statsmodels

Name: statsmodels
Availability: InStock
Author: shushuzn

Fit OLS, GLM, discrete-choice, and time-series models with diagnostics and forecasts.

4|Updated Mar 2, 2026

Search Tags:#hypothesis testing #regression #econometrics #model diagnostics #time series forecasting #robust standard errors #Python statsmodels

Ready to run

npx skills add https://github.com/shushuzn/Rairos --skill statsmodels-shushuzn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/shushuzn/Rairos/tree/main/skills/statsmodels
Command: npx skills add https://github.com/shushuzn/Rairos --skill statsmodels-shushuzn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you choose the right statistical model and produce publication-ready inference, diagnostics, and forecasts without manually stitching together many ad-hoc steps.

Core Features & Use Cases

Statistical modeling & inference: Fit OLS/GLM/discrete-choice/time-series models and interpret coefficients with confidence intervals and hypothesis tests.
Diagnostics & validation: Test assumptions (heteroskedasticity, autocorrelation, normality/specification) and check influence/outliers to trust results.
Model comparison & forecasting: Compare models via AIC/BIC or likelihood-ratio logic and generate forecasts with prediction intervals for time-ordered data.

Quick Start

Use the statsmodels skill to fit an OLS model and return a summary with coefficients, p-values, R-squared, and residual diagnostics for your dataset.

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 with robust standard errors and assumption checks?▼

Fit an OLS regression with robust standard errors and assumption checks by using built-in statistical APIs to estimate coefficients, calculate p-values, and run heteroskedasticity or normality diagnostics on your structured dataset.

What is the best way to generate time series forecasts with prediction intervals?▼

Generate time series forecasts with prediction intervals by fitting time-ordered models that provide robust estimation, standardized diagnostic testing, and future prediction uncertainty using established econometric APIs.

Can I use this for binary or count outcome modeling and causal-style inference?▼

Binary, count, and ordinal outcome modeling is fully supported for causal-style inference workflows, providing coefficient interpretation alongside confidence intervals and hypothesis testing for rigorous econometric analysis.

How do I compare multiple statistical models using AIC, BIC, or likelihood ratio tests?▼

Compare multiple statistical models using AIC, BIC, or likelihood-ratio logic to evaluate relative fit, guiding model selection by balancing statistical complexity against explanatory power for your structured data.

When should I check for autocorrelation and influence outliers in my regression model?▼

Check for autocorrelation and influence outliers during model diagnostics to validate underlying assumptions, ensuring your inference results are trustworthy before relying on the coefficients for publication-ready reporting.

Does this approach work for econometrics workflows requiring publication-ready inference?▼

This approach works for econometrics workflows requiring publication-ready inference by providing model selection guidance, robust estimation options, and standardized diagnostic testing without manually stitching together ad-hoc analytical steps.