statsmodels

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For...

Statsmodels: Statistical Modeling and Econometrics

Detailed Guide

Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

When to Use This Skill

This skill should be used when:

Fitting regression models (OLS, WLS, GLS, quantile regression)
Performing generalized linear modeling (logistic, Poisson, Gamma, etc.)
Analyzing discrete outcomes (binary, multinomial, count, ordinal)
Conducting time series analysis (ARIMA, SARIMAX, VAR, forecasting)
Running statistical tests and diagnostics
Testing model assumptions (heteroskedasticity, autocorrelation, normality)
Detecting outliers and influential observations
Comparing models (AIC/BIC, likelihood ratio tests)
Estimating causal effects
Producing publication-ready statistical tables and inference

Limitations

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.