One vs Two Variable Data

What is a variable?

A characteristic that can change or vary between participants/records.

One Variable / Single Variable Data

A dataset or a single column from a dataset where each item has only one characteristic (variable) being measured or observed.

Player Name	Height (cm)	Weight (kg)	40-Yard Dash (s)	Vertical Jump (in)
Marcus Doyle	198	118	4.78	33
DeShawn Rivers	185	93	4.41	37
Blake Armstrong	191	102	4.54	35
Jamal Carter	178	88	4.29	39
Trevor McKnight	195	109	4.65	32
Andre Lopez	183	96	4.50	36
Colton Hayes	200	124	4.89	31
Malik Jefferson	188	98	4.42	38
Jared Cole	180	85	4.31	40
Ethan Brooks	193	105	4.60	34

This list above is a fictional dataset containing an Football player's height, weight, their 40 yard run time in seconds and their vertical jump height in inches.

Each columns of height, weight, 40 yard dash time and vertical jump height can be studied individually as One Variable Data.

Example

Given the 10 player's 40-yard dash data, we can study and analyze as it one variable data analysis.

We could look at things like:

• Slowest and Fastest Player

• Average time elapsed between the 10 players

Two Variable Data

Can be called bivariate data

Data where each item has two characteristics (variables) measured at the same time.

• We often use two variable data to find relationship or associations between the variables

Player Name	Height (cm)	40-Yard Dash (s)
Marcus “Tank” Doyle	198	4.78
DeShawn Rivers	185	4.41
Blake Armstrong	191	4.54
Jamal “Jet” Carter	178	4.29
Trevor McKnight	195	4.65
Andre Lopez	183	4.50
Colton “Crusher” Hayes	200	4.89
Malik Jefferson	188	4.42
Jared “Lightning” Cole	180	4.31
Ethan Brooks	193	4.60

The dataset we have seen prior has been shrunk down, but also arranged so that we are creating a two variable dataset.

This table can be used to study the relationship between a player's height and their 40-yard dash speed.