statsmodels

Fit OLS regression models and run statistical tests in Python.

Updated Mar 10, 2026
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill statsmodels-felixboehm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/statsmodels
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill statsmodels-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive suite of tools for statistical modeling, estimation, inference, and diagnostics, enabling users to perform rigorous quantitative analysis.

Core Features & Use Cases

  • Regression Analysis: Fit OLS, WLS, GLS, Quantile Regression, Mixed Effects models.
  • Generalized Linear Models: Handle non-normal outcomes (Binomial, Poisson, Gamma, etc.).
  • Time Series Analysis: ARIMA, SARIMAX, VAR, forecasting, and diagnostics.
  • Statistical Tests: Perform hypothesis tests, diagnostic checks (normality, heteroskedasticity, autocorrelation), and influence analysis.
  • Use Case: Analyze economic data to build a time series forecast for GDP, including confidence intervals and diagnostic checks for model validity.

Quick Start

Use the statsmodels skill to fit an OLS regression model with y as the dependent variable and x1, x2 as independent variables.

Frequently Asked Questions about statsmodels

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

FAQPage Schema
How do I perform time series forecasting and diagnostic checks in Python?

Time series forecasting uses models like ARIMA, SARIMAX, and VAR to project future values and run diagnostic checks. This Skill enables fitting these models in Python, providing predictions alongside validity tests for autocorrelation and normality.

What's the best way to run OLS regression and hypothesis testing on economic data?

OLS regression and hypothesis testing on economic data are best handled through econometric modeling. This Skill fits OLS, WLS, and GLS models, enabling precise statistical inference and influence analysis for data-driven decision-making.

Can I model non-normal outcomes using generalized linear models for statistical inference?

Generalized linear models handle non-normal outcomes like Binomial, Poisson, and Gamma distributions for statistical inference. This Skill supports fitting these models to ensure robust estimation and accurate diagnostic checks.

Does this approach support discrete choice modeling and quantile regression?

Discrete choice modeling and quantile regression are supported for advanced statistical modeling. This Skill enables fitting these models alongside mixed effects and generalized linear models to solve complex econometric problems.

When do I need model diagnostics and robust estimation techniques for forecasting?

Model diagnostics and robust estimation techniques are needed when forecasting requires validating model assumptions and handling data irregularities. This Skill provides tests for heteroskedasticity and normality to ensure statistical inference reliability.