SUMMARY OUTPUT Regression Statistics Multiple R 0.95 0.90 R Square Adjusted R Square Standard Error 0.87 124.99 Observations 7 ANOVA df SS MS F Significance F 0.0013 Regression 668,089.58 42.77 668,089.58 78,110.42 Residual 15,622.08 5 6 746,200.00 Total Standard Lower Upper 95% Coefficients Error t Stat P-value 95% Intercept 931.23 678.87 1.37 0.23 -813.87 2,676.33 0.28 0.04 X Variable 1 6.54 0.00 0.17 0.39 Kallie Jungemann, owner of Flowers 4 You, operates a local chain of floral shops. Each shop has its own delivery van. Instead of charging a flat delivery fee, Jungemann wants to set the delivery fee based on the distance driven to deliver the flowers. Jungemann 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. Flowers 4 You does a regression analysis on the next year's data using Excel. The output generated by Excel is as follows: (Click the icon to view the regression analysis.) Requirements 1. Determine the firm's cost equation (use the output from the Excel regression). 2. Determine the R-square (use the output from the Excel regression). What does Flowers 4 You's R-square indicate? 3. Predict van operating costs at a volume of 16,500 miles assuming the company would use the cost equation from the Excel regression regardless of its R-square. Should the company rely on this cost estimate? Why or why not