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statsmodels

Fit OLS regression models and print comprehensive summary results with statsmodels.

Updated Feb 1, 2026
Search Tags:#statistics#regression#time-series#modeling#inference#econometrics
One-click install
npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill statsmodels-nferrer-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/nferrer-dev/claude-dotfiles/tree/main/skills/statsmodels
Command: npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill statsmodels-nferrer-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the need for precise, reproducible statistical inference and econometric modeling, moving beyond simple correlations to provide deep insights into data relationships, causality, and predictive accuracy.

Core Features & Use Cases

  • Advanced Modeling: Perform OLS, GLM, time series (ARIMA/SARIMAX), and discrete choice analysis with full diagnostic support.
  • Statistical Inference: Generate publication-ready coefficient tables, robust standard errors, and hypothesis testing results.
  • Use Case: Use this skill to determine the causal impact of a marketing campaign on sales while controlling for seasonality and heteroskedasticity in your time-series data.

Quick Start

Use the statsmodels skill to fit an OLS regression model on the provided dataframe and print the comprehensive summary of the results.

Frequently Asked Questions about statsmodels

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

FAQPage Schema
How do I perform OLS regression and get robust standard errors for hypothesis testing?▼

OLS regression with robust standard errors is performed using the statsmodels library to generate publication-ready coefficient tables and comprehensive hypothesis testing results for statistical inference.

What is the best way to forecast time-series data while controlling for seasonality?▼

Time-series forecasting while controlling for seasonality is best handled using SARIMAX models in statsmodels, providing deep insights into data relationships and predictive accuracy for scientific research.

Can I use pandas dataframes directly for econometric modeling and discrete choice analysis?▼

Pandas dataframes can be used directly for econometric modeling and discrete choice analysis with statsmodels, leveraging numpy and scipy dependencies to compute precise statistical inference.

How do I run residual analysis and model comparison diagnostics after fitting a GLM?▼

Residual analysis and model comparison diagnostics after fitting a GLM are executed within statsmodels, providing robust diagnostic capabilities to validate model assumptions and compare statistical performance.

Does this approach work for determining the causal impact of a marketing campaign on sales?▼

Determining the causal impact of a marketing campaign on sales is supported by this econometric modeling approach, moving beyond simple correlations to provide precise insights into data relationships and causality.

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