A pre-loaded example dataset in R

Main page: https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/cars

head(cars)
##   speed dist
## 1     4    2
## 2     4   10
## 3     7    4
## 4     7   22
## 5     8   16
## 6     9   10
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