alterlab-statsmodels

Configure StatsModels linear, GLM, discrete, and time-series models with diagnostics.

58|9|Updated Mar 16, 2026

Search Tags:#diagnostics #statistics #time-series #model-selection #glm #statsmodels #linear-regression

Ready to run

npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-statsmodels

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: alterlab-statsmodels
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/data-science/alterlab-statsmodels
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-statsmodels

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Statsmodels provides a comprehensive Python toolkit for statistical modeling, estimation, and inference, enabling researchers to specify and diagnose a wide range of models from linear regression to time-series.

Core Features & Use Cases

OLS, WLS, GLS, GLSAR, Quantile Regression, and Mixed Effects for continuous outcomes with diagnostics.
Generalized Linear Models (Binomial, Poisson, Gamma, etc.), discrete choice, and time-series models with forecasting and diagnostics.
Time Series analysis (ARIMA, SARIMAX, VAR, Exponential Smoothing) with forecasting, residual analysis, and model evaluation.
Model selection, hypothesis testing, and robust covariance matrices; comprehensive reference materials in references/ for deeper topics.

Quick Start

Fit your first model (e.g., OLS) using statsmodels with a few lines, then inspect the summary and residuals.

Frequently Asked Questions about alterlab-statsmodels

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

FAQPage Schema

Can I use StatsModels for time-series forecasting and ARIMA models?▼

Yes, StatsModels supports time-series forecasting by fitting ARIMA, SARIMAX, VAR, and Exponential Smoothing models to your data, generating forecasts and residual diagnostics for evaluation.

What is the best way to fit a Generalized Linear Model for count data?▼

The best way to fit a Generalized Linear Model for count data is using StatsModels to configure a Poisson GLM, ensuring thorough model validation with residual analysis and goodness-of-fit checks.

Does StatsModels support robust standard errors and model selection?▼

Yes, StatsModels supports robust standard errors and model selection by applying robust covariance matrices and hypothesis testing to ensure interpretable results across various datasets.

When should I use Quantile Regression instead of OLS in StatsModels?▼

Use Quantile Regression instead of OLS when your continuous outcomes violate OLS assumptions, allowing you to fit models and generate diagnostics without relying on mean-based estimation.