# How do you interpret the F-test for two sample variances in Excel?

## How do you interpret the F-test for two sample variances in Excel?

Performing the Two-Sample Variances Test in Excel

1. In Excel, click Data Analysis on the Data tab.
2. From the Data Analysis popup, choose F-Test Two-Sample for Variances.
3. Under Input, select the ranges for both Variable 1 Range and Variable 2 Range.
4. Check the Labels checkbox if you have meaningful variable names in row 1.

## What does the F statistic tell you?

The F-statistic is simply a ratio of two variances. The term “mean squares” may sound confusing but it is simply an estimate of population variance that accounts for the degrees of freedom (DF) used to calculate that estimate. Despite being a ratio of variances, you can use F-tests in a wide variety of situations.

## What is F value in Excel?

The F statistic or F value is calculated from the data while performing F-test. The F statistic is a ratio of the variances of the two samples. The F statistic is compared with the F critical value to determine whether the null hypothesis may be supported or rejected.

## What is a 2 sample F test?

The F-Test Two-Sample for Variances tool tests the null hypothesis that two samples come from two independent populations having the equal variances. In the example below, two sets of observations have been recorded. In the first sample, students were given a test before lunch and their scores were recorded.

## How do you interpret F statistic in regression?

The F value is the ratio of the mean regression sum of squares divided by the mean error sum of squares. Its value will range from zero to an arbitrarily large number. The value of Prob(F) is the probability that the null hypothesis for the full model is true (i.e., that all of the regression coefficients are zero).

## How do you manually calculate an F value?

State the null hypothesis and the alternate hypothesis. Calculate the F value. The F Value is calculated using the formula F = (SSE1 – SSE2 / m) / SSE2 / n-k, where SSE = residual sum of squares, m = number of restrictions and k = number of independent variables. Find the F Statistic (the critical value for this test).