statsmodels

Fit linear, generalized linear, and time-series models with statsmodels.

Updated Jan 22, 2026

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

Ready to run

npx skills add https://github.com/tomlupo/ai-playground --skill statsmodels-tomlupo

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, statsmodels, and includes references (resource) components.

What problem does it solve?

This Skill provides a unified workflow for statistical modeling and diagnostics using statsmodels, enabling rigorous analysis and reproducible inference.

Core Features & Use Cases

OLS/GLS/GLM support: Build linear and generalized linear models with robust diagnostics and summaries.
Time-series analytics: Fit ARIMA, SARIMAX, VAR, and related models with forecasting and diagnostic plots.
Model evaluation: Information criteria, likelihood ratio tests, and cross-validation-style checks to compare models and validate assumptions.
Reference-guided workflows: Leverage included reference documents (linear_models, time_series, GLM) to guide model selection and interpretation.

Quick Start

Run a quick example: load a sample dataset, fit an OLS model with statsmodels, and print the summary.

Frequently Asked Questions about statsmodels

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

FAQPage Schema

How do I perform linear regression and run diagnostics using statsmodels in Python?▼

To perform linear regression with statsmodels, load your data into a pandas DataFrame, fit an OLS model, and print the summary to view regression diagnostics. This provides rigorous statistical inference and model evaluation results.

Can I fit ARIMA and SARIMAX time-series models for forecasting in Python?▼

Yes, you can fit ARIMA, SARIMAX, and VAR time-series models using statsmodels. The workflow supports multivariate time-series forecasting and includes diagnostic plots to validate model assumptions and analyze econometric data.

What is the best way to compare statistical models and validate assumptions?▼

The best way to compare statistical models is using information criteria, likelihood ratio tests, and cross-validation-style checks. These model evaluation tools help validate assumptions and select the most appropriate GLM or regression model.

Does this approach support generalized linear models and discrete choice analysis?▼

Yes, this approach supports generalized linear models (GLM) and discrete choice analysis. It provides comprehensive reference materials to guide model selection and interpretation for econometrics, finance, and social science applications.

Do I need numpy and pandas installed to run statsmodels workflows?▼

Yes, you need numpy and pandas installed as dependencies to run statsmodels workflows. These libraries provide the foundational data structures and array processing required for statistical modeling and reproducible inference.