Exercise

# Simulating central limit theorem

**The cental limit theorem** (CLT) implies that we can apply statistical methods that work for normal distributions to problems involving other types of distributions.
Interviewers are eager to check your understanding of CLT, especially if your future position involves A/B testing.

You will show the mechanics behind CLT on the example of **die rolls**.

In the *last* exercise, you generated 1000 die rolls by setting the `size`

parameter: `sample(1:6, size = 1000, replace = TRUE)`

.

In step 1 of *this* exercise, you will generate 1 die roll output in a loop with 1000 iterations which is equivalent to the above.

To visualize:

**discrete data**- you can use`barplot(table(x))`

,**continuous data**- you can use`hist(x)`

.

The `die_outputs`

and `mean_die_outputs`

vectors have already been initialized.

Instructions 1/2

**undefined XP**

- In a loop with 1000 iterations, generate 1 random number from the range 1 to 6. Assign the results to the
`die_outputs`

vector. - Draw a bar chart to visualize the number of occurrences of each outcome.