## (Basic Statistics for Citizen Data Scientist)

# F Distribution

The F-distribution is primarily used to compare the variances of two populations, as described in Hypothesis Testing to Compare Variances. This is particularly relevant in the analysis of variance testing (ANOVA) and in regression analysis.

**Definition 1**: The The F-distribution with *n _{1}, n_{2}*degrees of freedom is defined by

**Theorem 1**: If we draw two independent samples of size *n _{1}* and

*n*respectively from two normal populations with the same variance then

_{2}Proof: By Theorem 2 of Chi-square Distribution, If *x* is drawn from a normally distributed population *N*(*μ ,σ*) then for samples of size *n*:

Thus if we draw two independent samples from two normal populations with the same variance σ, then by Definition 1,

**Property 1**: A random variable* t* has distribution *T*(*k*) if and only if *t*^{2} has distribution *F*(1, *k*).

**Excel Functions**: The following Excel functions are defined for the distribution:

**FDIST**(*x, df _{1}, df_{2}*) = the probability that the F-distribution with

*df*and

_{1}*df*degrees of freedom is ≥

_{2}*x*; i.e. 1 –

*F*(

*x*) where

*F*is the cumulative F-distribution function.

**FINV**(*α, df _{1}, df_{2}*) = the value

*x*such that FDIST(

*x, df*) = 1 –

_{1}, df_{2}*α*; i.e. the value

*x*such that the right tail of the F-distribution with area

*α*occurs at

*x*. This means that

*F*(

*x*) = 1 –

*α*, where

*F*is the cumulative F-function.

With Excel 2010/2013/2016 there are a number of new functions (**F.DIST, F.INV, F.DIST.RT **and **F.INV.RT**) that provide equivalent functionality to FDIST and FINV, but whose syntax is more consistent with other distribution functions. These functions are described in Built-in Statistical Functions.

**Observation**: Excel only calculates the above functions for positive integer values of *df*1 and *df*2. Non-integer values are rounded down to the nearest integer. Thus, F.DIST(3,1.6,5,TRUE) = F.DIST(3,1,5,TRUE). In particular, all of the above Excel functions yield an error value when *df*1 < 1 or *df*2 < 1.

If you need a more accurate value of any of the F distribution functions when either or both of the degrees of freedom are not integers, and in particular when either of them is less than one, then you can use Real Statistics’ noncentral F distribution functions (with noncentrality value of zero), as described in Noncentral F Distribution. For example, the formula F.DIST(3,1,5,TRUE) = .8562, but F.DIST(3,0.99,5,TRUE) = #NUM!, whereas NF_DIST(3,0.99,5,0,TRUE) = .8606.

Alternatively, you can use the following Real Statistics functions.

**Real Statistics Functions**: The Real Statistics Resource Pack provides the following functions:

**F_DIST**(*x*, *df*1, *df*2, *cum*) = BETA.DIST(*x* * *df*1 / (*x* * *df*1 + *df*2), *df*1 / 2, *df*2 / 2, *cum*)

**F_INV**(*p*, *df*1, *df*2) = *x* * *df*2 / (*df*1 * (1 – *x*)) where *x* = BETA.INV(*p*, *df*1/2, *df*2/2)

Here F_DIST is a substitute for F.DIST and F_INV is as substitute for F.INV. Not only do these functions provide better estimates of the F distribution when the degrees of freedom are not integers, but F_DIST is also useful in providing an estimate of the pdf for versions of Excel prior to Excel 2010, where F.DIST(*x*, *df*1, *df*2, FALSE) is not available.

The Real Statistics Resource also provides the following functions:

**F_DIST_RT**(*x, df1, df2*) = 1 – F_DIST(*x, df1, df2* TRUE)

**F_INV_RT**(*p, df1, df2*) = 1 – F_INV(*p, df1, df2*)

Statistics for Beginners – Discrete Probability Distributions

## Statistics for Beginners in Excel – F Distribution

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:

**All Notebooks in One Bundle: Data Science Recipes and Examples in Python & R****. **

**End-to-End Python Machine Learning Recipes & Examples.**

**End-to-End R Machine Learning Recipes & Examples.**

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**

**R Machine Learning & Data Science Recipes: Learn by Coding**

**Comparing Different Machine Learning Algorithms in Python for Classification (FREE)**

There are 2000+ End-to-End Python & R Notebooks are available to build **Professional Portfolio as a Data Scientist and/or Machine Learning Specialist**. All Notebooks are only $29.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.