Aliases: cars
Keywords: datasets
### ** Examples 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))
Call: lm(formula = log(dist) ~ log(speed), data = cars) Residuals: Min 1Q Median 3Q Max -1.00215 -0.24578 -0.02898 0.20717 0.88289 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.7297 0.3758 -1.941 0.0581 . log(speed) 1.6024 0.1395 11.484 2.26e-15 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4053 on 48 degrees of freedom Multiple R-squared: 0.7331, Adjusted R-squared: 0.7276 F-statistic: 131.9 on 1 and 48 DF, p-value: 2.259e-15
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)
Analysis of Variance Table Model 1: dist ~ poly(speed, degree) Model 2: dist ~ poly(speed, degree) Model 3: dist ~ poly(speed, degree) Model 4: dist ~ poly(speed, degree) Res.Df RSS Df Sum of Sq F Pr(>F) 1 48 11354 2 47 10825 1 528.81 2.3108 0.1355 3 46 10634 1 190.35 0.8318 0.3666 4 45 10298 1 336.55 1.4707 0.2316