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statsmodels

Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.

Published by sickn33 ↗sickn33/agentic-awesome-skills

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statsmodels

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Use statsmodels to analyze [dataset] to model [continuous outcome] from [predictors], delivering an interpretable regression summary with confidence intervals, assumption diagnostics, and out-of-sample validation.

Required inputs

  • [dataset]
  • [continuous outcome]
  • [predictors]

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SKILL.md

name: statsmodelsdescription: "Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods."license: BSD-3-Clause licensemetadata:     skill-author: K-Dense Inc.risk: safesource: communitydate_added: "2026-03-06"

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.