An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation.
Production
Volume (units) Total Cost ($)
400 4,300
450 5,300
550 5,700
600 6,200
700 6,700
750 7,300
Compute b1 and b0 (to 1 decimal). b1= b0=
Complete the estimated regression equation (to 1 decimal).
y=_+_x
What is the variable cost per unit produced (to 1 decimal)?
$
Compute the coefficient of determination (to 3 decimals). Note: report r2 between 0 and 1. r2 =
What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)?
__%
The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)?
$
The statistical software output for this problem is:
Hence,
Regression equation:
y = 1546.7 + 7.6 x
r2 = 0.959
Percent of variation explained = 95.9%
Estimated total cost = $5347
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