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)