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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 particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 3,700 450 4,700 550 5,100 600 5,600 700 6,100 750 6,700 a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations. Compute bị and bo (to 1 decimal). Do not round intermediate calculations. b1 bo Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations. = b. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations. c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: report r² between 0 and 1. What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? Do not round intermediate calculations. d. The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)? Do not round intermediate calculations.

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 particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 3,700 450 4,700 550 5,100 600 5,600 700 6,100 750 6,700 a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations. Compute bị and bo (to 1 decimal). Do not round intermediate calculations. b1 bo Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations. = b. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations. c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: report r² between 0 and 1. What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? Do not round intermediate calculations. d. The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)? Do not round intermediate calculations.

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 3,700 450 4,700 550 5,100 600 5,600 700 6,100 750 6,700 a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations. Compute bị and bo (to 1 decimal). Do not round intermediate calculations. b1 bo Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations. ŷ = + b. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations. $ c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: report r2 between 0 and 1. p2 What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? Do not round intermediate calculations. d. The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)? Do not round intermediate calculations. $
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 3,700 450 4,700 550 5,100 600 5,600 700 6,100 750 6,700 a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations. Compute bị and bo (to 1 decimal). Do not round intermediate calculations. b1 bo Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations. ŷ = + b. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations. $ c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: report r2 between 0 and 1. p2 What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? Do not round intermediate calculations. d. The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)? Do not round intermediate calculations. $
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