The normal distribution

Foundations of Probability in R

David Robinson

Chief Data Scientist, DataCamp

Flipping 10 coins

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

Foundations of Probability in R

Flipping 1000 coins

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

Foundations of Probability in R

Flipping 1000 coins

Foundations of Probability in R

Normal distribution has mean and standard deviation

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

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

Foundations of Probability in R

Normal approximation to the binomial

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)
Foundations of Probability in R

Comparing histograms

compare_histograms(binomial, normal)

Foundations of Probability in R

Let's practice!

Foundations of Probability in R

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