Does this skill help with model diagnostics?

Extensively. It provides tools for checking heteroskedasticity, normality of residuals, multicollinearity (VIF), and identifying influential data points.

What types of time series models are supported?

The skill covers a wide range of temporal models including ARIMA, SARIMAX, Vector Autoregression (VAR), and exponential smoothing, along with stationarity tests like ADF.

How does Statsmodels differ from Scikit-learn in this skill?

While Scikit-learn focuses on predictive performance and machine learning, this Statsmodels skill focuses on statistical inference, providing detailed p-values, confidence intervals, and diagnostic tests for model validation.

Can I use R-style formulas with this skill?

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

Statsmodels Statistical Analysis

Name: Statsmodels Statistical Analysis
Author: x-cmd

byx-cmd

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데이터 과학 및 ML

Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using Python's premier inference toolkit.

The Statsmodels skill empowers Claude to perform high-level statistical estimation and inference, moving beyond simple prediction into deep analytical diagnostics. It provides specialized support for linear regression (OLS), generalized linear models (GLM), discrete choice models, and sophisticated time series analysis like ARIMA. This skill is essential for data scientists and researchers who need to validate model assumptions through diagnostic tests, calculate robust standard errors, and generate publication-ready statistical summaries for econometric or scientific workflows.

주요 기능

01R-style Formula API for intuitive model specification

02Linear and Generalized Linear Models (OLS, WLS, Logistic, Poisson)

03Comprehensive Time Series Analysis (ARIMA, SARIMAX, VAR, Forecasting)

048 GitHub stars

05Discrete Choice Modeling for categorical and count-based outcomes

06Rigorous Statistical Tests (Heteroskedasticity, Normality, Autocorrelation)

사용 사례

01Conducting econometric research with detailed coefficient inference and p-values

02Validating model assumptions and detecting outliers using Cook's distance and leverage plots

03Forecasting temporal data with seasonal decomposition and stationarity testing

주요 기능

01R-style Formula API for intuitive model specification

02Linear and Generalized Linear Models (OLS, WLS, Logistic, Poisson)

03Comprehensive Time Series Analysis (ARIMA, SARIMAX, VAR, Forecasting)

048 GitHub stars

05Discrete Choice Modeling for categorical and count-based outcomes

06Rigorous Statistical Tests (Heteroskedasticity, Normality, Autocorrelation)

사용 사례

01Conducting econometric research with detailed coefficient inference and p-values

02Validating model assumptions and detecting outliers using Cook's distance and leverage plots

03Forecasting temporal data with seasonal decomposition and stationarity testing