It is important to remember that the advertising should be part of an integrated approach and is the component of that approach.

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)? $

On january 2, well co. purchased 10% of rea, inc.’s outstanding common shares for $400,000, which equaled the carrying amount and the fair value of the interest purchased in rea’s net assets. well did not elect the fair value option. because well is the largest single shareholder in rea, and well’s officers are a majority on rea’s board of directors, well exercises significant influence over rea. rea reported net income of $500,000 for the year and paid dividends of $150,000. in its december 31 balance sheet, what amount should well report as investment in rea?

Total marketing effort is a term used to describe the critical decision factors that affect demand: price, advertising, distribution, and product quality. define the variable x to represent total marketing effort. a typical model that is used to predict demand as a function of total marketing effort is based on the power function: d = axb suppose that a is a positive number. different model forms result from varying the constant b. sketch the graphs of this model for b = 0, b = 1, 0< b< 1, b< 0, and b> 1. (we encourage you to use excel to do this.) what does each model tell you about the relationship between demand and marketing effort? what assumptions are implied? are they reasonable? how would you go about selecting the appropriate model?