F-Test

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An F-Test is an Omnibus statistical test that uses an F-statistic (with an F-distribution).



References

2016

Common examples of F-tests
Common examples of the use of F-tests include the study of the following cases:

In addition, some statistical procedures, such as Scheffé's method for multiple comparisons adjustment in linear models, also use F-tests.


  • (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Omnibus_test Retrieved 2016-08-28
    • Omnibus tests are a kind of statistical test. They test whether the explained variance in a set of data is significantly greater than the unexplained variance, overall. One example is the F-test in the analysis of variance. There can be legitimate significant effects within a model even if the omnibus test is not significant. For instance, in a model with two independent variables, if only one variable exerts a significant effect on the dependent variable and the other does not, then the omnibus test may be non-significant. This fact does not affect the conclusions that may be drawn from the one significant variable. In order to test effects within an omnibus test, researchers often use contrasts.
In addition, Omnibus test as a general name refers to an overall or a global test. Other names include F-test or Chi-squared test.
Omnibus test as a statistical test is implemented on an overall hypothesis that tends to find general significance between parameters' variance, while examining parameters of the same type [...]
Omnibus tests commonly refers to either one of those statistical tests:
  • ANOVA F test to test significance between all factor means and/or between their variances equality in Analysis of Variance procedure ;
  • The omnibus multivariate F Test in ANOVA with repeated measures ;
  • F test for equality/inequality of the regression coefficients in Multiple Regression;
  • Chi-Square test for exploring significance differences between blocks of independent explanatory variables or their coefficients in a logistic regression.