---
title: "Statistics Lab"
output: html_document
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r, include = FALSE}
library(tidyverse)
library(broom)
library(jhur)
library(psych)
library(corrr)
```

# Part 1

Read in the following infant mortality data from http://johnmuschelli.com/intro_to_r/data/indicatordeadkids35.csv
```{r}
mort = read_csv("http://johnmuschelli.com/intro_to_r/data/indicatordeadkids35.csv")
```
or use 
```{r}
mort = read_mortality()
```

Then change the first column name to `country`"

```{r}
mort = mort %>% 
  rename(country = X1)
```

or 

```{r}
colnames(mort)[1] = "country"
```

1. Compute the correlation between the `1980`, `1990`, `2000`, and `2010` mortality data.
No need to save this in an object. Just display the result to the screen. Note any `NA`s.  Then compute using `use = "complete.obs"`.
```{r}


```


2. a. Compute the correlation between the `Myanmar`, `China`, and `United States` mortality data. Store this correlation matrix in an object called `country_cor`

b. Extract the Myanmar-US correlation from the correlation matrix.

```{r}

```


3. Is there a difference between mortality information from `1990` and `2000`?
Run a t-test and a Wilcoxon rank sum test to assess this.
Hint: to extract the column of information for `1990`, use `mort$"1990"`

```{r}

```
# Part 2

Read in the Kaggle cars auction dataset from http://johnmuschelli.com/intro_to_r/data/kaggleCarAuction.csv with the argument `col_types = cols(VehBCost = col_double())`:
```{r}
cars = read_csv("http://johnmuschelli.com/intro_to_r/data/kaggleCarAuction.csv",
col_types = cols(VehBCost = col_double()))
```


4. Fit a linear regression model with vehicle cost (`VehBCost`) as the outcome and 
vehicle age (`VehicleAge`) and whether it's an online sale (`IsOnlineSale`) as predictors as well as their interaction.  Save the model fit in an object called `lmfit_cars` and display the summary table.

```{r}

```

5. Create a variable called `expensive` in the `cars` data that indicates if the 
vehicle cost is over `$10,000`. Use a chi-squared test to assess if there is a
relationship between a car being expensive and it being labeled as a "bad buy" (`IsBadBuy`).

```{r}

```

6. Fit a logistic regression model where the outcome is "bad buy" status and predictors are the `expensive` status and vehicle age (`VehicleAge`).
Save the model fit in an object called `logfit_cars` and display the summary table.
Use summary or `tidy(logfit_cars, conf.int = TRUE, exponentiate = TRUE)`
or `tidy(logfit_cars, conf.int = TRUE, exponentiate = FALSE)` for log odds ratios 

```{r}

```

7. Randomly sample `10,000` observations (rows) with replacement from the `cars` dataset and store this new dataset in an object called `cars_subsample`.  Hint: `sample_n` function

```{r}

```


8. Fit the same logistic regression model as in problem 6 above 
with this sample and display the summary table using the `tidy` function. How do the results compare?  Call this model `logfit_cars_sub`:

```{r}

```
