Scatter Plots (also called scatter diagrams) are used to
investigate the possible relationship between two variables that
both relate to the same "event." A straight line of best fit
(using the least squares method) is often included.
Things to look for:
- If the points cluster in a band running from lower left to
upper right, there is a positive correlation (if x increases, y
- If the points cluster in a band from upper left to lower
right, there is a negative correlation (if x increases, y
- Imagine drawing a straight line or curve through the data so
that it "fits" as well as possible. The more the points cluster
closely around the imaginary line of best fit, the stronger the
relationship that exists between the two variables.
- If it is hard to see where you would draw a line, and if the
points show no significant clustering, there is probably no
There is a maxim in statistics that says, "Correlation does
not imply causality." In other words, your scatter plot may show
that a relationship exists, but it does not and cannot prove that
one variable is causing the other. There could be a third factor
involved which is causing both, some other systemic cause, or the
apparent relationship could just be a fluke. Nevertheless, the
scatter plot can give you a clue that two things might be
related, and if so, how they move together.
For scatter plots, the following statistics are
|Mean X and Y:
||the average of all the data points
in the series.
|Maximum X and Y:
||the maximum value in the
|Minimum X and Y
||the minimum value in the
||the number of values in the
|X Range and Y Range
||the maximum value minus the minimum
|Standard Deviations for X and Y
||Indicates how widely data is spread
around the mean.
|Line of Best Fit -
||The slope of the line which fits the
data most closely (generally using the least squares
|Line of Best Fit - Y
||The point at which the line of best
fit crosses the Y axis.
Create Scatter Plots using PathMaker's Data Analyst tool.