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. Excel file: data14-21.xls Production Volume (units) Total Cost (S) 400 450 4000 5000 5400 550 600 5900 6400 700 750 7000 . Compute bị and bo (to 2 decimals if necessary). b1 bo Complete the estimated regression equation (to 2 decimals if necessary).

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)
14th Edition
ISBN:9781305506381
Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris
Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris
Chapter4A: Problems In Applying The Linear Regression Model
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{Exercise 14.21}
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.
Excel file: data14-21.xls
Production Volume (units)
Total Cost (S)
400
450
4000
500
5400
550
600
700
5900
6400
750
7000
a. Compute bi and bo (to 2 decimals if necessary).
b1
bo
Complete the estimated regression equation (to 2 decimals if necessary).
y =
Transcribed Image Text:eBook {Exercise 14.21} 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. Excel file: data14-21.xls Production Volume (units) Total Cost (S) 400 450 4000 500 5400 550 600 700 5900 6400 750 7000 a. Compute bi and bo (to 2 decimals if necessary). b1 bo Complete the estimated regression equation (to 2 decimals if necessary). y =
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