using excel>data>data analysis>Regression
we have
| SUMMARY OUTPUT |
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| Regression Statistics |
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| Multiple R |
0.88148 |
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| R Square |
0.777006 |
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| Adjusted R Square |
0.702675 |
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| Standard Error |
1.808474 |
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| Observations |
5 |
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| ANOVA |
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df |
SS |
MS |
F |
Significance F |
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| Regression |
1 |
34.18827 |
34.18827 |
10.45328 |
0.0481 |
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| Residual |
3 |
9.811731 |
3.270577 |
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| Total |
4 |
44 |
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Lower 90.0% |
Upper 90.0% |
| Intercept |
4.52551 |
3.639997 |
1.243273 |
0.302075 |
-7.05858 |
16.1096 |
-4.04073 |
13.09175 |
| price in dollars |
0.084001 |
0.025981 |
3.233154 |
0.0481 |
0.001317 |
0.166684 |
0.022858 |
0.145143 |
A. the sum of squared errors (SSE) is 9.812
B. the estimated variance of errors, s2e is 1.808*1.808
=3.269
C. the estimated variance of slope, s2b1 is 0.026*0.026 =
0.000676
D. the 90%confidence interval for the slope
. Lower endpoint: 0.023 Upper endpoint: 0.145
E. the 95%confidence interval for the slope.
. Lower endpoint: 0.001 Upper endpoint: 0.167
