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statsmodels

Fit statistical models and analyze time series data in Python.

Updated Jun 6, 2026
Search Tags:#time series#statistics#regression#forecasting#econometrics
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill statsmodels-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/statsmodels
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill statsmodels-ritabrata-chakraborty

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 provides comprehensive statistical modeling capabilities, enabling you to perform a wide range of statistical analyses, from linear regression to time series forecasting, with detailed diagnostics and inference.

Core Features & Use Cases

  • Statistical Modeling: Offers a suite of linear, generalized linear, and discrete choice models for various data types.
  • Time Series Analysis: Supports ARIMA, SARIMAX, VAR, and other models for forecasting and analysis of time-ordered data.
  • Diagnostics and Inference: Provides tools for residual analysis, hypothesis testing, and model comparison.
  • Use Case: For instance, you can use this Skill to fit a linear regression model to predict sales based on advertising spend and other factors, or to analyze the effects of a policy change on a time series dataset.

Quick Start

Use the statsmodels skill to fit a linear regression model to your data. Prepare your data, specify the dependent and independent variables, and run the following command:

statsmodels linear-regression --data my_data.csv --depvar sales --indepvars advertising spend market share

Frequently Asked Questions about statsmodels

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

FAQPage Schema
How do I run linear regression on a CSV dataset for econometric analysis?▼

To run linear regression on a CSV dataset, specify your dependent and independent variables with the statsmodels linear-regression command. It fits the model and provides detailed statistical diagnostics and inference for econometric analysis.

What statistical models are available for time series forecasting?▼

Available statistical models for time series forecasting include ARIMA, SARIMAX, and VAR. These support comprehensive analysis and forecasting of time-ordered data for econometricians and data scientists.

Can I perform residual analysis and hypothesis testing on my regression models?▼

Yes, you can perform residual analysis and hypothesis testing on your regression models. The skill provides diagnostics and inference tools to validate model assumptions and compare different statistical models.

Does statsmodels support generalized linear models and discrete choice models?▼

Yes, statsmodels supports generalized linear models and discrete choice models. It offers a comprehensive suite of statistical modeling options for various structured data types and econometric requirements.

Do I need the statsmodels library installed to fit statistical models?▼

Yes, you need the statsmodels Python library installed. It is a required dependency for fitting linear regression, time series analysis, and other econometric models within your environment.

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