say that at the 5% significance level, we will the null hypothesis Ho: y = 0. reject fail to reject