An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost andusing the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate thecost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for amanufacturing operation.Production VolumeTotal Cost(units)($)4004,1004505,0005505,4006006,0007006,5007507,100(a) Use these datavolume. (Round your numerical values to two decimal places.)develop an estimated regression equation that could be used to predict the total cost for a given production(b) What is the variable cost (in dollars) per unit produced?24(c) Compute the coefficient of determination. (Round your answer to three decimal places.)What percentage of the variation in total cost can be explained by production volume? (Round your answer to one decimal place.)(d) The company's production schedule shows 650 units must be produced next month. Predict the total cost (in dollars) for thisoperation. (Round your answer to the nearest cent.)2$

Ah Importaht application of régression 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 Total Cost (units) ($) 400 4,100 450 5,000 550 5,400 600 6,000 700 6,500 750 7,100 (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.) (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 plac

Ah Importaht application of régression 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 Total Cost (units) ($) 400 4,100 450 5,000 550 5,400 600 6,000 700 6,500 750 7,100 (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.) (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 plac

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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Directions: Construct a Regression Model for the following data. Year Wage (in dollars) 2000 5.15 2005 5.15 2010 7.25 2016 7.25 U.S. Federal Minimum Wage 1. State your regression model (least-squares line). Make sure you first convert the Year into Years after 2000. y = 2. Use your model to predict the minimum wage for the year 2022. y = dollars 3. Use your model to predict when the minimum wage will reach $10. When During year Rounding: If you do round the numbers in your work, make sure you do it during your last step.
Report the equation of the regression line and interpret it in the context of the problem
Production Volume (units) Total Cost ($) 100 1730 200 2792 300 4129 400 5385 500 6497 600 7764 700 8835 800 10180 900 11412 Which of the following is your estimated regression equation? Total Cost = 468 + 12.1(Production Volume) + e Production Volume = 468 + 12.1(Total Cost) + e Total Cost = 468(Production Volume) + 12.1 + e Production Volume = 468(Total Cost) + 12.1 + e Will you reject the null hypothesis that the slope is zero at a significance level of 1%? Yes No Not enough information What is the R-square from your regression analysis? (Round your answer to four decimal places.) Which of the following is the correct interpretation of the R-square? Production volume explains 99.96% of the variation in total cost. Total cost explains 99.96% of the variation in production volume. The dependent variable explains 99.96% of the variation in the independent variable. 96% of the data points are…
Size Energy Expenditure 250 1153 750 1256 1250 1696 1750 1830 2250 2016 2750 2150 3250 2217 3750 2534 The percentage of variation in average annual energy expenditures that is explained by the least-squares regression line 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 O and total cost data for a manufacturing operation. O Production Volume (units) Total Cost ($) 400 4100 450 4600 500 5600 600 6400 700 7400 750 8400 N The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = -524.58+11.66z. a. The company's production schedule shows that 550 units must be produced next month. Predict the total cost for next month. ŷ* = (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. X (to 2 decimals) 8 t-value (to 3 decimals) 8pred (to 2 decimals) Prediction Interval for…
An article gave a scatter plot along with the least squares line of x = rainfall volume (m3) and y = runoff volume (m3) for a particular location. The accompanying values were read from the plot. x 6 12 14 20 23 30 40 50 55 67 72 79 96 112 127 y 4 10 13 15 15 25 27 46 38 46 53 74 82 99 104 (a) Does a scatter plot of the data support the use of the simple linear regression model? Yes, the scatterplot shows a reasonable linear relationship.Yes, the scatterplot shows a random scattering with no pattern. No, the scatterplot shows a reasonable linear relationship.No, the scatterplot shows a random scattering with no pattern. (b) Calculate point estimates of the slope and intercept of the population regression line. (Round your answers to four decimal places.) slope intercept (c) Calculate a point estimate of the true average runoff volume when rainfall volume is 50. (Round your answer to four decimal places.) m3(d) Calculate a point estimate of the…
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Total Cost ($) 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…
The table contains price-supply data and price-demand data for corn. Find a linear regression model for the price-supply data where x is supply (in billions of bushels) and y is price (in dollars). Do the same for the price-demand data Find the equilibrium price for corn. Price Supply ($/bu) (billion bu) 2.14 2.24 2.38 2.43 2.45 2.54 6.29 7.33 7.69 7.83 8.12 8.23 Price ($/bu) 2.02 2.14 2.24 2.35 2.35 2.42 Demand (billion bu) 9.76 9.47 8.48 8.18 7.72 6.93 XX Find a linear regression model for the price-supply data where x is supply (in billions of bushels) and y is price (in dollars). y=0 (Type an equation using x as the variable. Round to two decimal places as needed.)