| cars {datasets} | R Documentation |
The data give the speed of cars and the distances taken to stop. Note that the data were recorded in the 1920s.
cars
A data frame with 50 observations on 2 variables.
| [,1] | speed | numeric | Speed (mph) |
| [,2] | dist | numeric | Stopping distance (ft) |
Ezekiel, M. (1930) Methods of Correlation Analysis. Wiley.
McNeil, D. R. (1977) Interactive Data Analysis. Wiley.
require(stats); require(graphics)
plot(cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)",
las = 1)
lines(lowess(cars$speed, cars$dist, f = 2/3, iter = 3), col = "red")
title(main = "cars data")
plot(cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)",
las = 1, log = "xy")
title(main = "cars data (logarithmic scales)")
lines(lowess(cars$speed, cars$dist, f = 2/3, iter = 3), col = "red")
summary(fm1 <- lm(log(dist) ~ log(speed), data = cars))
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0),
mar = c(4.1, 4.1, 2.1, 1.1))
plot(fm1)
par(opar)
## An example of polynomial regression
plot(cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)",
las = 1, xlim = c(0, 25))
d <- seq(0, 25, length.out = 200)
for(degree in 1:4) {
fm <- lm(dist ~ poly(speed, degree), data = cars)
assign(paste("cars", degree, sep = "."), fm)
lines(d, predict(fm, data.frame(speed = d)), col = degree)
}
anova(cars.1, cars.2, cars.3, cars.4)