F Test Statistic. In general an f statistic is a ratio of two quantities that are expected to be roughly equal under the null hypothesis which produces an f statistic of approximately 1. 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.
The f statistic is the test statistic for f tests. Fisher initially developed t. In statistics an f test of equality of variances is a test for the null hypothesis that two normal populations have the same variance.
The f statistic is the test statistic for f tests.
The f statistic incorporates both measures of variability discussed above. An f statistic is a value you get when you run an anova test or a regression analysis to find out if the means between two populations are significantly different. A statistical f test uses an f statistic to compare two variances s 1 and s 2 by dividing them. The f test of overall significance in regression is a test of whether or not your linear regression model provides a better fit to a dataset than a model with no predictor variables.
