statsmodels

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

Updated Feb 22, 2026
One-click install
npx skills add https://github.com/jobu711/options_arena --skill statsmodels-jobu711
Or copy as Structured Prompt for Agent
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.