cars                package:datasets                R Documentation

_S_p_e_e_d _a_n_d _S_t_o_p_p_i_n_g _D_i_s_t_a_n_c_e_s _o_f _C_a_r_s

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     cars

_F_o_r_m_a_t:

     A data frame with 50 observations on 2 variables.

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

_S_o_u_r_c_e:

     Ezekiel, M. (1930) _Methods of Correlation Analysis_. Wiley.

_R_e_f_e_r_e_n_c_e_s:

     McNeil, D. R. (1977) _Interactive Data Analysis_. Wiley.

_E_x_a_m_p_l_e_s:

     require(stats)
     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)

