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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 4000 450 5000 550 5400 600 5900 700 6400 750 7000 compute b 1 and b 0 (to 2 decimals if necessary). b 1 b 0 complete the estimated regression equation (to 2 decimals if necessary). = + x what is the variable cost per unit produced (to 1 decimal)? $ compute the coefficient of determination (to 4 decimals). note: report r 2 between 0 and 1. r 2 = what percentage of the variation in total cost can be explained by the production volume (to 2 decimals)? % the company's production schedule shows 500 units must be produced next month. what is the estimated total cost for this operation (to 2 decimals)? $

En major recording acts are able to play at the stadium. if the average profit margin for a concert is $175,000, how much would the stadium clear for all of these events combined?

Until recently, hamburgers at the city sports arena cost $4.70 each. the food concessionaire sold an average of 13 comma 000 hamburgers on game night. when the price was raised to $5.40, hamburger sales dropped off to an average of 6 comma 000 per night. (a) assuming a linear demand curve, find the price of a hamburger that will maximize the nightly hamburger revenue. (b) if the concessionaire had fixed costs of $1 comma 500 per night and the variable cost is $0.60 per hamburger, find the price of a hamburger that will maximize the nightly hamburger profit.