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.

In the Microsoft Excel Online file below you will find a sample of production volumes and total cost data for a manufacturing operation. Conduct a regression analysis to explore the relationship between total cost and production volume and then answer the questions that follow.

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. In the Microsoft Excel Online file below you will find a sample of production volumes and total cost data for a manufacturing operation. Conduct a regression analysis to explore the relationship between total cost and production volume and then answer the questions that follow. X mu iliili Open spreadsheet a. Compute b, and bo (to 1 decimal). b1 bo Complete the estimated regression equation (to 1 decimal). y = b. According to this model, what is the change in cost (in dollars) for every unit produced (to 1 decimal)? c. Compute the coefficient of determination (to 3 decimals). Note: report / between 0 and 1. 12 = 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 cost for this operation (to the nearest whole number)? $ 5347 Check My Work Reset ProblemA B C D F G H K Production Volume (units) Total Cost ($) SUMMARY OUTPUT 400 4300 450 5300 Regression Statistics 550 5700 Multiple R 0.979127101 600 6200 R Square 0.958689879 700 6700 Adjusted R Square 0.948362349 750 7300 Standard Error 241.5229458 Observations 6 Production Target Est. Cost ($) ANOVA 500 5347 df SS MS F Significance F Regression 5415000 5415000 92.82857143 0.00064897 Residual 233333.3333 58333.33333 Total 5 5648333.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95% Upper 95% Intercept 1546.666667 464.1599341 3.332184777 0.029046438 257.9520927 2835.381241 257.9520927 2835.381241 Production Volume (units) 7.60 0.79 9.63 0.00 5.41 9.79 5.41 9.79 Cost Estimation 8000 7500 7000 6500 6000 5500 5000 4500 4000