How to Use the Correlation Calculator

Enter your data points with x and y values, one pair per line. Choose the correlation type: Pearson for linear relationships, Spearman for monotonic relationships, or Matrix for multiple variables.

Correlation Types

Pearson Correlation (r)

Measures the linear relationship between two continuous variables:

Range: -1 to +1
r = 1: Perfect positive linear relationship
r = -1: Perfect negative linear relationship
r = 0: No linear relationship
Assumes: Linear relationship, normally distributed data

Spearman Correlation (ρ)

Measures monotonic (consistently increasing or decreasing) relationships:

Uses ranks instead of raw values
Robust to outliers
Doesn't assume linear relationship
Works with ordinal data

Correlation Matrix

Shows pairwise correlations between multiple variables at once. Useful for exploring relationships in datasets with many variables.

Interpreting Correlation Strength

0.9 to 1.0: Very strong
0.7 to 0.9: Strong
0.5 to 0.7: Moderate
0.3 to 0.5: Weak
0.0 to 0.3: Very weak or negligible

Common Applications

Medical research (symptom relationships)
Psychology (variable associations)
Finance (asset correlations, portfolio analysis)
Marketing (consumer behavior patterns)
Education (test score relationships)
Quality control (process variables)

Important Considerations

Correlation ≠ Causation: A high correlation doesn't prove one variable causes the other
Outliers: Can heavily influence Pearson r; consider Spearman for robustness
Sample Size: Larger samples give more reliable estimates
Non-linear relationships: Pearson may miss curved relationships

Statistical Significance

The p-value indicates whether the correlation is statistically significant. A p-value less than 0.05 typically means the correlation is unlikely to be due to chance alone.

Correlation Calculator

rCorrelation Calculator