Claude AI Skill: Statistical Modeling Workflow & Diagnostics
Build and diagnose statistical models with structured workflows
What You Can Do
You can develop, validate, and troubleshoot statistical models using Claude's guided workflow approach. This skill walks you through model selection, assumption testing, diagnostic analysis, and interpretation of results. You'll receive step-by-step guidance for fitting models appropriately to your data and identifying potential issues before deployment.
Features
Choose between regression, classification, time series, and hierarchical models based on your data characteristics and research questions
Systematically verify linearity, normality, homoscedasticity, independence, and multicollinearity assumptions
Generate and interpret residual plots, Q-Q plots, leverage plots, and other diagnostic visualizations
Translate model outputs (coefficients, p-values, confidence intervals) into actionable insights
Validate data quality, detect outliers, check for missing patterns, and assess sample size adequacy
Evaluate competing models using AIC, BIC, cross-validation, and other fit statistics
Diagnose and resolve common issues like convergence failures, multicollinearity, and overfitting
Example Output
Example 1: Linear Regression Workflow
Model Selection → Data Preparation → Fit OLS Regression → Check Assumptions
✓ Linearity assumption met (plot visual inspection)
✗ Normality concern (Q-Q plot shows heavy tails)
→ Recommendation: Log-transform response variable
Diagnostics After Transformation:
✓ Linearity: Confirmed
✓ Normality: Improved (KS test p=0.087)
✓ Homoscedasticity: Confirmed (Breusch-Pagan p=0.34)
→ Model interpretation ready
Example 2: Model Comparison
Candidates: OLS, Ridge, Elastic Net (λ = 0.1)
| Model | AIC | BIC | CV RMSE | Condition # |
|-------|-----|-----|---------|-------------|
| OLS | 542 | 551 | 2.14 | 45 |
| Ridge | 539 | 550 | 2.08 | 8 ✓ |
| Elastic | 541 | 554 | 2.11 | 12 |
Recommendation: Ridge regression (best CV performance, improved stability)
What's Included
- SKILL.md: Complete workflow instructions for statistical modeling and diagnostics
- Assumption Testing Checklist: Structured checklist for validating model assumptions
- Diagnostic Interpretation Guide: Reference sheet for reading and acting on diagnostic plots
- Model Selection Decision Tree: Flowchart to identify appropriate model class for your problem
- Template: Regression Workflow: Step-by-step template for building and validating regression models
- Template: Model Comparison Matrix: Pre-built template for evaluating multiple candidate models
- Troubleshooting Reference: Common statistical issues and resolution strategies
Who It's For
- Data scientists developing predictive and explanatory models
- Statisticians conducting research and publishing analyses
- Quantitative analysts building risk and return models
- Researchers in social, behavioral, and biological sciences
- Business analysts performing statistical investigations and forecasting
Best For
- Building regression, classification, or time series models
- Validating that model assumptions are satisfied
- Diagnosing model problems (overfitting, multicollinearity, non-normality)
- Comparing competing statistical models
- Interpreting statistical results for non-technical stakeholders
- Troubleshooting convergence, estimation, or performance issues
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