# Statsmodels

> Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.

- Skill: `newmindsgroup/statsmodels` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add newmindsgroup/statsmodels`
- Raw SKILL.md: https://api.skillmd.com/api/skills/newmindsgroup/statsmodels/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: newmindsgroup (https://skillmd.com/u/newmindsgroup)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/newmindsgroup/statsmodels

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# Statsmodels: Statistical Modeling and Econometrics

Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.

## When to Use
- The request matches the skill description: Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.
- The task needs the implementation patterns, examples, validation checks, or edge cases listed in the topic map.
- The work would benefit from the complete guidance preserved in `references/full-guidance.md`.

## Core Workflow
1. Confirm the request matches this skill's trigger, scope, and risk profile.
2. Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.
3. Load `references/full-guidance.md` when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.
4. Apply only the relevant guidance instead of loading or repeating the entire reference by default.
5. Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.

## Topic Map
- Overview
- When to Use This Skill
- Quick Start Guide
- Linear Regression (OLS)
- Logistic Regression (Binary Outcomes)
- Time Series (ARIMA)
- Generalized Linear Models (GLM)
- Core Statistical Modeling Capabilities
- Linear Regression Models
- Discrete Choice Models
- Time Series Analysis
- Statistical Tests and Diagnostics
- Formula API (R-style)
- Model Selection and Comparison
- Information Criteria
- Likelihood Ratio Test (Nested Models)
- Cross-Validation
- Best Practices

## Reference Map
- `references/full-guidance.md` preserves the complete original guidance, including examples and detailed edge cases.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

## Progressive Loading
Keep this `SKILL.md` as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.