Correlation Analysis
Compute and interpret correlations: Pearson, Spearman, Kendall for numeric data, Cramér's V and point-biserial for categorical. Detect multicollinearity and identify meaningful feature relationships. Use when exploring feature relationships or checking for multicollinearity before modeling.
Install
$ agentstack add skill-ericwang915-data-scientist-skills-correlation-analysis ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Correlation Analysis
Purpose
Identify and interpret relationships between variables. Detects multicollinearity, ranks feature importance by association, and visualizes the correlation structure.
How It Works
Step 1: Choose Correlation Method
- Pearson: Linear relationships between continuous variables
- Spearman: Monotonic relationships (robust to outliers and non-linearity)
- Kendall: Ordinal data or small sample sizes
- Cramér's V: Association between categorical variables
- Point-biserial: Continuous vs. binary variable
Step 2: Compute Correlation Matrix
- Full pairwise correlation with p-values
- Highlight significant correlations (p 0.7) and multicollinearity (|r| > 0.8)
Step 3: Advanced Analysis
- Partial correlations: Control for confounding variables
- VIF (Variance Inflation Factor): Quantify multicollinearity severity
- Correlation clusters: Group variables that move together (hierarchical clustering on correlations)
Step 4: Visualization
- Annotated heatmap with hierarchical clustering
- Pair plots for top correlated pairs
- Network graph for complex correlation structures
Step 5: Recommendations
- Which correlated features to drop or combine before modeling
- Features most associated with the target variable
- Unexpected correlations worth investigating
Usage Examples
"Which features are most correlated with customer churn?
Flag any multicollinearity issues.""Create a correlation matrix for all numeric features, and
tell me which pairs I should investigate further"Output Format
- Correlation Matrix: Annotated heatmap with significance markers
- Top Pairs: Ranked list of strongest correlations with interpretation
- Multicollinearity Report: VIF scores with drop recommendations
- Target Correlations: Features ranked by association with target
- Python Code: Reproducible analysis script
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ericwang915
- Source: ericwang915/data-scientist-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.