Scatter Plots: Properties, Characteristics, and Examples
1. A scatter plot provides the most useful way to display bivariate (2-variable) data.
2. A scatter plot can indicate the presence or absence of an association or relationship between two
variables.
• If some association or relationship exists, the data will tend to cluster on or around some line or
curve that cuts through the plotted points.
• If no association or relationship exists, the data will appear spread out, perhaps like a shotgun
blast.
• An informal way to help identify whether some association or relationship exists is to apply the
Ellipse Test.
On your scatter plot, capture the plotted points with an ellipse.
The flatter and more elongated your ellipse, the more convincing is your evidence of
some association or relationship between the two variables. The direction (slope) of the
major axis in such an ellipse can provide a guide for the direction of the association or
relationship (see #3 below). The flatter your ellipse, the stronger the association or
relationship may be.
As your ellipse appears more and more circular, the less likely it is that there is some
association or relationship between the two variables.
3. When some association or relationship exists between two variables, a scatter plot can indicate the
direction of the relationship.
• When high values of one variable correspond to high values of another variable, and low values of
one variable correspond to low values of another variable, we say the relationship is positive
because there is a direct relationship between the two variables.
• When high values of one variable correspond to low values of another variable, and low values of
one variable correspond to high values of another variable, we say the relationship is negative
because there is an inverse relationship between the two variables.
4. A scatter plot can be used to help determine if an association or relationship is linear or nonlinear.
5. A scatter plot provides a visual revelation of outliers or dubiously measured or erroneously plotted data.
