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 costassociated 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 ($)450400050050006005900650670070072008008100The data on the production volume x and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = -954.00 + 11.52x.a. The company's production schedule shows that 750 units must be produced next month. Predict the total cost for next month.ŷ* =(to 2 decimals)b. Develop a 95% prediction interval for the total cost for next month.(to 2 decimals)t-value(to 3 decimals)Spred(to 2 decimals)Prediction Interval for an individual Value next month) (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, $6,000Select your answer - voutside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause concern.- Select your answer-is not

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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 and total cost data for a manufacturing operation. Production Volume (units) 400 450 550 600 700 750 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 by and be (to 1 decimal). bo Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations Total Cost (5) 4,000 5,000 5,400 5,900 6,400 7,000 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…
An important application of regression analysis 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, one can estimate the cost associated with a particular manufacturing volume. Consider the following sample of monthly production volumes and total costs data for a manufacturing operation for the year 2018. Month (2018) Volume of Production (Units) Total Costs ($) Jan 500 6000 Feb 350 4000 Mar 450 5000 Apr 550 5400 May 600 5900 Jun 400 4000 Jul 400 4200 Aug 350 3900 Sept 400 4300 Oct 600 6000 Nov 700 6400 Dec 750 7000 1) Calculate the (Pearson) correlation coefficient between production volume and total costs in this problem. 2) What is the most suitable interpretation of the (Pearson) correlation coefficient between production volume and total costs in this problem? 3) The equation of the regression line is given by...? 4) How…
An important application of regression analysis is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, one can estimate the cost associated with a particular manufacturing volume. Consider the following sample of monthly production volumes and total costs data for a manufacturing operation for the year 2018. Month (2018) Volume of Production (Units) Total Costs ($) Jan 500 6000 Feb 350 4000 Mar 450 5000 Apr 550 5400 May 600 5900 Jun 400 4000 Jul 400 4200 Aug 350 3900 Sept 400 4300 Oct 600 6000 Nov 700 6400 Dec 750 7000 1) Which of the following is NOT necessarily true about the interpretation of the value of b in the simple linear regression equation y = a + bx for this problem? 2) Which of the following statements are true about the dependent (or response) and independent (or predictor) variables for the simple linear model in…
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 ($) 400 4000 500 5000 550 5900 600 6500 700 7500 800 7900 The data on the production volume x and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = 95.92 + 10.20x. a. The company's production schedule shows that 750 units must be produced next month. Predict the total cost for next month. ŷ* : 7745.9. (to decimals) 2 b. Develop a 99% prediction interval for the total cost for next month. 0.01 (to 2 decimals) t-value 4.604 (to decimals) 3 Spred |(to 2 decimals) Prediction…
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Total Cost ($) 3900 4700 5300 5700 6400 7100 The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation =-490.00 + 10.60x, a. The company's production schedule shows that 450 units must be produced next month. Predict the total cost for next month. ŷ* = * (to 2 decimals) b. Develop a 99% prediction interval for the total cost for next month. (to 2 decimals) 8 t- value decimals) (to 3 * (to 2 Spred decimals) Prediction Interval for an individual Value next month Ⓡ Production Volume…
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 ($) 400 450 550 600 700 750 3,900 4,900 5,300 5,800 6,300 6,900 a. Compute b1 and bo (to 1 decimal). 1 0 Complete the estimated regression equation (to 1 decimal). b. What is the variable cost per unit produced (to 1 decimal)? C. Compute the coefficient of determination (to 3 decimals). Note: report r between 0 and 1 What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? 0 d. The company's production schedule shows 500 units must be produced next month. What is the estimated total…
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,700 450 5,700 550 6,100 600 6,600 700 7,100 750 7,700 Compute b1 and b0 (to 1 decimal)b1 b0 Complete the estimated regression equation (to 1 decimal). = + x What is the variable cost per unit produced (to 1 decimal)?$ Compute the coefficient of determination (to 3 decimals). Note: report r2 between 0 and 1.r2 = What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? % The company's production schedule shows 500 units must be produced next month. What is…
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 450 5000 550 5400 600 5900 700 6400 750 7000 Use these data to develop an estimated regression Equation that could be used to predict the total cost for a given production volume. What is the variable Cost per unit produced? Compute the coefficient of determination. What percentage of the variation in total cost can be explained by production volume? The company's production schedule shows 500 untis must be produced next month. Predict the total cost for this operation. Where data on production…

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