---
title: "SDM4 in R: Understanding Randomness (Chapter 10)"
author: "Nicholas Horton (nhorton@amherst.edu), Patrick Frenett, and Sarah McDonald"
date: "June 13, 2018"
output: 
  pdf_document:
    fig_height: 3
    fig_width: 6
  html_document:
    fig_height: 3
    fig_width: 5
  word_document:
    fig_height: 4
    fig_width: 6
---


```{r, include = FALSE}
# Don't delete this chunk if you are using the mosaic package
# This loads the mosaic and dplyr packages
require(mosaic)
```

```{r, include = FALSE}
# knitr settings to control how R chunks work.
require(knitr)
opts_chunk$set(
  tidy = FALSE,     # display code as typed
  size = "small"    # slightly smaller font for code
)
```

## Introduction and background 

This document is intended to help describe how to undertake analyses introduced 
as examples in the Fourth Edition of *Stats: Data and Models* (2014) by De Veaux, Velleman, and Bock.
More information about the book can be found at http://wps.aw.com/aw_deveaux_stats_series.  This
file as well as the associated R Markdown reproducible analysis source file used to create it can be found at http://nhorton.people.amherst.edu/sdm4.

This work leverages initiatives undertaken by Project MOSAIC (http://www.mosaic-web.org), an NSF-funded effort to improve the teaching of statistics, calculus, science and computing in the undergraduate curriculum. In particular, we utilize the `mosaic` package, which was written to simplify the use of R for introductory statistics courses. A short summary of the R needed to teach introductory statistics can be found in the mosaic package vignettes (http://cran.r-project.org/web/packages/mosaic).
A paper describing the mosaic approach was published in the *R Journal*: https://journal.r-project.org/archive/2017/RJ-2017-024.

## Chapter 10: Understanding Randomness

### Section 10.1: What is Randomness? 

### Section 10.2: Simulating by Hand

Shown below is the box and whisker plot from page 284.

```{r}
library(mosaic)
obs <- c(5,4,7,5,18,6,5,4,6) 
ID <- 1:length(data)
data <- data.frame(ID, obs)
gf_boxplot(obs ~ 1, data = data) # gf_boxplot(~ age) support is planned for the future
```