Question

# Regression Statistics Multiple R 0.3641 R Square 0.1325 Adjusted R Square 0.1176 Standard Error 0.0834 Observations...

 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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