Exercise

# Simulating binary data

A Bernoulli distribution is a special case of a binomial.
Next, you will see how to simulate both in R and then examine the outputs to see how they are similar.
Both distributions can be simulated with the random binomial function: `rbinom()`

.
`rbinom()`

requires 3 arguments:

`n`

, which is the number of draws or random numbers (i.e., an output vector of length`n`

).`size`

, which is the number of samples per draw (i.e., the maximum value for each random number).`prob`

, which is the probability for the simulation.

To sample with a Bernoulli, you simply use `size = 1`

.

If we take a single random draw (`n = 1`

) from a binomial distribution with a large number of samples per draw (e.g. `size = 100`

), we should get similar results as a taking a many samples (e.g. `n = 100`

) with 1 sample per draw (`size = 1`

).

Instructions 1/4

**undefined XP**

- Simulate from a binomial distribution with 1 sample (
`n`

), a size (`size`

) of 100, and a probability (`p`

) of 0.5.