Statistics
Linear Regression and Residuals worksheets
Fit ordinary least-squares lines with intercepts, predict responses, calculate observed-minus-predicted residuals, and identify extrapolation.
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Scatterplots and Correlation worksheets
Construct and read labeled scatterplots from actual paired observations, calculate Pearson r, and interpret linear association without claiming causation.
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Use the labeled paired observations. Describe form and direction before calculating Pearson r; round r to the nearest thousandth. Association alone does not establish causation.
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Customize this worksheetKeep the paired observations visible
Every plot and calculation comes from the labeled x and y table printed beside it. Construction tasks keep the student plot blank, while reading tasks provide the actual point cloud and worked keys complete each requested diagram.
Learning objective
What this activity builds
Use paired quantitative data to describe direction and form, calculate Pearson correlation, and distinguish association from causation.
Worked example
A scatterplot rises from left to right and has r = 0.82. What does that mean?
The sample has a strong positive linear association under the stated classroom cutoff.
The sign gives direction and the magnitude describes linear strength. It does not show that changing x causes y to change.
Teaching notes
Make the practice count
- Plot every ordered pair from the table before describing the cloud.
- Discuss form before relying on r; a curved pattern can have r near zero.
- Treat the printed strength cutoffs as a classroom convention, not universal boundaries.
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Choose paired-observation count, relationship patterns, construction/reading/Pearson/interpretation tasks, and problem count.
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