Can I use R-style formulas with this skill?

Yes, it fully supports the Statsmodels Formula API (smf), allowing you to define complex models using intuitive strings like 'y ~ x1 + x2 + x1:x2'.

How does this skill help with regression analysis?

It provides standardized patterns for OLS, WLS, and GLS regression with detailed summaries, including p-values, R-squared, and confidence intervals for reliable inference.

What diagnostic tests are included?

The skill facilitates tests for heteroskedasticity (Breusch-Pagan), autocorrelation (Durbin-Watson), and normality (Jarque-Bera) to ensure your model meets statistical assumptions.

Does it support time series forecasting?

Absolutely, it includes comprehensive support for ARIMA, SARIMAX, VAR, and exponential smoothing for advanced temporal data analysis and prediction.

Statsmodels Statistical Modeling

Name: Statsmodels Statistical Modeling
Author: sickn33

bysickn33

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31,721

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Data Science & ML

Provides comprehensive tools for rigorous statistical estimation, inference, and diagnostic testing in Python.

This skill integrates the Statsmodels library into Claude's workflow, enabling advanced statistical analysis, econometric modeling, and time series forecasting. It facilitates the implementation of diverse regression models, generalized linear models, and discrete choice models while providing robust diagnostic tools to validate model assumptions. Whether you are conducting academic research, financial forecasting, or causal inference, this skill ensures high-quality statistical output, publication-ready summaries, and deep insights into data relationships.

Key Features

01Linear and Generalized Linear Models including OLS, GLM, and Logit

0231,721 GitHub stars

03R-style formula API support for intuitive and rapid model specification

04Advanced Time Series Analysis with ARIMA, SARIMAX, and VAR models

05Robust standard error estimation and publication-quality summary tables

06Comprehensive statistical diagnostics for heteroskedasticity and normality

Use Cases

01Validating machine learning model assumptions through rigorous statistical testing

02Forecasting future trends using univariate and multivariate time series models

03Performing econometric analysis and causal inference for business and academic research

Key Features

01Linear and Generalized Linear Models including OLS, GLM, and Logit

0231,721 GitHub stars

03R-style formula API support for intuitive and rapid model specification

04Advanced Time Series Analysis with ARIMA, SARIMAX, and VAR models

05Robust standard error estimation and publication-quality summary tables

06Comprehensive statistical diagnostics for heteroskedasticity and normality

Use Cases

01Validating machine learning model assumptions through rigorous statistical testing

02Forecasting future trends using univariate and multivariate time series models

03Performing econometric analysis and causal inference for business and academic research