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Free online correlation calculator for Pearson and Spearman coefficients. Get R², p-value significance test, interactive scatter plot with trend line, step-by-step KaTeX formulas, and Python scipy export.
Compute Pearson or Spearman correlation with significance testing.
Correlation measures the strength and direction of the relationship between two variables. The correlation coefficient (r) ranges from −1 to +1.
As X increases, Y tends to increase. Example: study hours and exam scores.
As X increases, Y tends to decrease. Example: price and demand.
No linear relationship between X and Y. Points are scattered randomly.
| Property | Pearson (r) | Spearman (ρ) |
|---|---|---|
| Measures | Linear relationship | Monotonic relationship |
| Data type | Continuous, interval/ratio | Ordinal or continuous |
| Assumptions | Normal distribution, no outliers | No distribution assumption |
| Outlier sensitivity | High — easily distorted | Low — uses ranks |
| Best for | Linear, well-behaved data | Ranked data, curves, outliers |
| |r| Value | Strength | Meaning |
|---|---|---|
| 0.8 – 1.0 | Very Strong | Highly predictive relationship |
| 0.6 – 0.79 | Strong | Notable, meaningful relationship |
| 0.4 – 0.59 | Moderate | Clear but not dominant |
| 0.2 – 0.39 | Weak | Minor relationship |
| 0.0 – 0.19 | Very Weak | Little to no relationship |
Correlation ≠ Causation: A strong correlation does not prove that one variable causes the other. There may be confounding variables, reverse causation, or coincidence. Always consider context.