```{r setup, include=FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(kableExtra)

#data <- read_csv("data/...")
```

### Overview

In this notebook, we'll have an opportunity to explore inference on categorical variables. I didn't want to *choose* all of the contexts that we worked in this semester, so I'm providing several options here. I have the following data sets loaded into this Posit.Cloud workspace that we could choose to investigate.

+ `mn_police_use_of_force.csv` is a data set on *use of force* encounters with police in the state of Minnesota between 2016 and 2021.
+ `wound.csv` is a data set resulting from a medical experiment where wounds were treated with soap or saline solution. The experiment was designed to determine whether the method of cleaning had an impact on infection rates.
+ `prevend.csv` is a data set on the prevention of renal and vascular end-stage disease in the Netherlands between 2003 and 2006.

We'll decide on our dataset *and* the research questions together in class. The only requirements are that we should have (i) a research question that asks about a single population proportion, and (ii) a research question that asks about a comparison of proportions across two sub-populations.

## Our Data Set

We'll decide on our data set and read it in below.

```{r}
#Read in our chosen data set using this code cell...

```

We'll describe our data set briefly below.

### Our Research Questions and Hypotheses

We'll define our research questions and hypotheses below.

### Statistical Inference

We'll conduct our inference here.

```{r}
#We'll use this code cell (and perhaps some others) for conducting our analysis and making inference...
```

***

## Summary

We'll summarize the work we've done here.