Correlation Data Analyzer

📈 Correlation Analyzer Pro Pearson Spearman Kendall Heatmap

Advanced correlation analysis - Pearson, Spearman, Kendall tau with interactive visualizations

📊 Pearson Correlation

📈 Spearman Rank

🔄 Kendall Tau

🎨 Correlation Heatmap

📉 Scatter Plot

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Data Input

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Correlation Results

Correlation Coefficient (r)
--

--

📈 Statistical Details
Sample Size (n): --
P-value: --
Confidence: --
Interpretation: --

📐 Regression Equation
y = --
R²: --

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Scatter Plot with Regression Line

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Correlation Heatmap

📊 Correlation Interpretation
• +1: Perfect positive
• +0.7 to +0.9: Strong positive
• +0.3 to +0.6: Moderate positive
• 0 to ±0.2: Weak/No correlation
• -0.3 to -0.6: Moderate negative
• -0.7 to -0.9: Strong negative
• -1: Perfect negative

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Correlation Types Explained

📊 Pearson Correlation (r)
Measures linear relationship between continuous variables.
Assumes normal distribution and homoscedasticity.
Range: -1 to +1

📈 Spearman Rank Correlation (ρ)
Non-parametric measure of monotonic relationship.
Works with ordinal data or when assumptions violated.
Based on ranked values.

🔄 Kendall Tau Correlation (τ)
Non-parametric measure of ordinal association.
Based on concordant/discordant pairs.
More robust for small samples.

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Quick Tips

✅ When to use:
• Pearson: Linear, normally distributed
• Spearman: Monotonic, ordinal data
• Kendall: Small samples, tied ranks

📖 Interpretation:
• |r| > 0.7: Strong correlation
• 0.3 < |r| < 0.7: Moderate
• |r| < 0.3: Weak
• Sign indicates direction

⚠️ Cautions:
• Correlation ≠ Causation
• Outliers affect results
• Check assumptions first

📈 Correlation Analyzer Pro — Professional correlation analysis with multiple methods

Correlation Analyzer Pro — Advanced statistical tool for Pearson, Spearman, and Kendall Tau correlation analysis with interactive visualizations.