In this lesson:
By the end of this lesson, you should be able to:
How would you describe each so someone who can't see it knows what it shows?
Look at last lesson's study-time cloud.
Trace its general direction — it drifts up to the right.
That up-to-the-right drift has a name: positive association.
Knowing one variable tells you nothing about the other.
All three trend upward. Does loose scatter mean "no association"? Predict before advancing.
The three-part description:
For each: direction (up/down/none) and shape (linear/nonlinear)?
You've named what the cloud does overall.
Now zoom in on the points that break the pattern, and the places where points bunch up.
Negative linear; a cluster of warm southern cities; one cold-for-its-latitude outlier.
Write a complete 3-4 sentence description of a new scatter plot.
Include: direction, shape, any clusters, any outliers, and a plausible explanation.
✓ Describe direction, shape, and strength ✓ Outliers and clusters carry real information ✓ Always interpret in context
Watch out: loose scatter is still association, not "no pattern" Watch out: an outlier isn't automatically an error Watch out: association is never proof of causation
When the association is linear, the next standard draws a line through the cloud.
That line becomes an equation you can use to predict.
Click to begin the narrated lesson
Construct and interpret scatter plots for bivariate measurement data