# Distribution Property Functions

In the descriptions of the distributions described throughout the website, we have provided formulas for the distribution mean and variance. Real Statistics provides the following functions to carry out these calculations.

Real Statistics Functions: The Real Statistics Resource Pack contains the following functions.

MEAN_DIST(dist, param1, param2, param3) = the mean of the distribution dist based on the listed parameters.

VAR_DIST(dist, param1, param2, param3) = the variance of the distribution dist based on the listed parameters.

Here, dist is a text string which specifies a distribution. The distributions that are currently supported are shown in Figure 1, as well as the values for the parameters.

 Distribution dist param1 param2 param3 Normal norm μ σ Log normal lognorm μ σ Chi-square chisq df t t df F f df1 df2 Binomial binom n p Poisson poisson μ Skellam skellam μ1 μ2 Beta beta α β Gamma gamma α β Uniform uniform α β PERT pert a b c Triangular triang a b c Weibull weibull β α Exponential expon λ Geometric geom p Hypergeometric hypgeom n k m Negative Binomial negbinom k p Gumbel gumbel μ β Logistic logistic μ β Laplace laplace μ β Inverse Chi-square ichisq df Inverse Gamma igamma α β

Figure 1 – MEAN_DIST and VAR_DIST parameters

For example, the formula =MEAN_DIST(“beta”,3,4) returns the mean of a beta distribution with alpha parameter 3 and beta parameter 4. The text string is the one used by Excel or Real Statistics to return the cdf/pdf of the distribution (e.g. “chisq” for chi-square, “triang” for triangular, etc.). The order of the parameters is the same as that found in the Excel or Real Statistics function that returns the cdf/pdf.

Statistics with R for Business Analysts – Normal Distribution

# Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

## Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

# Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only) `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

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!