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.

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 ($) 450 4000 550 4900 650 5400 700 6300 750 7200 800 7700 The data on the production volume & and total cost y for particular manufacturing operation were used to develop the estimated regression equation ? = -965.69 + 10.59:. a. The company's production schedule shows that 500 units must be produced next month. Predict the total cost for next month. 4,329.:0 (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. 323.62 (to 2 decimals) t-value 3.75 (to 3 decimals) Spred 4,328.4 (to 2 decimals) Prediction Interval for an individual Value next month 1,451 5,779 ) (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, $6,000 |is not outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause concern