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F-test

The F-test is a statistical method used to compare the variances of two or more groups, primarily in the context of regression analysis and ANOVA (Analysis of Variance). It assesses whether the means of different populations are significantly different from each other by evaluating the ratio of variances. In regression analysis, the F-test helps determine if a more complex model significantly improves the explanation of variance in the dependent variable compared to a simpler model. By analyzing the F-statistic and its corresponding p-value, researchers can make informed decisions about the validity of their models and hypotheses.

The F-Test for Regression Analysis

Towards Data Science

The F-test, when used for regression analysis, lets you compare two competing regression models in their ability to “explain” the variance in the dependent variable.

How Useful is F-test in Linear Regression?

Towards Data Science

The F-test statistic for joint significance of the slope coefficients of a regression is routinely reported in regression outputs, along with other key statistics such as R² and t-ratio values. The…

How to do F-test in R | Compare variances in Rstudio

R-bloggers

The f-test in R is a powerful tool for comparing variances and drawing significant conclusions from your data. Understanding how to perform an F-test can transform your data analysis capabilities, all...

Comparison of F-test and mutual information

Scikit-learn Examples

Comparison of F-test and mutual information This example illustrates the differences between univariate F-test statistics and mutual information. We consider 3 features x_1, x_2, x_3 distributed unifo...

Uncovered Interest Rate Parity and F-test on Regression Parameters using R

R-bloggers

This post explains how to perform the F-test of joint parameter restrictions on a linear regression model. As an example, we use the data in Chen and Tsang (2013), who introduce so called relative Nel...

Test Fixtures and a Decorator for Explicit Waits

Test-Driven Web Development with Python

Now that we have a functional authentication system, we want to use it to identify users, and be able to show them all the lists they have created. To do that, we’re going to have to write FTs that ha...

Non-functional testing

Software Architecture with C plus plus

What we've already covered are so-called functional tests. Their aim is to check whether the system under test fulfills the functional requirements. But there are also other types of requirements besi...

The Most Important Statistical Test

Towards Data Science

The likelihood ratio test (LRT) “unifies” frequentist statistical tests. Brand-name tests like t-test, F-test, chi-squared-test, and so on are specific cases (or even approximations) of the LRT…

Testing

FastAPI Documentation

Testing Thanks to Starlette , testing FastAPI applications is easy and enjoyable. It is based on Requests , so it's very familiar and intuitive. With it, you can use pytest directly with FastAPI . Us...

P > 0.05? I can make any p-value statistically significant with adaptive FDR procedures

Simply Statistics

Everyone knows now that you have to correct for multiple testing when you calculate many p-values otherwise this can happen: http://xkcd.com/882/ One of the most popular ways to correct for multiple t...

Testing functional specification in linear regression

R-bloggers

Another one from the series on “misspecified regression models” (started with Model Misspecification and Linear Sandwiches). Intro Lately I’ve been messing around with the {lmtest} R package, a nice c...

F-statistic: Understanding model significance using python

Analytics Vidhya

In statistics, a test of significance is a method of reaching a conclusion to either reject or accept certain claims based on the data. In the case of regression analysis, it is used to determine…