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Regression Statistics Multiple R 0.983211253 R Square 0.966704367 Adjusted R Square 0.962542413 Standard Error 234.8326064 Observations...

Regression Statistics
Multiple R 0.983211253
R Square 0.966704367
Adjusted R Square 0.962542413
Standard Error 234.8326064
Observations 10

what can you conclude with this regression?

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Answer #1

The 10 observations in this regression seem to be a good fit since R Square is 0.9667. R Square value lies between 0 and 1, with 1 reflecting perfect fit. This means that the independent variables explain the variation in the dependent variable by approximately 96%. This is why the model is a good fit.

Adjusted R Square: Sometimes R square value is high due to a large number of independent variables. Adjusted R Square corrects the chances of a spurious regression model, adjusting the R Square value for the number of variables. In this model, the Adjusted R square is also high (and close to the R square value), and thus it can be concluded that the model is a good fit.

Multiple R shows the degree of correlation between the variables in the model, which is approx. 98% in this model.

Therefore, the model seems to be a good fit, implying correct specification and significant results.

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