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 4,000 450 5,000 550 5,400 600 5,900 700 6,400 750 7,000 a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations. Compute b and bo (to 1 decimal). b bo Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations g = + x 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 1 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 % 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 between 0 and 1. 12 = 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. Predict the total cost for this operation (to the nearest whole number). Do not round intermediate calculations