statsmodels

Name: statsmodels
Availability: InStock
Author: estrella-231

Fit regression, discrete outcome, and time series models with diagnostics.

Updated May 24, 2026

Search Tags:#python #time series #regression #econometrics #statsmodels #model diagnostics #robust inference

Ready to run

npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill statsmodels-estrella-231

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/statsmodels
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill statsmodels-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Statsmodels helps you build statistical models and produce trustworthy estimates, diagnostics, and publication-ready inference for regression, discrete outcomes, and time-series data.

Core Features & Use Cases

Flexible statistical modeling: Fit OLS/GLS/WLS, GLMs (Binomial/Poisson/Gamma, etc.), discrete choice models, and time-series models like ARIMA/SARIMAX.
Diagnostics and inference you can defend: Residual diagnostics, heteroskedasticity and autocorrelation checks, influence/outlier analysis, and robust standard errors.
Model comparison and forecasting: Use AIC/BIC and likelihood-based comparisons, then generate forecasts with confidence intervals and interval summaries.

Quick Start

Use the statsmodels skill to analyze the dataset you provide by fitting the appropriate regression model, running core diagnostics, and returning a summary of coefficients with uncertainty measures.

Frequently Asked Questions about statsmodels

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

FAQPage Schema

How do I run robust inference and check OLS regression assumptions in Python?▼

Run robust inference in Python by fitting OLS regression models and applying residual diagnostics, heteroskedasticity checks, and robust standard errors to validate assumptions and quantify uncertainty.

Can I fit time series models and generate ARIMA forecasts with confidence intervals?▼

Fit time series models like ARIMA and SARIMAX to generate forecasts with confidence intervals, applying model comparison metrics like AIC and BIC alongside interval summaries for rigorous time series forecasting.

What's the best way to model discrete outcomes and count data for econometrics?▼

Model discrete outcomes and count data for econometrics by fitting GLMs like Binomial or Poisson, running core diagnostics, and returning coefficient summaries with uncertainty measures for publication-ready results.

Does this approach support influence analysis and outlier detection for statistical models?▼

This approach supports influence analysis and outlier detection for statistical models by applying residual diagnostics and influence measures to identify impactful observations during model fitting and inference checks.

How do I compare statistical models using likelihood-based metrics?▼

Compare statistical models using likelihood-based metrics by calculating AIC and BIC values across fitted regression or discrete choice models to evaluate fit and select the most appropriate specification.