📈 tools.correlationCoefficientCalculator.name
tools.correlationCoefficientCalculator.description
📖 Correlation Types
Pearson (r): Measures linear relationships. Best for normally distributed data.
Spearman (ρ): Measures monotonic relationships using ranks. Robust to outliers.
Kendall (τ): Alternative rank-based measure. More stable for small samples.
Interpretation:
- ±0.9+: Very Strong
- ±0.7-0.9: Strong
- ±0.5-0.7: Moderate
- ±0.3-0.5: Weak
- ±0.1-0.3: Very Weak
📚 Statistical Notes
Pearson Correlation: Assumes linear relationship and normally distributed variables. Sensitive to outliers.
Spearman Correlation: Non-parametric, works with any monotonic relationship. Good for ordinal data.
Kendall Tau: More robust to outliers than Spearman, preferred for small samples or when ties are present.
P-values: Approximate values for hypothesis testing. Lower p-values indicate stronger evidence against null hypothesis (no correlation).