---
title: "Lecture 5 Exercises"
author: "Your Name"
output: html_document
---
```{r setup, message = FALSE, warning = FALSE, echo=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE, dev="svg", tidy=TRUE,  tidy.opts = list(only.comment=TRUE,  width.cutoff=80))
library(summarytools)
library(dplyr)
```

We're going back to the COVID-19 data that we used before, we need to load it first.  (It's also available locally in the RStudio.cloud instance or from [my website](https://quantoid.net/files/reg3/counties.rda))

```{r}
load(file("https://quantoid.net/files/reg3/l3/counties.rda"))
```

This makes an object in your workspace called `counties`.  To remind, here's a summary of the data: 

```{r, results='asis', eval=FALSE}
codebook <- as.data.frame(sapply(counties, function(x)attr(x, "label"))) %>% 
  as_tibble(rownames = "varname") %>% 
  setNames(c("varname", "description"))
library(DT)
datatable(codebook)
```

I want you to use the data to answer the following questions. 

### Question 1

Choose a few continuous variables as predictors of `cases_per100k` and estimate a linear model. 

- Diagnose any problems of non-linearity. 

- Try to fix any non-linearity problems by changing the specification of the independent variables. 

- How does the model change? 

### Question 2

Using the same independent variables as in question 1, do the following: 

- Estimate the base model again with no transformations or changes to the original independent variable specifications. 

- Try the methods discussed in lecture to transform the dependent variable.  
