Performs rigorous statistical modeling and econometric analysis using Python's statsmodels library.
This skill empowers Claude to conduct deep statistical research and data modeling using the statsmodels library. It provides comprehensive support for linear regression, generalized linear models, time series analysis (like ARIMA), and discrete choice models. Ideal for researchers and data scientists needing precise p-values, detailed coefficient tables, and robust diagnostic tests, it facilitates professional-grade inference and econometrics directly within the coding workflow.
Key Features
01Extensive statistical diagnostics for residuals, normality, and heteroskedasticity
020 GitHub stars
03Support for R-style formula API for intuitive model specification
04Generalized Linear Models (GLM) for Poisson, Logistic, and Gamma distributions
05Comprehensive time series analysis with ARIMA, SARIMAX, and VAR models
06Advanced regression modeling including OLS, WLS, GLS, and Quantile regression
Use Cases
01Conducting econometric research with rigorous hypothesis testing and coefficient inference
02Validating statistical assumptions and detecting outliers in scientific datasets
03Forecasting financial or operational trends using seasonal time series models
