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:
Month | Maintenance Cost | Machine Hours | ||||
Jan. | $ | 5,040 | 620 | |||
Feb. | 3,600 | 420 | ||||
Mar. | 4,320 | 520 | ||||
Apr. | 3,380 | 390 | ||||
May | 5,220 | 650 | ||||
June | 3,550 | 400 | ||||
July | 3,640 | 430 | ||||
Aug. | 5,360 | 680 | ||||
Sept. | 5,110 | 640 | ||||
Oct. | 4,860 | 610 | ||||
Nov. | 3,960 | 460 | ||||
Dec. | 3,790 | 440 | ||||
Sum | $ | 51,830 | 6,260 | |||
Average | $ | 4,319 | 522 | |||
A staff assistant has run regression analyses on the data and
obtained the following output using Excel:
REGRESSION ANALYSIS
Y (Dependent) Variable: Maintenance Cost
X (Independent) Variable: Maintenance Hours
Regression Statistics | |||||||||||
Multiple R | 0.998210294 | ||||||||||
R Square | 0.996423791 | ||||||||||
Adjusted R Square | 0.99606617 | ||||||||||
Standard Error | 47.0629563 | ||||||||||
Observations | 12 | ||||||||||
ANOVA | |||||||||||
df | SS | MS | F | Significance F | |||||||
Regression | 1 | 6171342.448 | 6171342 | 2786.257 | 1.44166E-13 | ||||||
Residual | 10 | 22149.21856 | 2214.922 | ||||||||
Total | 11 | 6193491.667 | |||||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | ||||||
Intercept | 783.7782188 | 68.34114772 | 11.46861 | 4.47E-07 | 631.504653 | 936.051785 | |||||
Hours | 6.777102456 | 0.12839066 | 52.78501 | 1.44E-13 | 6.491030239 | 7.06317467 | |||||
The statistic that indicates precision of the regression is:
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