Question

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?

Answer 1

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