Is this skill suitable for machine learning or just statistics?

While it can be used for prediction, its primary focus is on statistical inference, hypothesis testing, and understanding the relationships between variables, which complements standard ML workflows.

Does the skill include support for model diagnostics?

Yes, it covers a wide range of diagnostic tests including Breusch-Pagan for heteroskedasticity, Ljung-Box for autocorrelation, and influence measures like Cook's distance.

Can I perform time-series forecasting with this skill?

Absolutely. It includes patterns for univariate and multivariate time-series models, including ARIMA, SARIMAX, and Vector Autoregression (VAR).

How does this skill help with statistical modeling in Claude?

It provides Claude with specialized implementation patterns, diagnostic procedures, and best practices for the Python Statsmodels library, ensuring rigorous statistical inference.

Does it support R-style formula notation?

Yes, the skill includes guidance for using the formula API (Patsy), allowing you to specify models using strings like 'y ~ x1 + x2' just like in R.

Statsmodels Statistical Analysis

Name: Statsmodels Statistical Analysis
Author: K-Dense-AI

byK-Dense-AI

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2,188

Data Science & ML

Provides a comprehensive toolkit for rigorous statistical modeling, econometric analysis, and time-series forecasting within the Claude environment.

About

The Statsmodels skill empowers developers and data scientists to perform professional-grade statistical inference and complex data analysis directly within their coding workflow. It provides Claude with specific guidance for fitting regression models, performing generalized linear modeling (GLM), and conducting advanced time-series analysis like ARIMA and SARIMAX. Beyond simple model fitting, this skill emphasizes rigorous diagnostics, hypothesis testing, and model comparison (AIC/BIC), making it an essential tool for econometric studies, scientific research, and any application requiring statistically sound data interpretation.

Key Features

R-style formula support for intuitive model specification and categorical variable handling
Comprehensive regression modeling including OLS, GLS, and Quantile Regression
Professional-grade time series analysis with ARIMA, SARIMAX, and VAR models
2,188 GitHub stars
Extensive diagnostic suite for testing heteroskedasticity, normality, and autocorrelation
Advanced Generalized Linear Models (GLM) for Poisson, Binomial, and Gamma distributions

Use Cases

Developing econometric models to estimate causal effects and elasticities in business data
Building robust time-series forecasting pipelines with seasonal adjustments and stationarity testing
Performing rigorous hypothesis testing and outlier detection for scientific data validation

About

Key Features

R-style formula support for intuitive model specification and categorical variable handling
Comprehensive regression modeling including OLS, GLS, and Quantile Regression
Professional-grade time series analysis with ARIMA, SARIMAX, and VAR models
2,188 GitHub stars
Extensive diagnostic suite for testing heteroskedasticity, normality, and autocorrelation
Advanced Generalized Linear Models (GLM) for Poisson, Binomial, and Gamma distributions

Use Cases

Developing econometric models to estimate causal effects and elasticities in business data
Building robust time-series forecasting pipelines with seasonal adjustments and stationarity testing
Performing rigorous hypothesis testing and outlier detection for scientific data validation