Exercise

# The linear model

It is rather cumbersome to try to get the correct least squares line, i.e. the line that minimizes the sum of squared residuals, through trial and error. Instead we can use the `lm()`

function in R to fit the linear model (a.k.a. regression line).

```
model <- lm(formula, data = your_dataframe)
```

The first argument in the function `lm()`

is a formula that takes the form `y ~ x`

. Here it can be read that you want to make a linear model of `y`

as a function of `x`

. The second argument specifies that R should look in the `my_dataframe`

data frame to find the `x`

and `y`

variables.

Instructions

**100 XP**

Estimate a linear model with `runs`

as the dependent variable and `at_bats`

as the explanatory variable. Use the `lm()`

function and assign the output to `m1`

.