README.md

In umutkahramankaptan/CLT_Shiny: Shiny App for Demonstrating Central Limit Theorem

Introduction

This Shiny App is for visually displaying Central Limit Theorem which states that, "the distribution of the sum (or average) of a large number of independent, identically distributed variables will be approximately normal, regardless of the underlying distribution." (1)

The app is created for Coursera Data Science Specialization - Developing Data Products course, and is available through https://ukahramankaptan.shinyapps.io/Central_Limit_Theorem

Instructions to use

To demonstrate CLT:

1. Select a number of Simulations to run via Sliding Bar.
2. Input a positive number for number of iterates per simulation
3. Select an underlying distribution.

Goals of observation

1. While number of iid variables increase, simulations will approximate to the underlying distribution in the upper figure.
2. While number of simulations increasing distribution of means will approximate to the normal distribution in the lower figure.
3. With changing underlying distributions, while the simulation changes according to their underlying distribution in the upper figure, the distribution of means will still approximate to the normal distribution in the lower figure

References

(1) The Cetral Limit Theorem, University of Alabama in Huntsville, Mathematics Department; http://www.math.uah.edu/stat/sample/CLT.html

umutkahramankaptan/CLT_Shiny documentation built on May 3, 2019, 2:24 p.m.