using excel>data>data analysis>Regression
we have
SUMMARY OUTPUT |
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Regression Statistics |
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Multiple R |
0.858112 |
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R Square |
0.736356 |
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Adjusted R Square |
0.709992 |
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Standard Error |
5.705367 |
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Observations |
12 |
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ANOVA |
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df |
SS |
MS |
F |
Significance F |
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Regression |
1 |
909.1545 |
909.1545 |
27.92997 |
0.000355 |
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Residual |
10 |
325.5122 |
32.55122 |
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Total |
11 |
1234.667 |
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Intercept |
2.5216 |
14.25129 |
0.176938 |
0.863089 |
-29.2323 |
34.27546 |
Systolic |
0.622566 |
0.117801 |
5.284881 |
0.000355 |
0.360089 |
0.885044 |
Hypotheses:
H0: Slope and Correlation are both zero
H1: Slope and Correlation are both not zero
Results:
the correlation coefficient
r =0.8581
the variation of absences are explained by the model
R2=73.64
What is the equation for the regression line? Use 2 decimal places
in answers.
Diastolic = 0.62(Systolic) +2.52
State the p-value. Round answer to nearest hundredth percent (i.e.
2.55%).
p-value =0.00
Conclusion:
We have sufficient evidence to support the claim that the
correlation coefficient and slope of the regression line are both
statistically different than zero