F-test


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F-test

Etymology: Sir Ronald Aylmer Fisher, British statistician, 1890-1962
a statistical test comparing the means of more than two groups simultaneously by comparing two different measures of variance of the observations. One statistic measures the variations between the means of the groups (the between-groups variation), the other the variations within the groups (the within-group variation). If the two measures of variance yield similar results and their ratio, the F-ratio, approximates 1.0, the null hypothesis that all observations came from the same population cannot be rejected, whereas under the alternative hypothesis, the F-ratio is expected to be larger than 1.0. The test is the first step in the analysis of variance.
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The effect of non-normality on F-test robustness has, since the 1930s, been extensively studied under a wide variety of conditions.
An F-test rejects equality of mean scores across the anxiety classifications.
The results of the independent samples f-test performed for OCB indicate furloughs have a statistically significant effect on the OCB of federal government employees.
Between the parametric tests include primarily Student t-test for testing difference between the two mean values and F-test (Fisher test) for testing the difference of two variances.
In the ARDL model, the long-run relationship among variables is carried out on the bases of calculating partial F-test on the first differenced portion of Unrestricted Error Correction Model (UECM) of the above mentioned equation.
Moreover, a dependent sample f-test was used for comparison of workload at each physiological parameter.
An F-test was used to establish the significance of these relations, the null hypothesis being that the distribution of student characteristics was similar along the different categories of a determined teacher attribute.
In the case of the F-test, 5 among 100 solutions were wrong and they were correctly identified by the validation procedure.