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 ($) 350 3900 450 4800 550 5800 650 6600 700 7200 800 7600 The data on the production volume and total cost for particular manufacturing operation were used to develop the estimated regression equation y=993.37 + 8.55x a. The company’s production schedule shows that 600 units must be produced next month. Predict the total cost for next month. y^* (to 2 decimals) b. Develop a 95 prediction interval for the total cost for next month. s (to 2decimals) t-value (to 2decimals) s pred (to 2 decimals) Prediction Interval for an individual Value next month ( , ) (to whole number)
An important application of
| Production Volume (units) | Total Cost ($) |
| 350 | 3900 |
| 450 | 4800 |
| 550 | 5800 |
| 650 | 6600 |
| 700 | 7200 |
| 800 | 7600 |
The data on the production volume and total cost for particular manufacturing operation were used to develop the estimated regression equation y=993.37 + 8.55x
a. The company’s production schedule shows that 600 units must be produced next month. Predict the total cost for next month.
| y^* | (to 2 decimals) |
b. Develop a 95 prediction interval for the total cost for next month.
| s | (to 2decimals) |
| t-value | (to 2decimals) |
| s pred | (to 2 decimals) |
Prediction Interval for an individual Value next month
( , ) (to whole number)
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