# F kenőcs test

Julyy 1, 2020 An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis.It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled. Exact "F-tests" mainly arise when the models have been fitted to the data using least squares.

The overall F-test determines whether this relationship is statistically significant. If the P value for the overall F-test is less than your significance level, you can conclude that the R-squared value is significantly different from zero. To see how the F-test works using concepts and graphs, see my post about understanding the F-test.

This example teaches you how to perform an F-Test in Excel. The F-Test is used to test the null hypothesis that the variances of two populations are equal. Below you can find the study hours of 6 female students and 5 male students. H 0: σ 1 2 = σ 2 2 H 1: σ 1 2 ≠ σ 2 2

The test statistic F test for equal variances is simply: F = Var(X) / Var(Y) Where F is distributed as df1 = len(X) - 1, df2 = len(Y) - 1. scipy.stats.f which you mentioned in your question has a CDF method. This means you can generate a p-value for the given statistic and test whether that p-value is greater than your chosen alpha level.

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# F-test res.ftest - var.test(len ~ supp, data = my_data) res.ftest F test to compare two variances data: len by supp F = 0.6386, num df = 29, denom df = 29, p-value = 0.2331 alternative hypothesis: true ratio of variances is not equal to 1 95 percent confidence interval: 0.3039488 1.3416857 sample estimates: ratio of variances 0.6385951

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Table of F-statistics P=0.05 t-statistics F-statistics with other P-values: P=0.01 | P=0.001 Chi-square statistics

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F kenőcs test