Burmer Co. has accumulated data to use in preparing its annual profit plan for the upcoming year. The cost behavior pattern of the maintenance costs must be determined. Data regarding the machine hours and maintenance costs for the last year and the results of the regression analysis are as follows: Maintenance Machine Month Hours 620 420 520 390 650 400 430 680 640 610 460 440 Cost $ 5,040 Jan. Feb. Mar. Apr. May June July Aug. Sept- oct. Nov. Dec 3,600 4,320 3,380 5,220 3,550 3,640 5,360 5,110 4,860 ,960 3,790 Sum $51,8306,260 Average 4,319 522 A staff assistant has run regression analyses on the data and obtained the foillowing output using Excel: REGRESSION ANALYSIS Y (Dependent) Variable: Maintenance Cost X (Independent) Variable: Maintenance Hours Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA 0.998210294 0.996423791 0.99606617 7.0629563 12 dE MS F Significance F Regression Residual Total 6171342.448 6171342 2786.257 1.44166E-13 22149.21856 2214.922 193491-667 10 Coefficients Standard Error stat P-value Intercept Hours 783.7782188 6.777102456 Lower 95 Upper 95 631.504653 936.051785 6.491030239 7.06317467 68.34114772 11.46861 4.47E-07 0.12839066 52.78501 1.44E-13 68.34114772 11.46861 4.47E- Intercept Hours 783.7782188 6.777102456 0.12839066 52.78501 1.44E-1 23 The statistic that indicates precision of the regression is: Significance F (1.44E-13) 0.9981 470630. 9982 Lower 95% and upper 95%