
---
title: "Problem Set 10"
author: "WRITE YOUR NAME HERE"
date: "Due Monday, November 28, 2016 at 5pm"
output:
  html_document
---



## Please Indicate

* Who you collaborated with. If no one, please indicate so: 
* Approximately how much time did you spend on this problem set: 
* What, if anything, gave you the most trouble: 



```{r, message=FALSE, echo=FALSE}
library(ggplot2)
library(dplyr)
library(mosaic)
library(readr)
library(knitr)
```



## Instructions/Hints:

* If your simulations are really slow at first, instead of generating your null
distributions based on many, many, many instances, try using a smaller number at
first to test your code out. Once you feel like you have the right format, then
set it to a large number.





## Question 1:

Answer the question from Question 3 from the midterm "The Dude Rolling Die": is 
he full of BS or can he actually correctly predict die rolls? Base your answer
on the p-value.

For this question, since there are only 7 possible outcomes (# correct out of 6
guesses), use a `geom_bar()` instead of a `geom_histogram()` to show your
results.

```{r, message=FALSE, echo=TRUE, warning=FALSE, fig.width=16/2, fig.height=9/2}

```





## Question 2:

#### a) 

Let's consider the hiring data in `hiring.csv`. Let's say we are only looking
for mild evidence that women got lower `hireable` scores than men.

1. Create an appropriate exploratory data analysis visualization.
1. Perform an hypothesis test of
    + $H_0:$ men and women got the on average the same `hireable` score vs
    + $H_A:$ you choose! state your choice below
1. State the conclusion of the test both
    + in statistical language
    + in terms of the data's context.


```{r, message=FALSE, echo=TRUE, warning=FALSE, fig.width=16/2, fig.height=9/2}
# Copy the code that loads hiring.csv into RStudio here:


# Do your exploratory data analysis here:


# Perform your hypothesis test, including p-value


```


#### b) 

Who penalized women applicants more on average? The male evaluators or the
female evaluators? Do not perform an exhaustive hypothesis test, but rather

1. Generate an appropriate exploratory data analysis visualization AND
1. Summarize the data in an appropriate table of values


```{r, message=FALSE, echo=TRUE, warning=FALSE, fig.width=16/2, fig.height=9/2}
# Do your exploratory data analysis here:


```
