statsmodels

Fit OLS, GLM, ARIMA, and VAR statistical models in Python.

Updated Mar 10, 2026

Search Tags:#time series #hypothesis testing #statistics #regression #inference #econometrics

Ready to run

npx skills add https://github.com/Yezez9/Research-Agent --skill statsmodels-yezez9

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/statsmodels
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill statsmodels-yezez9

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for statistical modeling, enabling users to perform complex analyses, test hypotheses, and generate robust statistical inference for research and data analysis.

Core Features & Use Cases

Model Fitting: Supports OLS, GLM, ARIMA, VAR, and many other statistical models.
Hypothesis Testing: Includes a wide array of statistical tests for assumptions, significance, and comparisons.
Diagnostics: Offers tools for residual analysis, influence detection, and model validation.
Use Case: Analyze economic time series data using ARIMA to forecast future trends, or fit a logistic regression model to predict customer churn based on demographic and behavioral features.

Quick Start

Fit an OLS linear regression model using the provided y and X data.

Frequently Asked Questions about statsmodels

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

FAQPage Schema

How do I perform time series forecasting with ARIMA in Python?▼

Time series forecasting with ARIMA involves fitting an autoregressive integrated moving average model to your sequential data. This Skill supports ARIMA model fitting to analyze economic trends and generate robust future forecasts.

What statistical tests are available for hypothesis testing and model diagnostics?▼

Hypothesis testing and model diagnostics include a wide array of statistical tests for assumptions, significance, and comparisons. You can perform residual analysis and influence detection to validate model assumptions.

Can I fit a logistic regression model to predict customer churn using this approach?▼

Yes, you can fit a logistic regression model to predict customer churn. This Skill supports Generalized Linear Models (GLM) for analyzing demographic and behavioral features to generate rigorous statistical inference.

Do I need detailed statistical knowledge to use these econometrics models?▼

Yes, detailed statistical knowledge is required for model selection and interpretation. This Skill provides advanced statistical modeling capabilities for econometrics, finance, and social sciences, demanding rigorous understanding.

What is the best way to run OLS linear regression for data analysis?▼

The best way to run OLS linear regression is by fitting the model using your provided `y` and `X` data. This Skill enables robust estimation and offers tools for influence detection and model validation.

Are there limitations when applying robust estimation to economic time series data?▼

Limitations arise if model assumptions are violated, requiring thorough diagnostics. You must perform residual analysis and influence detection during model validation to ensure robust estimation across econometrics domains.