04-sampling-dsn.Rmd

---
title: "Chapter 4: Sampling Distributions"
output: html_document
---


## Introduction

```{r}
library(purrr)
```


## Example 4.2

```{r}
my_means <- 1000 %>%
  rerun(rexp(100, rate = 1/15)) %>%
  map_dbl(~mean(.)) %>%
  data.frame(means = .)

my_means %>%
  ggplot(aes(x = means)) + 
  geom_histogram(bins = 20)
```

## Example 4.3

```{r}
my_max <- 1000 %>%
  rerun(runif(12)) %>%
  map_dbl(~max(.)) %>%
  data.frame(max = .)

my_max %>%
  ggplot(aes(x = max)) + 
  geom_histogram(bins = 20)
```


## Example 4.7

```{r}
my_means <- 1000 %>%
  rerun(rgamma(30, shape = 5, rate = 2)) %>%
  map_dbl(~mean(.)) %>%
  data.frame(means = .)

my_means %>%
  ggplot(aes(x = means)) + 
  geom_histogram(bins = 20)

mean(my_means$means)
sd(my_means$means)
mean(my_means$means > 3)
```


## Example 4.8

```{r}
my_means <- 1000 %>%
  rerun(rgamma(17, shape = 100, rate = 5)) %>%
  map_dbl(~mean(.)) %>%
  data.frame(means = .)

my_means %>%
  ggplot(aes(x = means)) + 
  geom_histogram(bins = 20)
```