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# acct 6 answer - (10-15 min E 6-21 The month with the...

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(10-15 min.) E 6-21 The month with the highest volume is February and the month with the lowest volume is July. The high-low method uses only these two months to determine the cost equation. Step 1) Find the slope: Rise = y (high) y (low) = (\$5,748 \$5,020) = 0.26 per mile Run x (high) x (low) (17,300 14,500) miles Step 2) Find the vertical intercept (the fixed cost component) by plugging the slope into a cost equation, using either the February or May data. Using February data: y = vx + f \$5,748 = (\$0.26 per mile × 17,300 miles) + f f = \$1,250 Or, Using July data: y = vx + f \$5,020 = (\$0.26 per mile × 14,500 miles) + f f = \$1,250 Step 3) Write the cost equation: y = \$0.26 x + \$1,250 where y = monthly van operating costs x = number of miles driven

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Predict monthly operating costs when volume is 15,000 miles y = (\$0.26 × 15,000) + \$1,250 y = \$5,150
(15-20 min.) E 6-22 Req. 1 Student graphs may vary slightly. For example, some students may force the origin to show on the graph by setting the minimum x and y axis values to zero. The graph title and axes titles may also vary from that shown. Van Operating Costs 14,000 14,500 15,000 15,500 16,000 16,500 17,000 17,500 4800 5000 5200 5400 5600 5800 6000 Miles Driven Cost

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(continued) E 6-22 Req. 2 The Excel regression output looks like this: SUMMARY OUTPUT Regression Statistics Multiple R 0.949707 R Square 0.901943 Adjusted R Square 0.882332 Standard Error 115.3003 Observations 7 ANOVA df SS MS F Significanc e F Regression 1 611411.1 611411.1 45.99092 0.00106 Residual 5 66470.85 13294.17 Total 6 677882 Coefficient s Standard Error T Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 802.3909 677.7279 1.183943 0.289648 -939.761 - 2544.543 -939.761 - 2544.543 X Variable 0.290029 0.042767 6.781661 0.00106 0.180094 0.399965 0.180094 0.399965 Req. 3 Based on the Excel output, Flower Power’s van operating cost equation is: y = \$0.29 x + \$802.39 where y = monthly van operating costs x = miles driven
This equation found by looking at the “X variable 1 coefficient” (.290029) and the “intercept coefficient” (802.3909) on the Excel output.

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(continued) E 6-22 Req. 4 The R-square is .901 (rounded). The r-square indicates that the cost equation explains 90.1% of the variability in the data. In other words, it fits the data quite well. Flower
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