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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 tota cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4000 500 5000 550 5900 600 6500 700 7500 800 7900 The data on the production volume a and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = 95.92 + 10.20x. a. The company's production schedule shows that 750 units must be produced next month. Predict the total cost for next month. 7745.9 (to 2 decimals) ŷ* = b. Develop a 99% prediction interval for the total cost for next month. 0.01 (to 2 decimals) t-value 4.604 (to 3 decimals) (to 2 decimals) Spred Prediction Interval for an individual Value next month ( 6204 9288 ) (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 f the month? Discuss. Based on one month, $6,000 is not A outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause concern.

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 tota cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4000 500 5000 550 5900 600 6500 700 7500 800 7900 The data on the production volume a and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = 95.92 + 10.20x. a. The company's production schedule shows that 750 units must be produced next month. Predict the total cost for next month. 7745.9 (to 2 decimals) ŷ* = b. Develop a 99% prediction interval for the total cost for next month. 0.01 (to 2 decimals) t-value 4.604 (to 3 decimals) (to 2 decimals) Spred Prediction Interval for an individual Value next month ( 6204 9288 ) (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 f the month? Discuss. Based on one month, $6,000 is not A outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause concern.

MATLAB: An Introduction with Applications
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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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 4000 500 5000 550 5900 600 6500 700 7500 800 7900 The data on the production volume x and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = 95.92 + 10.20x. a. The company's production schedule shows that 750 units must be produced next month. Predict the total cost for next month. ŷ* : 7745.9. (to decimals) 2 b. Develop a 99% prediction interval for the total cost for next month. 0.01 (to 2 decimals) t-value 4.604 (to decimals) 3 Spred |(to 2 decimals) Prediction Interval for an individual Value next month 6204 9288 ) (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, $6,000 [ is not +) outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause concern.
Transcribed Image Text:An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4000 500 5000 550 5900 600 6500 700 7500 800 7900 The data on the production volume x and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = 95.92 + 10.20x. a. The company's production schedule shows that 750 units must be produced next month. Predict the total cost for next month. ŷ* : 7745.9. (to decimals) 2 b. Develop a 99% prediction interval for the total cost for next month. 0.01 (to 2 decimals) t-value 4.604 (to decimals) 3 Spred |(to 2 decimals) Prediction Interval for an individual Value next month 6204 9288 ) (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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