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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. ŷ* = (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. 8 t-value (to 2 decimals) (to 3 decimals) Production Volume (units) 400 Spred (to 2 decimals) Prediction Interval for an individual Value next month 450 500 550 600 Total Cost ($) 3900 4400 700 The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = -317.14+10.66x. a. The company's production schedule shows that 650 units must be produced next month. Predict the total cost for next month. 5100 5500 6300 7000 ( ) (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 - Select your answer - 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 total cost data for a manufacturing operation. ŷ* = (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. 8 t-value (to 2 decimals) (to 3 decimals) Production Volume (units) 400 Spred (to 2 decimals) Prediction Interval for an individual Value next month 450 500 550 600 Total Cost ($) 3900 4400 700 The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = -317.14+10.66x. a. The company's production schedule shows that 650 units must be produced next month. Predict the total cost for next month. 5100 5500 6300 7000 ( ) (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 - Select your answer - 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
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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. ŷ* = (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. = (to 2 decimals) (to 3 decimals) Spred (to 2 decimals) Prediction Interval for an individual Value next month S Production Volume (units) t-value 400 450 500 550 600 Total Cost ($) 3900 700 The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation a. The company's production schedule shows that 650 units must be produced next month. Predict the total cost for next month. 4400 5100 5500 6300 7000 = : -317.14+10.66x. ) (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, $6,000 - Select your answer ✓outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause concern.
Transcribed Image Text:An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. ŷ* = (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. = (to 2 decimals) (to 3 decimals) Spred (to 2 decimals) Prediction Interval for an individual Value next month S Production Volume (units) t-value 400 450 500 550 600 Total Cost ($) 3900 700 The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation a. The company's production schedule shows that 650 units must be produced next month. Predict the total cost for next month. 4400 5100 5500 6300 7000 = : -317.14+10.66x. ) (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 - Select your answer ✓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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