Using Excel, go to Data, Select Data Analysis, choose
Regression. Put Age in X input range and Credit score in Y input
range. Put confidence level = 90
| SUMMARY OUTPUT |
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| Regression Statistics |
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| Multiple R |
0.0627 |
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| R Square |
0.0039 |
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| Adjusted R Square |
-0.0727 |
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| Standard Error |
93.1960 |
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| Observations |
15 |
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| ANOVA |
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df |
SS |
MS |
F |
Significance F |
| Regression |
1 |
445.5950 |
445.5950 |
0.0513 |
0.8243 |
| Residual |
13 |
112911.3384 |
8685.4876 |
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| Total |
14 |
113356.9333 |
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Coefficients |
Standard Error |
t Stat |
P-value |
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| Intercept |
679.8155 |
75.6761 |
8.9832 |
0.0000 |
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| Age |
0.2854 |
1.2602 |
0.2265 |
0.8243 |
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Slope estimate = 0.29
p-value (Significance F) = 0.8243
H0: Age and credit score are not linearly related
H1: Age and credit score are linearly related
p-value = 0.8243
Since p-value is more than 0.10, we do not reject the null
hypothesis.
The data does not support the claim that age and credit score
are linearly related.
