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 ($) 400 4,600 450 5,600 550 6,000 600 6,500 700 7,000 750 7,600 a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Compute b0 and b1 (to 1 decimal). Do not round intermediate calculations. Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations. b. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations. c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: r^2 report between 0 and 1.
An important application of
| Production Volume (units) | Total Cost ($) |
| 400 | 4,600 |
| 450 | 5,600 |
| 550 | 6,000 |
| 600 | 6,500 |
| 700 | 7,000 |
| 750 | 7,600 |
a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume.
Compute b0 and b1 (to 1 decimal). Do not round intermediate calculations.
Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations.
b. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations.
c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: r^2 report between 0 and 1.
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