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statsmodels

Perform statistical modeling and econometric analyses with Python libraries.

Updated May 8, 2026
Search Tags:#time series#statistics#regression#econometrics#generalized linear models
One-click install
npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill statsmodels-zeyuyang-0420
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/ml-training-ai/statsmodels
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill statsmodels-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires statsmodels, numpy, pandas, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides comprehensive statistical modeling and econometrics capabilities in Python, enabling users to perform a wide range of statistical analyses, including regression, GLM, time series, and more.

Core Features & Use Cases

  • Statistical Modeling: Offers a suite of linear models, GLMs, discrete choice models, and time series models for various data types and outcomes.
  • Econometrics: Provides tools for estimation, inference, and diagnostics across a wide range of statistical methods, including regression, GLM, and time series analysis.
  • Use Case: Imagine you have a dataset with multiple predictor variables and a binary outcome. Use this Skill to fit a logistic regression model and interpret the results.

Quick Start

Use the statsmodels skill to fit a logistic regression model to predict whether a customer will churn or not based on their features.

Frequently Asked Questions about statsmodels

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

FAQPage Schema
How do I perform econometric analysis and regression on a dataset in Python?▼

This Skill enables econometric analysis and regression in Python by fitting linear models, generalized linear models, and discrete choice models on numpy and pandas datasets.

Can I use pandas DataFrames to fit generalized linear models for a binary outcome?▼

You can fit generalized linear models for a binary outcome using pandas DataFrames, applying logistic regression to predict customer churn based on feature variables.

What Python libraries do I need to run time series statistics and diagnostics?▼

Running time series statistics and diagnostics requires numpy, pandas, scipy, and statsmodels installed in your Python environment to perform estimation and inference.

What is the best way to run statistical diagnostics and inference on time series data?▼

Running statistical diagnostics and inference on time series data is best achieved using this Skill, which applies statsmodels to deliver estimation, inference, and diagnostic tools.

Does this approach work for both discrete choice models and linear regression?▼

This approach works for both discrete choice models and linear regression, providing a suite of statistical methods for various data types and outcomes across econometrics.

When should I not use statsmodels for my statistical modeling tasks?▼

Avoid using statsmodels for statistical modeling if your workflow lacks numpy, pandas, or scipy dependencies, as these are strictly required to perform the various statistical methods.

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