X | Y | ||||||||||
Machine Hour | Costs | 1 | |||||||||
6500 | $35,800 | ||||||||||
9200 | $42,900 | ||||||||||
12700 | $45,400 | ||||||||||
14600 | $51,500 | ||||||||||
17600 | $59,900 | ||||||||||
Click "Data" | |||||||||||
Click "Data Analysis" | |||||||||||
Select "Regression" tool | |||||||||||
Click"OK" | |||||||||||
Input y_Range (Costs) | |||||||||||
Input x_Range (Machine hour)) | |||||||||||
Click "OK" | |||||||||||
Cost (Y)=Intercept(a)+X Variable(b)*Machine Hour(X) | |||||||||||
Intercept | 22451.30 | ||||||||||
X variable | 2.03 | ||||||||||
Y=22451.30+2.03X | |||||||||||
Fixed Cost per month | $22,451.30 | ||||||||||
Variable Cost per machine hour | $2.03 | ||||||||||
R square | 0.956294539 | ||||||||||
R square indicates coefficient of determination | |||||||||||
It indicates 95.6% of observations can be explained by the above regression equation | |||||||||||
4 | For Machine hour =18750 | ||||||||||
X=18750 | |||||||||||
Y=22451.30+2.03*18750= | $60,583.57 | ||||||||||
COSTS=$60,583.57 | |||||||||||
SUMMARY OUTPUT | |||||||||||
Regression Statistics | |||||||||||
Multiple R | 0.977903134 | ||||||||||
R Square | 0.956294539 | ||||||||||
Adjusted R Square | 0.941726051 | ||||||||||
Standard Error | 2197.336571 | ||||||||||
Observations | 5 | ||||||||||
ANOVA | |||||||||||
df | SS | MS | F | Significance F | |||||||
Regression | 1 | 316935136 | 316935136 | 65.64130714 | 0.003929933 | ||||||
Residual | 3 | 14484864.02 | 4828288.006 | ||||||||
Total | 4 | 331420000 | |||||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | ||||
Intercept | 22451.29718 | 3197.091289 | 7.022413547 | 0.005931745 | 12276.72582 | 32625.87 | 12276.73 | 32625.87 | |||
X Variable 1 | 2.033721355 | 0.251016829 | 8.101932309 | 0.003929933 | 1.234873776 | 2.832569 | 1.234874 | 2.832569 | |||
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