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 account estimate the cost associated with a particular månufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. 5. Production Volume (units) Total Cost ($) 6. 400 3800 50 4600 550 5400 650 6300 700 7200 750 7800 The data on the production volume I and total cost y for particular manufacturing operation were used to develop the estimated rugression equation ý=-996.22 + 11.57x a. The company's production schedule shows that 450 units must be produced next month, Predict the total cost for next month. (to 2 decimals) b. Develop a 95% prediction interval for the total cost for next month. (Lo 2 decimals) t-value (to 3 decimals) (to 2 decimals) pnd Prediction Interval for an individual Value next month (to whole number) c. an accounting cost report at the and of next munth shows that the actual pruduction cost during the munth was $6,000, stiuuld mariagurs be concerned about incurring sucha high total cust for the imonth? Discuss. Based on one month, $6,000 is not V outside tiư upper limit of the prediction interval. A suquence of five to saven monthis with consistently higti costs slhould cause Luncen
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an account estimate the cost associated with a particular månufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. 5. Production Volume (units) Total Cost ($) 6. 400 3800 50 4600 550 5400 650 6300 700 7200 750 7800 The data on the production volume I and total cost y for particular manufacturing operation were used to develop the estimated rugression equation ý=-996.22 + 11.57x a. The company's production schedule shows that 450 units must be produced next month, Predict the total cost for next month. (to 2 decimals) b. Develop a 95% prediction interval for the total cost for next month. (Lo 2 decimals) t-value (to 3 decimals) (to 2 decimals) pnd Prediction Interval for an individual Value next month (to whole number) c. an accounting cost report at the and of next munth shows that the actual pruduction cost during the munth was $6,000, stiuuld mariagurs be concerned about incurring sucha high total cust for the imonth? Discuss. Based on one month, $6,000 is not V outside tiư upper limit of the prediction interval. A suquence of five to saven monthis with consistently higti costs slhould cause Luncen
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can
estimate the cost associated with a particular månufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation.
Production Volume (units)
Total Cost ($)
6.
400
3800
500
4600
550
5400
650
6300
700
7200
750
7800
The data on the production volume I and total cost y for particular manufacturing operation were used to develop the estimated regression equation ý-996.22 + 11.57x
a. The company's production schedule shows that 450 units must be produced next month. Predict the total cost for next month.
(to 2 decimals)
b. Develop a 95% prediction interval for the total cost for next month.
(Lo 2 decimals)
t-value
(Lo 3 decimals)
(to 2 decimals)
prnd
Prediction Interval for an individual Value next month
D (to whole number)
c. I an accounting cost report at the and of next month shows that the actual production cost during the month was 56,000, should managers be concermed about incurring such a high total cost for the month? Discuss.
Based on one month, $6,000 is not
v outside tiu upper limit of the prediction inturval. A suuence of five to Mven months with consistently high costs slhuuld cause Luncen
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