Regression Statistics Multiple R 0.87 R Square 0.75 Adjusted R Square 0.70 Standard Error 181.55 Observations ANOVA df MS 498,803.13 SS 498,803.13 164,796.87 F 15.13 Significance F 0.0115 Regression 1 5 Residual 32,959.37 663,600.00 Total Standard Lower Upper Coefficients Error t Stat P-value 95% 95% 8 Intercept X Variable 1 1964.29 0.20 26.89 0.05 2.38 3.89 0.06 0.01 -161.31 0.07 4,089.89 0.34 Kallie Jungemann, 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, 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. Flower Direct 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 Flower Direct's R-square 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-square. Should the company rely on this cost estimate? Why or why not? Requirement 1. Determine the firm's cost equation (use the output from the Excel regression). (Enter amounts to two decimal places.) y=$ Ox+$ O