Jacob runs a chain of corner stores. He has developed a regression model to predict the daily revenues (in thousands of dollars) of each store he owns. He believes that the daily revenue of a store depends on three different variables such as the number of Employees working on the day (Employees), the Number of Hours the store is open on the day (Hours) and the distance of the store in miles from the city center (Distance). He has generated the following regression output: SUMMARY OUTPUT Regression Statistics Multiple R 0.465229201 R Square 0.216438209 Adjusted R Square Standard Error 15.33431469 Observations 20 ANOVA of SS MS F Significance F Regression 3 6080.20 2026.7 5.2476791 0.010324022 Residual 16 6179.44 386.22 Total 19 0.00 Coefficients Standard Error tStat P-value Lower 95% Upper 95% Intercept 63.95872 14.56976671 0.045 -1.12456 28.87201 Employees 9.29696 2.990575561 0.01 -5.6228 13.32554 Hours 7.36168 2.507335371 0.04 -3.37368 19.98832 Distance -3.7101 1.336597408 0.04 -4.49824 18.87785 Answer the follwoing questions Based on the given output a) (1 mark) How much of the variation in dependent variable seems to be explained by variation in the independent variables? b) (2 marks) Does the regression model seem to have predictive value? Explain what value from the output is the basis for your answer? c) (3 marks) Given that you require a 95% level of confidence, list the variables that appear to affect the daily revenue of a store? d) (4 marks) Predict the daily revenues for a store that is open for 7 hours , has four Employees and is located at 2.5 miles from the city center