9.4: Variability

Let’s first compute the variance, which is the average squared difference between each value and the mean. Let’s do this with our cleaned-up version of the height data, but instead of working with the entire dataset, let’s take a random sample of 150 individuals:

height_sample <- NHANES %>%
  drop_na(Height) %>%
  sample_n(150) %>%
  pull(Height)

First we need to obtain the sum of squared errors from the mean. In R, we can square a vector using **2:

SSE <- sum((height_sample - mean(height_sample))**2)
SSE

## [1] 63419

Then we divide by N - 1 to get the estimated variance:

var_est <- SSE/(length(height_sample) - 1)
var_est

## [1] 426

We can compare this to the built-in var() function:

var(height_sample)

## [1] 426

We can get the standard deviation by simply taking the square root of the variance:

sqrt(var_est)

## [1] 21

Which is the same value obtained using the built-in sd() function:

sd(height_sample)

## [1] 21