How does this help with regression analysis?

The skill ensures Claude correctly handles data preparation (like adding constants), selects appropriate robust standard errors, and performs necessary diagnostic tests for model validation.

What is the Statsmodels Claude Code skill?

It is a specialized extension that provides Claude with expert knowledge, implementation patterns, and best practices for using the Python statsmodels library.

Can this skill handle time series forecasting?

Yes, it includes specialized patterns for univariate and multivariate time series analysis, including stationarity tests, ARIMA/SARIMAX modeling, and forecast visualization.

Does it support R-style formulas?

Yes, it provides guidance on using the statsmodels formula API (statsmodels.formula.api) to specify models using a syntax similar to the R programming language.

Statsmodels Statistical Analysis

Name: Statsmodels Statistical Analysis
Author: BbgnsurfTech

byBbgnsurfTech

•

Ciencia de Datos y ML

Performs rigorous statistical modeling and econometric analysis using Python's statsmodels library for deep data insights.

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The Statsmodels skill equips Claude with specialized expertise in Python's premier library for statistical modeling, inference, and diagnostics. It provides structured guidance for fitting linear and generalized linear models, conducting time-series forecasting with ARIMA/SARIMAX, and executing complex hypothesis tests. Whether you are conducting econometric research, validating model assumptions through residual diagnostics, or generating publication-quality summary tables, this skill ensures mathematically sound implementations and accurate interpretation of statistical metrics like AIC, BIC, and p-values.

Características Principales

Linear and Generalized Linear Models (OLS, GLM, Logistic)
3 GitHub stars
Time series analysis and forecasting (ARIMA, SARIMAX, VAR)
Comprehensive statistical diagnostics and hypothesis testing
R-style formula API support for intuitive model specification
Discrete choice modeling for categorical and count data

Casos de Uso

Conducting univariate or multivariate time series forecasting for business metrics
Validating regression assumptions through heteroskedasticity and normality testing
Fitting econometric models to analyze causal relationships in financial or social data

Acerca de

Características Principales

Linear and Generalized Linear Models (OLS, GLM, Logistic)
3 GitHub stars
Time series analysis and forecasting (ARIMA, SARIMAX, VAR)
Comprehensive statistical diagnostics and hypothesis testing
R-style formula API support for intuitive model specification
Discrete choice modeling for categorical and count data

Casos de Uso

Conducting univariate or multivariate time series forecasting for business metrics
Validating regression assumptions through heteroskedasticity and normality testing
Fitting econometric models to analyze causal relationships in financial or social data