Statistical Model Selection & Validation Framework
Systematically select, validate, and document statistical models
What You Can Do
Claude guides you through a structured framework for evaluating statistical models, testing key assumptions, and documenting your methodology in reproducible format. You'll get clear decision criteria for choosing among candidate models, verification checklists for critical assumptions, and templates for documenting your analytical process so others can understand and replicate your results.
Features
evaluate multiple models across fit quality, interpretability, and assumption compliance
test normality, homoscedasticity, independence, and autocorrelation with formal statistical procedures
interpret diagnostic plots and identify when violations require model adjustment
establish train-test splits and k-fold validation with documented rationale
standardized format for recording model selection decisions and justification
ensure all random seeds, data versions, and hyperparameters are recorded
translate statistical test output into actionable insights for refinement
recommend specific transformations or model alternatives when assumptions fail
Example Output
Model Selection Matrix
| Criterion | Linear Regression | Polynomial (deg=2) | Ridge Regression |
|---|---|---|---|
| R² Score | 0.72 | 0.78 | 0.76 |
| AIC | 1245 | 1198 | 1210 |
| Normality (Shapiro-Wilk) | ✓ p=0.18 | ✓ p=0.42 | ✓ p=0.35 |
| Homoscedasticity (BP test) | ✗ p=0.03 | ✓ p=0.15 | ✓ p=0.12 |
| Overfitting Risk | Low | Medium | Low |
Diagnostics Interpretation
Your linear regression violates homoscedasticity (variance increases with fitted values). Next steps: (1) Apply log transformation to response, (2) Fit Ridge Regression to reduce overfitting, (3) Use heteroscedasticity-consistent standard errors for inference.
Reproducibility Record
- Random seed: 42
- Data version: train_v2.3.csv (SHA256: abc123...)
- CV method: 5-fold stratified
- All hyperparameters locked
- Test set: held-out, never touched during tuning
What's Included
- SKILL.md: Complete framework with model selection flowchart, assumption testing protocols, and decision trees
- Model Comparison Template: Pre-formatted matrix for evaluating candidate models side-by-side
- Assumption Validation Checklist: Step-by-step procedures for testing normality, homoscedasticity, independence, and linearity
- Residual Diagnostics Guide: Interpretation key for Q-Q plots, scale-location plots, and residual patterns
- Methodology Documentation Template: Structured record for capturing data source, preprocessing, rationale, and verification steps
- Reproducibility Audit Checklist: Verification steps for seeds, versions, hyperparameters, and data lineage
- Assumption Remediation Reference: Specific transformations and model alternatives for each common violation
Who It's For
- Data scientists comparing models in production workflows and selecting approaches before deployment
- Statisticians conducting formal analyses requiring transparent documentation for peer review or publication
- Quantitative analysts evaluating models for financial forecasting, pricing, or risk assessment
- Academic researchers publishing papers with explicit methodology and reproducibility requirements
- Business analysts choosing between predictive models for decision-making while avoiding overfitting
Best For
- Comparing multiple candidate models to select the most statistically appropriate one
- Validating that model assumptions hold before making predictions or inferences from results
- Documenting statistical methodology for peer review, publication, or regulatory compliance review
- Ensuring reproducibility by recording all random seeds, data versions, hyperparameters, and decisions
- Diagnosing model failures and selecting remedies when diagnostic tests reveal assumption violations
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