cars {base} | R Documentation

## Speed and Stopping Distances of Cars

### Description

The data give the speed of cars and the distances taken to stop. Note that the data were recorded in the 1920s.

### Usage

    data(cars)

### Format

A data frame with 50 observations on 2 variables.

[,1] | speed | numeric | Speed (mph)
[,2] | dist | numeric | Stopping distance (ft)

### Source

Ezekiel, M. (1930) Methods of Correlation Analysis. Wiley.

### References

McNeil, D. R. (1977) Interactive Data Analysis. Wiley.

### Examples

    require(stats)
    data(cars)
    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, len = 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)

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