Variance is a tricky statistic to use because its units are different from both the mean and the data itself. For example, the mean of our NBA dataset is `77.98`

inches. Because of this, we can say someone who is `80`

inches tall is about two inches taller than the average NBA player.

However, because the formula for variance includes *squaring* the difference between the data and the mean, the variance is measured in *units squared*. This means that the variance for our NBA dataset is `13.32`

inches squared.

This result is hard to interpret in context with the mean or the data because their units are different. This is where the statistic *standard deviation* is useful.

Standard deviation is computed by taking the square root of the variance. `sigma`

is the symbol commonly used for standard deviation. Conveniently, `sigma`

squared is the symbol commonly used for variance:

`$\sigma = \sqrt{\sigma^2} = \sqrt{\frac{\sum_{i=1}^{N}{(X_i -\mu)^2}}{N}}$`

In R, you can take the square root of a number using `^ 0.5`

or `sqrt()`

, up to you which one you prefer:

num <- 25 num_square_root <- num ^ 0.5

### Instructions

**1.**

We’ve written some code that calculates the variance of the NBA dataset and the OkCupid dataset.

The variances are stored in variables named `nba_variance`

and `okcupid_variance`

.

Calculate the standard deviation by taking the square root of `nba_variance`

and store it in the variable `nba_standard_deviation`

. Do the same for the variable `okcupid_standard_deviation`

.