Question

Grawburg Inc. maintains a call center to take orders, answer questions, and handle complaints. The costs of the call center for a number of recent months are listed below:

Calls Taken Call Center Cost : April 7560 $ 81,960 May 7529 $ 81,773 June 7570 $ 82,025 July 7568 $ 81,997 August 7535 $ 81,816 September 7549 $ 81,895 October 7592 $ 82,156 November 7579 $ 82,092

Management believes that the cost of the call center is a mixed cost that depends on the number of calls taken. The excel least squares regression output is as follows:

SUMMARY OUTPUT Regression Statistics MultipleR R Square Adjusted R Square Standard Error Observations 0.998198 0.996399 0.995799 8.52932 ANOVA MS nificance F Regression Residual Total 1 120783120783 1660.264 1.46E-08 6 436.4958 72.7493 7 121219.5 coefficientandard Err t Stat P-value Lower 95%Upper 95%ower 95.09 pper 95.0% 35914.19 1130.169 31.77772 6.45E-08 33148.76 38679.61 33148.76 38679.61 6.091077 0.149488 40.74633 1.46E-08 5.725293 6.45686 5.725293 6.45686 X Variable 1

Using the output above identify the following:

1. What is the “measure of goodness of fit”? (round to 2 decimal place)

2. What is the variable cost per unit based on the regression equation? (round to 2 decimal place) $

3. What is the fixed cost portion of Inspection Costs? (round to 2 decimal place) $

4. Cost equation for Inspection Costs (in the linear equation format) Y= Estimated Inspection costs when 973 units are produced (round to nearest whole dollar) $

Answer 1

1) Measure of goodness of fit1.00 [R Square value] The excel least squares regression output is as follows SUMMARY OUTPUT In the regression analysis, R-Squae value is also known as Coefficient of determination. It explains how closely the given data fits to the regression equation line drawn on the graph. In general, R-Square value of 1.0 implies that the given data exactly fits the regression equation line drawn on the graph;Any R-Square value of less than 1.0 implies that the given data do not fit the regression line of the graph. ression Statistics R Square Adjusted R Square Standard Error Observations 0.998198 0.996399 0.995799 8.52932 In the given regression values, R-Square value 0.996399 which is equal to 1.00. Therefore, it implies that the data used to draw the regression outputs fits ther ANOVA ssion line MS nificance F Regression Residual Total 1 120783 120783 1660.2641.46E-08 6 436.4958 72.7493 7 121219.5 2) Variable cost per unit X Variable 1's Coefficient 6.09 3) Fixed Cost Intercept's Coefficient35914.19 Coe ndard Err t Stat P-value Lower 95%U pper 95%over 95.02 pper 95.0% From the regression results given, Cost equation is: Total Cost : Intercept's Coefficient + (X Variable 1's coefficient x # of Units) ) Total Cost 35914.19+ (6.09 x Number of Units produced) 914.19 130.169 31.77772 6.45E-08 33148.76 38679.61 33148.76 38679.61 6.091077 0.149488 40.74633 1.46E-08 5.725293 6.45686 5.725293 6.45686 Variable 1 5) Total Cost 35914.19(6.09 x973)-Total Cost $41,840