using excel>data>data analysis>regression
we have
Ans 1 )
SUMMARY OUTPUT |
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Regression Statistics |
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Multiple R |
0.79588 |
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R Square |
0.633425 |
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Adjusted R Square |
0.56011 |
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Standard Error |
1389.213 |
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Observations |
7 |
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ANOVA |
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df |
SS |
MS |
F |
Significance F |
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Regression |
1 |
16674011 |
16674011 |
8.639778 |
0.032281 |
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Residual |
5 |
9649560 |
1929912 |
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Total |
6 |
26323571 |
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Lower 95.0% |
Upper 95.0% |
Intercept |
14368.13 |
3828.227 |
3.753208 |
0.01325 |
4527.363 |
24208.9 |
4527.363 |
24208.9 |
number of lab test |
3.165236 |
1.076849 |
2.93935 |
0.032281 |
0.397108 |
5.933365 |
0.397108 |
5.933365 |
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Ans 2 ) the​ lab's cost equation is y = 14368.13+3.17 * number
of lab test
Ans 3 ) the​ R-square 0.6334 it indicates that 63.34 % variation
in Total Laboratory overhead explained by number of lab test
Ans 4 )the​ lab's total overhead costs for the month if 3,400
tests are preformed is 14368.13+3.17 *3400 = 25146.13