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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 provides a better fit to the data compared to a simpler model. By testing the null hypothesis that all regression coefficients are equal to zero, the F-test aids in understanding the explanatory power of the independent variables in predicting the dependent variable.

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…

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...

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...

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…

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...

All about T-tests— demystified

Analytics Vidhya

T-test is one of the most widely used test for hypothesis testing in the world of statistics. I have covered student's t test, paired t test and two sample t test

The Lesser-Known, AWESOME Signs Test

Towards Data Science

We are all familiar with the T-test, maybe the F-test, and maybe even some others like Wilcox rank-sum tests and things of that nature. There are a lot of statistical tests, some of which we all use…

Testing

Matplotlib User's Guide

Testing Matplotlib uses the pytest framework. The tests are in lib/matplotlib/tests , and customizations to the pytest testing infrastructure are in matplotlib.testing . Requirements To run the tests ...

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...