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4. Kathy Jungemann, owner of Flower Power, 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 Power 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 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 Power'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? 1. Requirement 1. Determine the firm's cost equation (use the output from the Excel regression). (Enter amounts to two decimal places.) y = $ X + $ Requirement 2. Determine the R-square (use the output from the Excel regression). The R-square is (1) What does Flower Power's R-square indicate? The R-square indicates that the cost equation explains (2) of the variability in the data. In other words, it (3) Flower Power (4) feel confident using this cost equation to predict total costs at other volumes within the same relevant range. Requirement 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? (Round your answer to the nearest cent.) The operating costs at a volume of 16,500 miles is $ Should the company rely on this cost estimate? Why or why not? Since the r-square is (5) the company (6) 1: Regression analysis SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.87 Standard Error 124.99 Observations 7 ANOVA df SS MS F Significance F 0.0013 Regression 1 668,089.58 42.77 668,089.58 78,110.42 Residual 5 15,622.08 6 Total 746,200.00 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 X Variable 1 0.28 0.04 6.54 0.00 0.17 0.39 O 87.4% (1) O 0.895322 0 0.228498 O 0.946215 O 0.281064 O 0.874387 (2) O 89.5% O 22.8% O 28.1% O 94.6% (3) Oshould be used with caution. O fits the data quite well. O does not fit the data at all. (4) O may or may not O will not O should (5) O over 80 O between .50 and .80 o less than .50 (6) O can rely on this cost estimate O should use the cost equation with caution. O should not rely on this cost estimate. 4. Kathy Jungemann, owner of Flower Power, 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 Power 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 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 Power'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? 1. Requirement 1. Determine the firm's cost equation (use the output from the Excel regression). (Enter amounts to two decimal places.) y = $ X + $ Requirement 2. Determine the R-square (use the output from the Excel regression). The R-square is (1) What does Flower Power's R-square indicate? The R-square indicates that the cost equation explains (2) of the variability in the data. In other words, it (3) Flower Power (4) feel confident using this cost equation to predict total costs at other volumes within the same relevant range. Requirement 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? (Round your answer to the nearest cent.) The operating costs at a volume of 16,500 miles is $ Should the company rely on this cost estimate? Why or why not? Since the r-square is (5) the company (6) 1: Regression analysis SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.87 Standard Error 124.99 Observations 7 ANOVA df SS MS F Significance F 0.0013 Regression 1 668,089.58 42.77 668,089.58 78,110.42 Residual 5 15,622.08 6 Total 746,200.00 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 X Variable 1 0.28 0.04 6.54 0.00 0.17 0.39 O 87.4% (1) O 0.895322 0 0.228498 O 0.946215 O 0.281064 O 0.874387 (2) O 89.5% O 22.8% O 28.1% O 94.6% (3) Oshould be used with caution. O fits the data quite well. O does not fit the data at all. (4) O may or may not O will not O should (5) O over 80 O between .50 and .80 o less than .50 (6) O can rely on this cost estimate O should use the cost equation with caution. O should not rely on this cost estimate
