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A F-test uses F-distribution. This test is used only when the sample size is smaller than 30. This is a flexible test and lets you evaluate multiple model terms and compare the fits of different linear models. Assumptions that need to be considered while conducting this test are: Populations from which the sample is drawn should be normal. The larger of the sample variance needs to be placed in the numerator of the test statistic.

F-test is not very popular as it is not widely used by the scholars. To know more about this test, visit https://www.itl.nist.gov/div898/handbook/eda/section3/eda359.htm.

Although it is easy to conduct the test, it is difficult to interpret its significance. You have to compare the p-value for the F-test to the significance level. If the p-value is less than the significance level, then the sample data provide sufficient evidence to conclude that the model under consideration fits the data better than the model with no independent variables. But if none of the independent variables are significant then the overall test is not statistically significant.

Thank you so much for the answers.

https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/hypothesis-testing/f-test/. This site has general steps to be followed while conducting F-test. But it’s better if you talk to your supervisor and ask him/her about the exact steps that need to be followed.