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Summarise a data set
summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
fit <- lm(dist ~ speed, data = cars)
coef(fit)
## (Intercept) speed
## -17.579095 3.932409
Include a plot
plot(cars, pch = 19, col = "steelblue",
xlab = "Speed (mph)", ylab = "Stopping distance (ft)")
abline(fit, col = "firebrick", lwd = 2)
Stopping distance vs. speed
Math
Inline math like $y = \beta_0 + \beta_1 x + \varepsilon$ works, and so do display equations:
$$ \hat{\beta}_1 = \frac{\sum_i (x_i - \bar{x})(y_i - \bar{y})}{\sum_i (x_i - \bar{x})^2} $$