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) 400 Total Cost ($) 3900 500 4700 550 5300 600 5700 650 6400 700 7100 The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ=-490.00+10.60x. a. The company's production schedule shows that 450 units must be produced next month. Predict the total cost for next month. ŷ* (to 2 decimals) b. Develop a 99% prediction interval for the total cost for next month. (to 2 decimals) 8 t- value decimals) ( (to 3 (to 2 Spred decimals) Prediction Interval for an individual Value next month ) (to whole number)

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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.
Total Cost
($)
3900
4700
5300
5700
6400
7100
The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation =-490.00 + 10.60x,
a. The company's production schedule shows that 450 units must be produced next month. Predict the total cost for next month.
ŷ* =
* (to 2 decimals)
b. Develop a 99% prediction interval for the total cost for next month.
(to 2
decimals)
8
t-
value decimals)
(to 3
* (to 2
Spred decimals)
Prediction Interval for an individual Value next month
Ⓡ
Production Volume
(units)
400
500
550
600
650
700
) (to whole number)
c. If an accounting cost report at the end of next month shows that the actual production cost during the month was $6,000, should managers be concerned about incurring such a high total
cost for the month? Discuss.
Based on one month, $6,000 is not
✔ outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause
concern.
Transcribed Image Text:An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Total Cost ($) 3900 4700 5300 5700 6400 7100 The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation =-490.00 + 10.60x, a. The company's production schedule shows that 450 units must be produced next month. Predict the total cost for next month. ŷ* = * (to 2 decimals) b. Develop a 99% prediction interval for the total cost for next month. (to 2 decimals) 8 t- value decimals) (to 3 * (to 2 Spred decimals) Prediction Interval for an individual Value next month Ⓡ Production Volume (units) 400 500 550 600 650 700 ) (to whole number) c. If an accounting cost report at the end of next month shows that the actual production cost during the month was $6,000, should managers be concerned about incurring such a high total cost for the month? Discuss. Based on one month, $6,000 is not ✔ outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause concern.
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