How does this differ from using scikit-learn?

While scikit-learn focuses on predictive accuracy, Statsmodels is designed for statistical inference, providing detailed summary tables with p-values, confidence intervals, and diagnostic tests.

Can I perform time series forecasting with this skill?

Absolutely. It supports a wide range of time series tools including stationarity tests (ADF), ARIMA/SARIMAX models, and impulse response functions.

Does it include model diagnostic tools?

Yes, the skill provides specific workflows for checking assumptions such as the Breusch-Pagan test for heteroskedasticity and Durbin-Watson for autocorrelation.

Does this skill support R-style model formulas?

Yes, it includes patterns for the statsmodels.formula.api, allowing you to define models using the familiar 'y ~ x1 + x2' syntax.

Statsmodels Statistical Analysis

Name: Statsmodels Statistical Analysis
Author: lifangda

bylifangda

•

Data Science & ML

Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using Python's statsmodels library.

About

The Statsmodels skill provides Claude with the specialized knowledge to execute deep statistical inference beyond simple prediction. It enables the creation of complex regression models (OLS, GLM), discrete choice analysis, and advanced time series forecasting while prioritizing model diagnostics and validity. Perfect for researchers, economists, and data scientists, this skill ensures that statistical assumptions like homoscedasticity and normality are verified through formal tests, delivering publication-ready results and robust causal insights.

Key Features

Comprehensive regression suite including OLS, WLS, and Quantile Regression
Generalized Linear Models (GLM) for Binomial, Poisson, and Gamma data
Advanced time series analysis with ARIMA, SARIMAX, and VAR capabilities
R-style formula support for intuitive and flexible model specification
16 GitHub stars
Extensive diagnostic tests for autocorrelation, heteroskedasticity, and outliers

Use Cases

Conducting formal econometric research and causal effect estimation
Performing rigorous hypothesis testing and model comparison for scientific reports
Building and validating time series forecasting models for financial data

About

Key Features

Comprehensive regression suite including OLS, WLS, and Quantile Regression
Generalized Linear Models (GLM) for Binomial, Poisson, and Gamma data
Advanced time series analysis with ARIMA, SARIMAX, and VAR capabilities
R-style formula support for intuitive and flexible model specification
16 GitHub stars
Extensive diagnostic tests for autocorrelation, heteroskedasticity, and outliers

Use Cases

Conducting formal econometric research and causal effect estimation
Performing rigorous hypothesis testing and model comparison for scientific reports
Building and validating time series forecasting models for financial data