Before we start analyzing, we need to make one more distinction between different types of data. Then our data can take a seat on the couch and we'll start getting to the root of its daddy issues.

**Single-variable** or **univariate** data refers to data where we're only observing one aspect of something at a time. With single-variable data, we can put all our observations into a list of numbers.

We take a group of people, measure their heights, and get this list of heights:

5'2'', 5'4'', 6'1'', 5'9'', 5'3''.

This is univariate data, since we're only observing one aspect (the height) of each person.

With **two-variable**, or **bivariate** data, we observe two aspects. We can put our observations into a table. The columns-and-rows kind, not the upending-and-throwing-across-the-room-in-a-rage kind.

We take a group of people, measure their heights and weights, and get the following information:

This is bivariate data, since we have observations about two aspects (the height and weight) of each person.

Next Page: Analysis of Single-Variable Data

Previous Page: Discrete v. Continuous Data