Statistics Foundation · Lesson 5.1
Correlation and simple relationships.
Regression begins with relationships between variables. This lesson develops scatterplots, direction, strength, Pearson correlation, nonlinear patterns, outliers and the crucial distinction between association and causation.
Lesson route
Move from visual association to careful interpretation.
This lesson prepares students for regression by showing how relationships appear in data, how correlation summarises linear association and why causal claims require more than a numerical relationship.
0–15 min
Relationships between variables
Understand what it means for two quantitative variables to move together.
15–35 min
Scatterplots
Learn how scatterplots reveal direction, form, strength, clusters and unusual points.
35–60 min
Correlation
Study correlation as a numerical measure of linear association.
60–85 min
Interpreting r
Understand sign, magnitude, units, limitations and common mistakes.
85–115 min
Outliers and nonlinearity
Explore how correlation can be distorted by unusual points and can miss curved relationships.
115–145 min
Association versus causation
Separate statistical association from causal explanation, confounding and study design.
Mastery checklist
Students should reason beyond the number.
Describe positive, negative and weak associations.
Read scatterplots for form, direction, strength and outliers.
Define Pearson correlation.
Interpret the sign and magnitude of r.
Explain why r is unit-free.
Recognise nonlinear relationships.
Explain how outliers can affect correlation.
Separate association from causation.