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Unformatted text preview: he data well. The vert ical differences between the actual costs and the regression line appear to be quite small. Significance of independent variable. The regression line has a steep posit ive slope and increases by more than $19 for each addit ional customer. Because the slope is not flat, there is a strong relat ionship between number of customers and total restaurant costs. The regressio n line is the more accurate estimate of the relationship between number of customers and total restaurant costs because it uses all available data points while the highlow method relies only on two data points and may therefore miss some informat ion contained in the other data points. Nevertheless, the graphs of the two lines are fairly close to each other, so the cost funct ion est imated using the highlow method appears to be a good approximation o f the cost funct ion est imated using the regression method. 4. The cost estimate by the two methods will be equal where the two lines intersect. You can find the number of customers by setting the two equations to be equal and so lving for x. That is, $1,250 + $20.60x = $2,453 + $19.04x $20.60 x ─ $19.04 x = $2,453 ─ $1,250 1.56 x = 1,203 x = 771.15 or ≈ 771customers. 1021 1033 (3040 min.) Highlow method, regression analysis. 1. Solution Exhibit 1033 presents the plots of advertising costs on revenues. SOLUTION EXHIBIT 1033 Plot and Regressio n Line of Advertising Costs on Revenues $90,000 80,000 70,000 60,000 R evenues 50,000 40,000 30,000 20,000 10,000 0 $0 $1,000 $2,000 $3,000 $4,000 $5,000 Advertising Costs 2. Solution Exhibit 1033 also shows the regressio n line o f advertising costs on revenues. We evaluate the est imated regressio n equat ion using the criteria of econo mic plausibilit y, goodness of fit, and slope of the regressio n line. Economic plausibility. Advert ising costs appears to be a plausible cost driver o f revenues. Restaurants frequent ly use newspaper advert ising to promote their restaurants and increase their patronage. Goodness of fit. The vertical differences between actual and predicted revenues a...
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 Spring '10
 Cole
 Cost Accounting

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