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.

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