statsmodels

Perform statistical modeling and inference with Python's Statsmodels library.

Updated Feb 22, 2026

Search Tags:#diagnostics #statistics #regression #time-series #model-selection #statsmodels

Ready to run

npx skills add https://github.com/jobu711/options_arena --skill statsmodels-jobu711

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/jobu711/options_arena/tree/main/.claude/skills/statsmodels
Command: npx skills add https://github.com/jobu711/options_arena --skill statsmodels-jobu711

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provide rigorous statistical modeling and inference in Python using a mature, well-documented library that covers regression, GLMs, time-series, and diagnostic tools.

Core Features & Use Cases

OLS, GLS, GLM, ARIMA, VAR, and MixedLM for a wide range of regression and time-series tasks.
Formula API and diagnostics for robust inference, model selection, and hypothesis testing.
Multivariate and state-space capabilities, with extensive documentation and examples.

Quick Start

Run a simple OLS example on your dataset to obtain a quick regression summary.

Frequently Asked Questions about statsmodels

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run OLS regression and get a statistical summary in Python?▼

To run OLS regression in Python, you can apply the formula API to your dataset and generate a quick regression summary with full diagnostics and inference statistics.

What statistical models are available for time-series analysis in Python?▼

Time-series analysis in Python supports ARIMA and VAR models, alongside state-space capabilities, providing robust inference and multivariate forecasting for economic and financial data.

Does this approach support GLMs and mixed linear models for hypothesis testing?▼

Yes, it supports GLMs and MixedLM for hypothesis testing, offering extensive diagnostics and model selection capabilities to ensure rigorous statistical inference across social science datasets.

How do I perform model selection and diagnostics for generalized linear models?▼

Perform model selection and diagnostics for GLMs using the formula API, which provides extensive diagnostic tools and hypothesis testing to validate model assumptions and robustness.

When should I use statsmodels instead of other statistical modeling libraries?▼

Use this approach when you need rigorous inference, mature documentation, and a wide range of models like OLS, GLS, and discrete choice, rather than just predictive accuracy.

Can I apply discrete choice modeling for economics and social science datasets?▼

Yes, you can apply discrete choice modeling to economics and social science datasets, leveraging rigorous statistical inference and formula API for robust hypothesis testing.