Univariate and Bivariate Data

Univariate: one variable,
Bivariate: two variables

Univariate means "one variable" (one type of data)

Example: Travel Time (minutes): 15, 29, 8, 42, 35, 21, 18, 42, 26

The variable is Travel Time


Example: Puppy Weights

You weigh the pups and get these results:

2.5, 3.5, 3.3, 3.1, 2.6, 3.6, 2.4

The variable is Puppy Weight

We can do lots of things with univariate data:


Bivariate means "two variables", in other words there are two types of data

With bivariate data we have two sets of related data we want to compare:

Example: Sales vs Temperature

An ice cream shop keeps track of how much ice cream they sell versus the temperature on that day.

The two variables are Ice Cream Sales and Temperature.

Here are their figures for the last 12 days:

Ice Cream Sales vs Temperature
Temperature °C Ice Cream Sales
14.2° $215
16.4° $325
11.9° $185
15.2° $332
18.5° $406
22.1° $522
19.4° $412
25.1° $614
23.4° $544
18.1° $421
22.6° $445
17.2° $408

And here is the same data as a Scatter Plot:

scatter plot ice cream sales vs temperature

Now we can easily see that warmer weather and more ice cream sales are linked, but the relationship is not perfect.

So with bivariate data we are interested in comparing the two sets of data and finding any relationships.

We can use Tables, Scatter Plots, Correlation, Line of Best Fit, and plain old common sense.