Intermediate level · Module 4: Relationships and Regression for Analysis · Lesson 1 of 3
Correlation
Measure the strength and direction of relationships and understand what correlation does not show.
In this lesson you will
- Calculate Pearson and Spearman correlations
- Explain why correlation is not causation
Key concepts
Pearson correlationSpearman correlationscatter plotcorrelation vs causation
The lesson
Correlation measures how two numeric variables move together, from −1 to +1. Pearson's correlation measures straight-line relationships; Spearman's uses ranks and suits curved but consistent relationships and outliers.
Always plot the data: very different patterns can share the same correlation, and one outlier can create or hide a correlation.
Correlation does not show cause. Two variables may both be driven by a third — wealthier areas have both better schools and better water.
Step by step
- Plot attendance against distance to school.
- Calculate Pearson and Spearman correlations.
- Remove an outlier and recalculate.
- Suggest a possible third variable.
Practice activity
Analyse the relationships between attendance and three factors with plots and correlations.
Expected result: Correct correlations with plots and cautious interpretation.
Check your understanding
-
When is Spearman correlation preferable?
Show answer
For monotonic but non-linear relationships or when outliers are present.
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Why can correlation not prove causation?
Show answer
A third factor may drive both variables, or the direction may be reversed.
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