---
title: "Central Limit Theorem"
author: "Russell Almond"
date: "September 8, 2020"
output: html_document
runtime: shiny
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Pick a distribution:
* Uniform -- platykurtic
* Binomial -- symmetric and mesokurtic
* Exponential -- highly skewed
* Gamma (shape = 3) -- skewed
* T (df =3) -- high kurtosis

Slide the sample size up and down, notice how the empirical distribution function and histogram coverge to the normal distribution function and density.

```{r DistConv, echo=FALSE}
nmax <- 1000
mmax <- 100
rdist <- list(Uniform=runif,
              Binomial= function(n) rbinom(n,1,.5),
              Exponential = rexp, 
              Gamma = function(n) rgamma(n,3),
              "T" = function(n) rt(n,3))

inputPanel(
  selectInput("dist",label="Distribution Type",
              choices=c("Uniform","Binomial","Exponential","Gamma","T"),
              selected="Unifor"),
   sliderInput("NN", label = "Number of Samples:",
              min = 25, max=nmax, value=nmax, step=5),
  sliderInput("MM",label="Size of each sample:", min=1, max=mmax,value=1,step=1)

)

XX <- reactive(matrix(do.call(rdist[[input$dist]],list(nmax*mmax)),nmax,mmax))

renderPlot({
  layout(matrix(1:4,2,2))
  X1 <- XX()[1:input$NN,1]
  Xmean <- rowMeans(XX()[1:input$NN,1:input$MM,drop=FALSE])

  hist(X1,main="Average of sample of size 1",probability=TRUE)
  curve(dnorm(x,mean(X1),sd(X1)),add=TRUE,lty=2,col="red")

  qqnorm(X1,main="Average of sample of size 1")
  qqline(X1)

  hist(Xmean,
       main=paste("Average of sample of Size",input$MM),probability=TRUE)
  curve(dnorm(x,mean(Xmean),sd(Xmean)),add=TRUE,lty=2,col="red")

  qqnorm(Xmean,main=paste("Average of sample of Size",input$MM))
  qqline(Xmean)
  
  
  
})
```

The left column shows the original distribution.  (I call that the _black hat_ in my CLT demo.)

The right column shows the distribution of means of size $M$ (adjusted with the second slider).  (This is the _white hat_ distribuiton, and $M$ is the number of cards averaged to get the white hat value.)  

The top row shows histograms with a normal curve on top.

The bottom row shows a QQ-plot.  This shows how much the sample is different from a normal distribution.  A normal distribution should be right on top of the diagonal line.  

* A U-shaped curve indicates skewness (and an upside down U is negatively skewed).  Try the exponential distribution.

* An S-shaped curve indicates high kurtosis (a backwards S, low kurtosis)  Try the Student-t and Uniform distributions.

As $M$ (the number of cards averages to get to the white hat) gets bigger, the distribution should get closer and closer to the normal distribution.

## Take home

* Even if the underlying data aren't normal, the distribuiton of the means of various groups should be close to normal.

* Close depends on the sample size.

* A bigger sample is needed if the data are highly skewed (expontential and gamma) or leptokurtic (exponential and Student t).