Can I use R-style formulas with this skill?

Yes, this skill fully supports the Statsmodels Formula API, allowing you to specify models using the 'y ~ x1 + x2' syntax familiar to R users.

Does it handle non-normal data distributions?

Absolutely. Through Generalized Linear Models (GLM), it supports Binomial, Poisson, Gamma, and Negative Binomial distributions among others.

How does this skill differ from standard scikit-learn usage?

While scikit-learn focuses primarily on predictive accuracy, the Statsmodels skill focuses on statistical inference, providing detailed summaries including p-values, confidence intervals, and log-likelihoods.

What types of time series models are supported?

It supports a wide range of univariate and multivariate models including ARIMA, SARIMAX, Vector Autoregression (VAR), and Exponential Smoothing.

How does this help with AI-driven discovery?

As part of the Kosmos suite, it provides the mathematical framework for an AI to autonomously validate scientific hypotheses and ensure findings are statistically significant.

Statsmodels Statistical Analysis

Name: Statsmodels Statistical Analysis
Author: jimmc414

byjimmc414

•

324

Ciencia de Datos y ML

Performs rigorous statistical modeling and econometric analysis using regression, time series, and diagnostic testing.

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The Statsmodels skill empowers Claude to perform advanced statistical analysis and econometric modeling within Python environments. It provides specialized support for fitting diverse regression models (OLS, GLM), conducting time series forecasting (ARIMA, SARIMAX), and executing complex hypothesis tests. Designed for data scientists and researchers, this skill ensures rigorous statistical inference by automating model diagnostics, assumption testing, and the generation of publication-quality statistical summaries, bridging the gap between raw data processing and autonomous scientific discovery.

Características Principales

Comprehensive regression modeling including OLS, WLS, and GLS
324 GitHub stars
Generalized Linear Models (GLM) for Poisson, Logistic, and Gamma distributions
Extensive diagnostic suite for testing heteroskedasticity, autocorrelation, and normality
R-style formula API support for intuitive and readable model specification
Advanced time series analysis with ARIMA, SARIMAX, and VAR models

Casos de Uso

Validating machine learning model assumptions through rigorous statistical hypothesis testing
Conducting econometric research and causal inference for academic or business reporting
Forecasting future trends in time-series data with seasonal and exogenous variables

Acerca de

Características Principales

Comprehensive regression modeling including OLS, WLS, and GLS
324 GitHub stars
Generalized Linear Models (GLM) for Poisson, Logistic, and Gamma distributions
Extensive diagnostic suite for testing heteroskedasticity, autocorrelation, and normality
R-style formula API support for intuitive and readable model specification
Advanced time series analysis with ARIMA, SARIMAX, and VAR models

Casos de Uso

Validating machine learning model assumptions through rigorous statistical hypothesis testing
Conducting econometric research and causal inference for academic or business reporting
Forecasting future trends in time-series data with seasonal and exogenous variables