**Goal:** After completing this lab, you should be able toâ€¦

*Use*the`lm()`

function in`R`

to fit regression models.*Plot*fitting SLR models.*Predict*new observations using SLR models.

In this lab we will use, but not focus onâ€¦

`R`

Markdown. This document will serve as a template. It is pre-formatted and already contains chunks that you need to complete.

Some additional notes:

- Please see
**Carmen**for information about submission, and grading. - You may use this document as a template. You do not need to remove directions. Chunks that require your input have a comment indicating to do so.
- The following readings may be very useful:

In class we looked at the (boring) `cars`

dataset. Use `?cars`

to learn more about this dataset. (For example, the year that it was gathered.)

`head(cars)`

```
plot(dist ~ speed, data = cars, pch = 20)
grid()
```

Our purpose with this dataset was to fit a line that summarized the data. We did this with the `lm()`

function in `R`

.

`cars_mod = lm(dist ~ speed, data = cars)`

Using the `summary()`

function on the result of the `lm()`

function produced some useful output, including the slope and intercept of the line that we fit.

`summary(cars_mod)`

```
##
## Call:
## lm(formula = dist ~ speed, data = cars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.069 -9.525 -2.272 9.215 43.201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.5791 6.7584 -2.601 0.0123 *
## speed 3.9324 0.4155 9.464 1.49e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.38 on 48 degrees of freedom
## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438
## F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12
```

We could use the `abline()`

function to add this line to a plot.

```
plot(dist ~ speed, data = cars, pch = 20)
grid()
abline(cars_mod, col = "red")
```