While measures of central tendency try to give an idea about where data is centered, measures of dispersion are meant to give a general idea about how data is distributed around the center. Standard deviation and variance are the most popular measures of dispersion. The square root of the variance equals the standard deviation. It's very easy to get both values with R:
sd(big_sample, na.rm = T)# outputs [1] 5.01836var(big_sample, na.rm = T)# outputs [1] 25.18394
Keep in mind that these computations we've done so far are estimations from the (real) parameters, not parameters itself.
The sd() function estimates the standard deviation while var() estimates the variation. In most cases, we find ourselves with a DataFrame ...
