statsmodels

Perform statistical modeling and econometric analysis with OLS, GLM, ARIMA, and more.

3|Updated Apr 17, 2026

Search Tags:#statistical modeling #econometrics #statsmodels #time series analysis #linear regression

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill statsmodels-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/statsmodels
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill statsmodels-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides comprehensive statistical modeling and econometric analysis, enabling users to build and validate models across various statistical methods.

Core Features & Use Cases

Regression Analysis: Conduct linear and generalized linear regression, including OLS, WLS, GLS, quantile regression, and mixed effects models.
Time Series Analysis: Perform ARIMA, SARIMAX, VAR, and forecasting for time-ordered data.
Discrete Choice Models: Model categorical and count outcomes with Logit, Probit, MNLogit, and more.
Statistical Tests: Execute a variety of tests, including hypothesis testing, influence diagnostics, and model comparison.

Quick Start

Use the statsmodels skill to fit a linear regression model with the formula 'y ~ x1 + x2 + x1:x2' on the data in 'data.csv'.

Frequently Asked Questions about statsmodels

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

FAQPage Schema

How do I run linear regression with interaction terms on a CSV file?▼

You can fit an OLS linear regression using the formula 'y ~ x1 + x2 + x1:x2' directly on your CSV data. This enables rigorous inference and model validation across various statistical methods.

Can I perform time series forecasting with ARIMA and SARIMAX?▼

Yes, time series analysis is fully supported. You can employ ARIMA, SARIMAX, and VAR models to forecast time-ordered data, enabling comprehensive econometric modeling and analysis.

What statistical tests are available for econometric modeling?▼

The statistical modeling toolkit provides hypothesis testing, influence diagnostics, and model comparison. These tests ensure rigorous inference when validating econometric models.

Does this tool support generalized linear models and discrete choice models?▼

Yes, it supports GLM, Logit, Probit, and MNLogit for discrete choice modeling. These tools allow you to model categorical and count outcomes for econometric analysis.

When should I use mixed effects models instead of OLS?▼

Use mixed effects models instead of OLS when dealing with hierarchical or grouped data structures. The statistical modeling toolkit provides both methods, allowing rigorous inference across various research fields.