Kristen Reynolds, owner of Flower Direct, operates a local chain of floral shops. Each shop has its own delivery van. Instead of charging a flat delivery fee, Reynolds wants to set the delivery fee based on the distance driven to deliver the flowers. Reynolds wants to separate the fixed and variable portions of her van operating costs so that she has a better idea how delivery distance affects these costs. Flower Direct does a regression analysis on the next year's data using Excel. The output generated by Excel is as follows: B Click the icon to view the regression analysis.) Read the requirements. Requirement 1. Determine the firm's cost equation (use the output from the Excel regression). (Enter amounts to two decimal places.) y= 947.2 x+ 0.27 en Reynolds, owner of Flower Direct, operates a local chain of floral shops. Each shop has its delivery van. Instead of charging a flat delivery fee, Reynolds wants to set the delivery fee ed on the distance driven to deliver the flowers. Reynolds wants to separate the fixed and able portions of her van operating costs so that she has a better idea how delivery distance cts these costs. Flower Direct does a regression analysis on the next year's data using Excel. i Requirements 1. Determine the firm's cost equation (use the output from the Excel regression). 2. Determine the R-squared (use the output from the Excel regression). What does Flower Direct's R-squared indicate? 3. Predict van operating costs at a volume of 17,000 miles assuming the company would use the cost equation from the Excel regression regardless of its R-squared. Should the company rely on this cost estimate? Why or why not? Print Done A B D E LL G N 0.85 1 SUMMARY OUTPUT Regression Statistics 3 Multiple R 4 R Square 5 Adjusted R Square 6 Standard Error 0.72 0.66 207.23 7 Observations 7 8 ANOVA Significance F 9 df SS MS F 10 Regression 1 545,878.49 545,878.49 12.71 0.0161 11 Residual 5 214,721.51 b7 42,944.30 12 Total 6 760,600.00 Print Done 4 R Square 0.72 0.66 5 Adjusted R Square 6 Standard Error 207.23 7 Observations 7 8 ANOVA Significance F 9 df SS MS F 10 Regression 1 545,878.49 545,878.49 12.71 0.0161 11 Residual 5 214,721.51 42,944.30 12 Total 6 760,600.00 Standard Error P. value Lower 95% Upper 95% 13 Coefficients t Stat 947.20 1,217.79 0.78 0.47 -2,183.23 4,077.64 14 Intercept X Variable 15 1 0.27 0.08 3.57 0.02 0.08 0.46