F-test refers to any statistical test in which the test statistic has an F-distribution under the null hypothesis. In short, f-test is the ratio of two mean squares. The F Test is based on the F Distribution. F-test is mostly used for comparing the statistical models that have been fir to a data set to identify the model that best fits the population from which the data were sampled.
An F test is normally a test for the joint hypothesis that a number of coefficients are zero. The Examples of F-Test includes the following:
- The hypothesis that the means of multiple normally distributed populations, all having the same standard deviation, are equal. This is perhaps the most well-known of hypotheses tested by means of an F-test, and the simplest problem in the analysis of variance (ANOVA).
- The hypothesis that the standard deviations of two normally distributed populations are equal, and thus that they are of comparable origin.
Example:
Enter first set of values (Eg:2,3,4,5) = 2,4,1,3
Enter second set of values (Eg:4,5,6,7) = 1,4,2,3
Solution:
Apply formula:
F-VALUE=S1^{2}/S2^{2
}F-value = 1