La distribuzione normale

Fondamenti di probabilità in R

David Robinson

Chief Data Scientist, DataCamp

Lanciare 10 monete

flips <- rbinom(100000, 10, .5)

flips <- rbinom(100000, 1000, .5)

$$X \sim \text{Normal}(\mu,\sigma)$$

$$\sigma = \sqrt{\text{Var}(X)}$$

binomial <- rbinom(100000, 1000, .5)

$$\mu = \text{size} \cdot p$$ $$\sigma = \sqrt{\text{size} \cdot p \cdot (1 - p)}$$

expected_value <- 1000 * .5
variance <- 1000 * .5 * (1 - .5)
stdev <- sqrt(variance)

normal <- rnorm(100000, expected_value, stdev)

compare_histograms(binomial, normal)

Fondamenti di probabilità in R