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statsmodels

Performs statistical modeling, econometric analysis and time series forecasting with statsmodels.

2|Updated Jun 4, 2026
Search Tags:#statistics#regression#time-series#modeling#inference#econometrics
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill statsmodels-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/statsmodels
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill statsmodels-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill provides a comprehensive framework for performing advanced statistical analysis, econometrics, and time series forecasting, ensuring that your research and data modeling are mathematically sound and publication-ready.

Core Features & Use Cases

  • Statistical Modeling: Perform OLS, GLM, and mixed-effects regression with detailed diagnostic tables.
  • Time Series Analysis: Execute ARIMA, SARIMAX, and VAR models for forecasting and temporal dynamics.
  • Use Case: Use this skill to conduct a rigorous econometric study on time-series data, including stationarity testing, model fitting, and residual diagnostics to ensure valid inference.

Quick Start

Use the statsmodels skill to fit an OLS regression model to my dataset and generate a comprehensive summary of the results.

Frequently Asked Questions about statsmodels

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

FAQPage Schema
How do I fit an OLS regression model and generate a comprehensive summary of statistical results?▼

To fit OLS regression and generate a comprehensive summary, apply statistical modeling techniques that produce detailed coefficient inference and diagnostic tables for publication-ready results.

Can I run ARIMA and SARIMAX time series forecasting with stationarity testing in Python?▼

Yes, time series forecasting with ARIMA and SARIMAX supports stationarity testing, model fitting, and residual diagnostics to capture temporal dynamics and ensure valid inference.

Does this approach support econometric analysis with robust standard error estimation for discrete choice models?▼

Econometric analysis supports discrete choice modeling with robust standard error estimation, executing detailed coefficient inference and complex model specification for rigorous research workflows.

What is the best way to perform GLM and mixed-effects regression with diagnostic testing?▼

The best way to perform GLM and mixed-effects regression is using frameworks that execute diagnostic testing and detailed coefficient inference, ensuring mathematical soundness for research workflows.

Do I need pandas and numpy to execute time series modeling and econometric analysis?▼

Yes, executing time series modeling and econometric analysis requires pandas and numpy, alongside scipy and matplotlib, to handle data structures and mathematical operations for valid inference.

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