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Suppose you run a regression and the Serial Correlation LM Test, has a p-value of 0.0000. How would you interpret the results using an F-test?

  1. Suppose you run a regression and the Serial Correlation LM Test, has a p-value of 0.0000. How would you interpret the results using an F-test?
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Answer #1

An F-test is a type of statistical test that is very flexible. You can use them in a wide variety of settings. F-tests can evaluate multiple model terms simultaneously, which allows them to compare the fits of different linear models. The overall F-test compares the model that you specify to the model with no independent variables.

F-test has two significance hypothesis:-

a- Null hypothesis:- Model with no independent variable.

b- Alternate hypothesis:- Model fits the data better than the intercept-only model

p-value of 0.0000

Interpreting the F-test:-

Compare the p-value for the F-test to your significance level. If the p-value is less than the significance level, your sample data provide sufficient evidence to conclude that your regression model fits the data better than the model with no independent variables. It means that the independent variables in your model improve the fit.

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