i) An analyst used Excel to investigate the relationship between "Weekly Sales" (in $million) of a store and the "Hours" the store is open per week. SUMMARY OUTPUT Regression Statistics Multiple R 0.962 R Square 0.926 Adjusted R Square 0.919 Standard Error 0.097 Observations 12 Standard Lower Upper Coefficients Error t Stat P-value 95% 95% Intercept 0.074 0.090 0.822 0.430 -0.126 0.273 Hours 0.015 0.001 11.20 0.000 0.012 0.018 Comment on the suggested relationship. What is the predicted effect on weekly sales of a store being open one extra hour? Hint: Refer to the direction of the relationship between the 2 variables & use an appropriate regression statistic to assess how well the regression equation fits the sample data.ii) Note: Unrelated to part i. At a company, employees receive $200 (GBP/pounds) commission even if they sell nothing, plus 1% for all sales made under $20,000 and 4% for all sales over E20,000. Which graph (A, B or C) best represents this scenario? Please explain your answer with reference to the vertical intercept and slope/gradients. A B C Commission Commission Commission 0 Sales 0 Sales 0 Sales