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statsmodels

Fit and evaluate statistical models in Python using Statsmodels.

Updated Apr 2, 2026
Search Tags:#python#diagnostics#regression#time-series#inference#statistical-models
One-click install
npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill statsmodels-viniruggeri
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/viniruggeri/applied-dynamical-systems/tree/main/.agents/skills/statsmodels
Command: npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill statsmodels-viniruggeri

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Statsmodels provides a comprehensive toolkit for statistical modeling, estimation, inference, and diagnostics in Python, enabling rigorous analysis beyond basic regression.

Core Features & Use Cases

  • OLS, GLM, and mixed-effects modeling for regression and inference
  • Time-series methods (ARIMA, state-space, forecasting) and seasonality analysis
  • Generalized linear models, discrete choice models, and formula API
  • Extensive diagnostic tests, robust standard errors, and hypothesis testing
  • Reproducible workflows for econometrics, finance, and social sciences

Quick Start

Estimate a simple OLS model and inspect results with 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 robust standard errors in Python?▼

Run OLS regression in Python using the formula API and summary() to inspect results. The Skill supports fitting models with robust standard errors and extensive diagnostic tests for inference.

Can I use Python for time-series forecasting and seasonality analysis?▼

Time-series forecasting and seasonality analysis in Python are supported through ARIMA and state-space methods. The Skill fits these statistical models to enable econometric and financial predictions.

What statistical models are available for econometrics and social science applications?▼

Statistical models for econometrics and social sciences include generalized linear models, discrete choice models, and mixed-effects modeling. These support reproducible workflows for rigorous inference and hypothesis testing.

Does Python support a formula API for generalized linear models and mixed-effects modeling?▼

The formula API in Python fully supports generalized linear models and mixed-effects modeling. It enables fitting and evaluating statistical models with comprehensive diagnostic tests and robust standard errors.

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