Statsmodels Statistical Toolkit

About

The Statsmodels skill empowers Claude to perform high-level statistical modeling and econometric analysis using Python's premier statistics library. It enables the fitting of various regression models—including OLS, GLM, and logistic regression—as well as sophisticated time-series analysis with ARIMA and seasonal models. This skill is essential for users needing to conduct formal hypothesis tests, validate model assumptions through diagnostic checks, and produce publication-ready statistical summaries that go beyond simple machine learning predictions to provide deep inferential insights into data relationships.

Key Features

• Discrete choice models for binary, multinomial, and count data outcomes
• 1 GitHub stars
• Comprehensive time series analysis suite with ARIMA, SARIMAX, and VAR capabilities
• R-style formula API support for intuitive and flexible model specification
• Advanced linear and generalized linear modeling including OLS, GLM, and WLS
• Rigorous statistical diagnostics for heteroskedasticity, normality, and autocorrelation

Use Cases

• Forecasting financial or economic trends using sophisticated time-series modeling
• Estimating causal relationships and conducting econometric research for academic or business reports
• Validating scientific hypotheses through rigorous statistical testing and residual diagnostics