Goal: After completing this lab, you should be able to…

In this lab we will use, but not focus on…

Some additional notes:


Exercise 0 - Cars

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")