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 regressionequation 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 fora manufacturing operation.Production VolumeTotal Cost(units)($)4003,9004504,9005505,5006006,0007006,4007507,000(a) Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. (Round your numerical values to two decimal places.)ý =(b) What is the variable cost (in dollars) per unit produced?$(c) Compute the coefficient of determination. (Round your answer to three decimal places.)What percentage of the variation in total cost can be explained by production volume? (Round your answer to one decimal place.)(d) The company's production schedule shows 500 units must be produced next month. Predict the total cost (in dollars) for this operation. (Round your answer to the nearest cent.)$

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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. 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.…
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An article in Technometrics by S. C. Narula and J. F. Wellington (Prediction. Linear Regression and a Minimum Sum of Relative Errors." Vol. 19. 1977) presents data on the selling price (y) and annual taxes (x) for 24 houses. The taxes include local, school and county taxes. The data are shown in the table below. Calculate the least square estimates of slope and intercept. Sale Price/1000 Taxes/1000 ... 25.9 4.9176 29.5 5.0208 27.9 4.5429 4.5573 5.0597 3.8910 25.9 29.9 29.9 30.9 28.9 35.9 5.0980 5.6039 5.8282 31.5 5.3003 31.0 6.2712 30.9 5.9592 30.0 5.0500 36.9 8.2464 41.9 6.6909 40.5 7.7841 43.9 9.0384 37.5 5.9894 37.9 7.5422 44.5 8.7951 37.9 6.0831 34.9 8.3607 8.1400 26.9 45.8 9.1416 Input answers up to 3 decimal places. Slope 3.324 Intercept 13.320 Blank 1 3.324 Blank 2 13.320
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 ($) 400 3,800 450 4,800 550 5,200 600 5,700 700 6,200 750 6,800 a. Compute b1 and bo (to 1 decimal). b1 bo Complete the estimated regression equation (to 1 decimal). + b. What is the variable cost per unit produced (to 1 decimal)? c. Compute the coefficient of determination (to 3 decimals). Note: report r between 0 and 1. What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? d. The company's production schedule shows 500 units must be produced next month. What is the estimated total…
Report the equation of the regression line and interpret it in the context of the problem
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes O and total cost data for a manufacturing operation. O Production Volume (units) Total Cost ($) 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 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 4,300 450 5,300 550 5,700 600 6,200 700 6,700 750 7,300 Compute b1 and b0 (to 1 decimal). Complete the estimated regression equation (to 1 decimal) y^ = ____ + ____x What is the variable cost per unit produced (to 1 decimal)?
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…
An important application of regression analysis is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, one can estimate the cost associated with a particular manufacturing volume. Consider the following sample of monthly production volumes and total costs data for a manufacturing operation for the year 2018. Month Production Volume (units) Total Costs ($) January 2018 February 2018 500 6,000 350 4,000 March 2018 450 5,000 April 2018 550 5,400 May 2018 600 5,900 June 2018 400 4,000 July 2018 August 2018 September 2018 400 4,200 350 3,900 400 4,300 October 2018 600 6,000 November 2018 700 6,400 December 2018 750 7,000 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? * I. The monthly total costs will increase by $7.6437 for every one-unit increase in the…
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,700 450 5,700 550 6,100 600 6,600 700 7,100 750 7,700 A) Compute b1 and b0 (to 1 decimal)b1 b0 Complete the estimated regression equation (to 1 decimal). = + x B) What is the variable cost per unit produced (to 1 decimal)?$ C) 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)? % D) The company's production schedule shows 500 units must be produced next…
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…

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