```{r setup, include=FALSE}
library(tidyverse)

wildfires <- read_csv("http://www.evanlray.com/data/sdm4/Wildfires_2012.csv")
names(wildfires) <- c("num_fires", "years_since_1985", "ave_acres_burned")

wildfires <- wildfires %>%
  arrange(years_since_1985)
```

## Wildfire

The National Interagency Fire Center (www.nifc.gov) reports statistics about wildfires. I have loaded a dataset called `wildfires` with information about the number of wildfires, and the average number of acres burned per wildfire (in hundreds of thousands of acres) in each year from 1985 to 2012.

You can take a look at the data using the `head` function like this.

```{r}
head(wildfires)
```

### Part I

#### Problem 1
Make a scatter plot with `years_since_1985` on the horizontal axis and `num_fires` on the vertical axis. Also add the least square line on your scatter plot.

#### Problem 2
Fit a regression model using `num_fires` as the response variable and `years_since_1985` as the explanatory variable, and print out a summary of the linear model fit.

#### Problem 3
Is a linear model appropriate for these data? Remember to check the following conditions:

-   **Straight Enough Condition** 
-   **Outlier Condition** 
-   **Does the Plot Thicken? Condition**

#### Problem 4
Interpret the slope in this context.

#### Problem 5
Can we interpret the intercept? Why or why not?

#### Problem 6
What does $R^2$ mean in this context?

### Part II

We saw in Part I that the number of fires was nearly constant. But has the damage they cause remained constant as well? Let's examine the trend in Acres per Fire, (in hundreds of thousands of acres).

#### Problem 1
Make a scatter plot with `years_since_1985` on the horizontal axis and `ave_acres_burned` on the vertical axis. Also add the least square line on your scatter plot.

#### Problem 2
Fit a regression model using `ave_acres_burned` as the response variable and `years_since_1985` as the explanatory variable, and print out a summary of the linear model fit.

#### Problem 3
Is a linear model appropriate for these data? Remember to check the following conditions:

-   **Straight Enough Condition** 
-   **Outlier Condition** 
-   **Does the Plot Thicken? Condition**

#### Problem 4
Interpret the slope in this context. 

#### Problem 5
Can we interpret the intercept? Why or why not?

#### Problem 6
What does $R^2$ mean in this context?

