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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)? $

The following are budgeted data: january february march sales in units 16,200 22,400 19,200 production in units 19,200 20,200 18,700 one pound of material is required for each finished unit. the inventory of materials at the end of each month should equal 20% of the following month's production needs. purchases of raw materials for february would be budgeted to be:

Why is it easier for new entrants to get involved in radical innovations when compared to incumbent firms? a. unlike incumbent firms, new entrants do not have to face the high entry barriers, initially. b. new entrants are embedded in an innovation ecosystem, while incumbent firms are not. c. unlike incumbent firms, new entrants do not have formal organizational structures and processes. d. incumbent firms do not have the advantages of network effects that new entrants have.