F-test

The F-test is a statistical method used to compare two or more models to determine if they significantly differ in their ability to explain the variance in a dependent variable. Primarily utilized in regression analysis and ANOVA (Analysis of Variance), the F-test assesses whether the inclusion of additional variables in a model improves its explanatory power. By evaluating the ratio of variances, the F-test helps researchers decide if a more complex model is justified over a simpler one. This test is essential for validating hypotheses and ensuring the robustness of statistical conclusions in various fields, including economics and social sciences.

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.

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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...

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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...

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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...

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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…

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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...

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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…

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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...

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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…

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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

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Fuzz-testing

 Software Architecture with C plus plus

A subcategory of DAST tools, fuzz-testing checks the behavior of your application when confronted with invalid, unexpected, random, or maliciously formed data. Such checks can be especially useful whe...

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A/B testing with Random Forest

 Towards Data Science

General non-parametric A/B test based on the Random Forest using the R-package hypoRF Continue reading on Towards Data Science

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