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statsmodels

Fit OLS, GLM, ARIMA, and VAR models with diagnostics and inference.

1|2|Updated Apr 29, 2026
Search Tags:#diagnostics#statistics#time-series#econometrics#hypothesis-testing#statsmodels#linear-models
One-click install
npx skills add https://github.com/fuzzy-dynamics/strings --skill statsmodels-fuzzy-dynamics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/fuzzy-dynamics/strings/tree/main/packages/skills/statsmodels
Command: npx skills add https://github.com/fuzzy-dynamics/strings --skill statsmodels-fuzzy-dynamics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This library provides a comprehensive toolkit for fitting a wide range of statistical models (OLS, GLM, mixed effects, ARIMA, and more) and producing rigorous diagnostics, inference, and results interpretation, helping researchers avoid ad-hoc calculations and misinterpretations.

Core Features & Use Cases

  • Model fitting across OLS, WLS, GLS, GLM, ARIMA, VAR, MixedLM, QuantReg, and more.
  • Diagnostics and inference: standard errors, p-values, confidence intervals, residual analysis, influence measures, and hypothesis tests.
  • Model selection and comparison: AIC/BIC, likelihood ratio tests, cross-validation options, and information criteria.
  • Time-series econometrics and forecasting: Granger causality, impulse responses, and dynamic factor modeling (where supported).

Quick Start

Fit a simple OLS model on your dataset and review the summary to interpret coefficients and diagnostics.

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 interpret hypothesis testing results?▼

Run OLS regression by fitting your dataset to a linear model, then review the summary output to interpret coefficients, p-values, standard errors, and confidence intervals for hypothesis testing.

Can I fit time-series models like ARIMA and perform Granger causality tests?▼

Yes, you can fit time-series models like ARIMA and VAR to perform forecasting, Granger causality tests, and impulse response analysis for econometric and policy analysis tasks.

What's the best way to compare statistical models using AIC and likelihood ratio tests?▼

Compare statistical models by evaluating AIC/BIC information criteria and conducting likelihood ratio tests to determine the best fit for your econometric and social science research data.

Does this approach support generalized linear models and robust standard errors?▼

Yes, this approach supports fitting generalized linear models (GLM), mixed effects models, and quantile regression, providing comprehensive diagnostics and calculating robust standard errors.

What diagnostic checks are available for residual analysis and influence measures?▼

Available diagnostic checks include residual analysis, influence measures, and comprehensive inference outputs to validate model assumptions and detect outliers in your statistical modeling.

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