Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inferen…
---
name: statsmodels
description: Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.9+ and statsmodels 0.14.6-compatible dependencies. Use `uv pip install statsmodels==0.14.6`; optional predictive-metric examples also need scikit-learn.
license: BSD-3-Clause license
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---
# Statsmodels: Statistical Modeling and Econometrics
## Overview
Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods. Apply this skill for rigorous statistical analysis, from simple linear regression to complex time series models and econometric analyses.
## Current Compatibility
Examples target statsmodels 0.14.6, released Dec 5, 2025. For reproducible environments, pin the primary package:
```bash
uv pip install statsmodels==0.14.6
```
Use `statsmodels.api` and `statsmodels.formula.api` for stable high-level imports, and direct module imports when examples require newer or specialized classes such as `HurdleCountModel`.
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