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statsmodels

Run statistical modeling and analysis using Python's statsmodels library.

6|3|Updated Jan 29, 2026
Search Tags:#time series#statistics#regression#forecasting#GLM
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill statsmodels-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/scientific/statsmodels
Command: npx skills add https://github.com/jonnabio/ace-framework --skill statsmodels-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires statsmodels, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies statistical modeling by providing Python's premier library for estimation, inference, and diagnostics across a wide range of statistical methods.

Core Features & Use Cases

  • Regression Models: Offers comprehensive linear and generalized linear models for continuous and categorical outcomes.
  • Discrete Choice Models: Handles binary, multinomial, count, and ordinal models with maximum likelihood estimation.
  • Time Series Analysis: Includes univariate and multivariate models for time series data, including ARIMA, VAR, and state space models.
  • Diagnostics and Testing: Provides tools for model validation, including residual diagnostics, hypothesis testing, and robust standard errors.
  • Use Case: Imagine you have time series data on sales and want to forecast future sales using ARIMA.

Quick Start

To run a simple linear regression, use the command statsmodels linregress x y.

Frequently Asked Questions about statsmodels

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

FAQPage Schema
How do I run time series forecasting with ARIMA in Python?▼

Time series forecasting with ARIMA is handled by Python's statsmodels library, which provides univariate and multivariate models like ARIMA and VAR to estimate and project sequential data values.

What statistical methods are available for generalized linear models?▼

Generalized linear models and discrete choice models are available for continuous and categorical outcomes, offering maximum likelihood estimation for binary, multinomial, count, and ordinal data.

Do I need Python installed to use statsmodels for regression analysis?▼

Yes, running linear regression and statistical analysis requires a Python environment with the statsmodels library installed to execute the necessary estimation and diagnostic commands.

How do I validate a regression model and test for residual errors?▼

Model validation and testing are performed using built-in diagnostics tools that provide residual diagnostics, robust standard errors, and hypothesis testing to verify regression model accuracy.

What is the best way to forecast sales data using statistical modeling?▼

Statistical modeling for sales forecasting is best handled by applying time series models like ARIMA from Python's statsmodels to estimate and project future values from historical data.

Can I handle discrete choice and count data with maximum likelihood estimation?▼

Discrete choice and count data are handled using maximum likelihood estimation to fit binary, multinomial, and ordinal models for comprehensive statistical inference on categorical outcomes.

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