statsmodels

Name: statsmodels
Availability: InStock
Author: vini

Fit and evaluate statistical models in Python using Statsmodels.

Updated Apr 2, 2026

Search Tags:#python #diagnostics #regression #time-series #inference #statistical-models

Ready to run

npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill statsmodels-viniruggeri

💬 Prompt for ChatGPT / Claude Chat▼

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.