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statsmodels

Model relationships in data using statsmodels linear, GLM, and time-series tools.

1|Updated Mar 11, 2026
Search Tags:#diagnostics#time-series#econometrics#glm#linear-regression#statistical-modeling
One-click install
npx skills add https://github.com/SciMate-AI/scicli --skill statsmodels-scimate-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/SciMate-AI/scicli/tree/main/internal/skills/bundled/claude-scientific-skills/skills/statsmodels
Command: npx skills add https://github.com/SciMate-AI/scicli --skill statsmodels-scimate-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Statsmodels provides a comprehensive Python library for statistical modeling, estimation, inference, and diagnostics across a wide range of methods, enabling rigorous data analysis and reproducible research.

Core Features & Use Cases

  • Linear and generalized linear models (OLS, WLS, GLS, GLM, Quantile Regression) for inference on continuous outcomes.
  • Time-series and discrete-choice capabilities (ARIMA, VAR, Logit/Probit, Poisson, NB) with diagnostics and model selection.
  • Diagnostic tools, robust standard errors, and model comparison to validate assumptions and report results.

Quick Start

Run a simple OLS example to estimate a model and view a 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 linear regression and get a statistical summary?▼

You can perform OLS linear regression by fitting your data to the model, which returns parameter estimates, hypothesis testing results, and a visualization-ready statistical summary.

Can I fit time-series models like ARIMA and VAR for econometrics?▼

Yes, time-series modeling supports ARIMA and VAR for econometrics workflows, enabling parameter estimation, residual diagnostics, and model selection using information criteria to validate temporal relationships.

What statistical models are available for discrete choice and count data?▼

Discrete choice and count data modeling includes Logit, Probit, Poisson, and Negative Binomial models, providing parameter estimation and robust standard errors for inference on categorical outcomes.

How do residual diagnostics and robust standard errors validate model assumptions?▼

Residual diagnostics and robust standard errors validate model assumptions by testing error terms, ensuring accurate parameter estimation and reliable hypothesis testing in statistical modeling workflows.

Does this support Generalized Linear Models (GLM) and Weighted Least Squares (WLS)?▼

Yes, Generalized Linear Models (GLM), Weighted Least Squares (WLS), and Generalized Least Squares (GLS) are supported for comprehensive statistical modeling, parameter estimation, and diagnostics on continuous outcomes.

When should I use robust standard errors and model comparison for econometrics?▼

Use robust standard errors and model comparison when validating assumptions and reporting results in econometrics, ensuring reliable inference through diagnostic tools and information criteria across statistical models.

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