Linear regression in R

Fitting a linear model with the formula interface.

set.seed(5)
data <- data.frame(
  hours = rnorm(60, 20, 5),
  caffeine = runif(60, 0, 4)
)
data$score <- 40 + 2.5 * data$hours + 3 * data$caffeine + rnorm(60, sd = 6)

model <- lm(score ~ hours + caffeine, data = data)

coefs <- coef(model)
r_squared <- summary(model)$r.squared
residuals <- resid(model)
predictions <- fitted(model)

How it works

1. score ~ hours + caffeine names outcome and predictors.
2. coef reads the fitted coefficients.
3. summary(model)$r.squared, resid, and fitted assess it.

Keywords and builtins used here

• coef
• fitted
• lm
• resid
• rnorm
• runif
• summary

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Step 2 of 4 in Correlation & regression, step 7 of 11 in Statistics.

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Linear regression in other languages

• Python