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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 4100 450 4600 500 5600 600 6400 700 7400 750 8400 N The data on the production volume x and total cost y for particular manufacturing operation were used to develop the estimated regression equation = -524.58 +11.66z. a. The company's production schedule shows that 550 units must be produced next month. Predict the total cost for next month. (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. (to 2 decimals) 8 t-value (to 3 decimals) (to 2 decimals) 8pred Prediction Interval for an individual Value next month ) (to whole number) A 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. outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause Based on one month, $6,000 is not 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. Production Volume (units) Total Cost ($) 400 4100 450 4600 500 5600 600 6400 700 7400 750 8400 N The data on the production volume x and total cost y for particular manufacturing operation were used to develop the estimated regression equation = -524.58 +11.66z. a. The company's production schedule shows that 550 units must be produced next month. Predict the total cost for next month. (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. (to 2 decimals) 8 t-value (to 3 decimals) (to 2 decimals) 8pred Prediction Interval for an individual Value next month ) (to whole number) A 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. outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause Based on one month, $6,000 is not concern.

Elementary Linear Algebra (MindTap Course List)
Elementary Linear Algebra (MindTap Course List)
8th Edition
ISBN:9781305658004
Author:Ron Larson
Publisher:Ron Larson
Chapter2: Matrices
Section2.CR: Review Exercises
Problem 90CR
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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 O and total cost data for a manufacturing operation. O Production Volume (units) Total Cost ($) 400 4100 450 4600 500 5600 600 6400 700 7400 750 8400 N The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = -524.58+11.66z. a. The company's production schedule shows that 550 units must be produced next month. Predict the total cost for next month. ŷ* = (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. X (to 2 decimals) 8 t-value (to 3 decimals) 8pred (to 2 decimals) Prediction Interval for an individual Value next month ) (to whole number) A 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. outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause Based on one month, $6,000 is not concern. DD F10 FB A 888 F7 80 FA FA ! 1 Q 2 W 2 # 3 E C $ 4 R LL F % y 5 T G A 6 Y & 7 H U 00 8 J I FO K O O 1 - P { I ( am 11 O s 1 1 ✔ delete
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 O and total cost data for a manufacturing operation. O Production Volume (units) Total Cost ($) 400 4100 450 4600 500 5600 600 6400 700 7400 750 8400 N The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = -524.58+11.66z. a. The company's production schedule shows that 550 units must be produced next month. Predict the total cost for next month. ŷ* = (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. X (to 2 decimals) 8 t-value (to 3 decimals) 8pred (to 2 decimals) Prediction Interval for an individual Value next month ) (to whole number) A 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. outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause Based on one month, $6,000 is not concern. DD F10 FB A 888 F7 80 FA FA ! 1 Q 2 W 2 # 3 E C $ 4 R LL F % y 5 T G A 6 Y & 7 H U 00 8 J I FO K O O 1 - P { I ( am 11 O s 1 1 ✔ delete
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