(Q1): A sample of 4 students was taken from a large class of students. Their preparation time (x) and their test score (y) are shown in the Table below. Using simple linear regression, we want to predict the exam score as a function of the preparation time. Preparation time in hours (x Exam Score (y) 10 2 25 35 40 (a) Use Excel Data Analysis menu item ( and then choose "Regression method" from the list ) to find the slope and intercept of the best-fit line. Paste the output from XL to show that you have run it. You can also attach a screenshot instead.SUMMARY OUTPUT Regression Statistics Multiple R 0.9759 R Square 0.952381 Adjusted R Square 0.928571 Standard Error 3.535534 Observations 4 ANOVA df SS MS F ignificance F Regression 500 500 40 0.0241 Residual 2 25 12.5 Total 3 525 Coefficientsandard Err t Stat P-value Lower 95%Upper 95%ower 95.09pper 95.0% Intercept 2.5 4.330127 0.57735 0.622036 -16.131 21.13103 -16.131 21.13103 Preparation time in hours (x) 10 1.581139 6.324555 0.0241 3.196909 16.80309 3.196909 16.80309(b) Based on XL's ANOVA test, is the best-fit line better at predicting exam scores than simply predicting that the exam score will always be 22.5, regardless of preparation time? Explain. (c) Based on the 95% confidence interval reported by XL for the slope, can you reject the hypothesis that the slope is 0? Explain. Write down the confidence interval