Regression Statistics | ||||||
Multiple R | 0.3641 | |||||
R Square | 0.1325 | |||||
Adjusted R Square | 0.1176 | |||||
Standard Error | 0.0834 | |||||
Observations | 60 | |||||
ANOVA | ||||||
df | SS | MS | F | Significance F | ||
Regression | 1 | 0.0617 | 0.0617 | 8.8622 | 0.0042 | |
Residual | 58 | 0.4038 | 0.0070 | |||
Total | 59 | 0.4655 | ||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | -0.0144 | 0.0110 | -1.3062 | 0.1966 | -0.0364 | 0.0077 |
X Variable 1 | 0.8554 | 0.2874 | 2.9769 | 0.0042 | 0.2802 | 1.4307 |
How do you interpret the above table?
from the given O/p,
intercept = -0.0144
slope = 0.8554
so, regression equation is
Y^ = -0.0144 + 0.8554*x
for every unit increase in value of x, predicted value of y get increase by 0.8554
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correlation coefficient = 0.3641
this indicates a weak positive correlation between x and y
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R² = 0.1325
this indicates about 13.25% of variation in observation of Y is explained by variable x
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F stat = 8.86
p value=0.042
So, this overall model of regression is significant
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