Agent skill · data analytics · aj-geddes
Correlation Analysis
Measure relationships between variables using correlation coefficients, correlation matrices, and association tests for correlation measurement, relationship analysis, and multicollinearity detection
Why this skill is useful
Provides specific implementation details and Python scripts for calculating various correlation coefficients and visualizing results that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, scipy, seaborn installed locally. About 3k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Correlation Analysis Overview Correlation analysis measures the strength and direction of relationships between variables, helping identify which features are related and detect multicollinearity. When to Use Identifying relationships between numerical variables Detecting multicollinearity before regression modeling Exploratory data analysis to understand feature dependencies Feature selection and dimensionality reduction Validating assumptions about variable relationships Comparing linear and non-linear associations Correlation Types Pearson: Linear correlation (continuous variables) Spearman: Rank-based correlation (ordinal/non-linear) Kendall: Rank correlation (robust alternative) Cramér's V: Association for categorical variables Mutual Information: Non-linear dependencies Key Concepts Correlation Coefficient: Ranges from -1 to +1 Positive Correlation: Variables move together Negative Correlation: Variables move oppositely Multicollinearity: High correlations between predictors Implementation with Python Interpretation Guidelines r = 0.0-0.3: Weak correlation r = 0.3-0.7: Moderate correlation r = 0.7-1.0: Strong correlation p < 0.05: Statistically significant High VIF (>10): Multicollinearity problem Important Notes Correlation ≠ Causation Non-linear relationships missed by Pearson Outliers can distort correlations Sample size affects significance Temporal trends can create spurious correlations Visualization Strategies Heatmaps for overview Scatter plots for relationships Pair plots for multivariate analysis Rolling correlations for time-varying relationships Deliverables Correlation matrices (Pearson, Spearman) Correlation heatmaps with annotations Statistical significance table Scatter plots with regression lines Multicollinearity assessment (VIF) Partial correlation analysis Relationship interpretation report
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/correlation-analysis