Fit linear, generalized linear, and time-series models with statsmodels.
npx skills add https://github.com/tomlupo/ai-playground --skill statsmodels-tomlupo
Please help me install this Agent Skill. Skill: statsmodels Source: https://github.com/tomlupo/ai-playground/tree/main/.claude/skills/statsmodels Command: npx skills add https://github.com/tomlupo/ai-playground --skill statsmodels-tomlupo
This Skill provides a unified workflow for statistical modeling and diagnostics using statsmodels, enabling rigorous analysis and reproducible inference.
Run a quick example: load a sample dataset, fit an OLS model with statsmodels, and print the summary.
High-intent search queries and answers about installing and using this skill.
To perform linear regression with statsmodels, load your data into a pandas DataFrame, fit an OLS model, and print the summary to view regression diagnostics. This provides rigorous statistical inference and model evaluation results.
Yes, you can fit ARIMA, SARIMAX, and VAR time-series models using statsmodels. The workflow supports multivariate time-series forecasting and includes diagnostic plots to validate model assumptions and analyze econometric data.
The best way to compare statistical models is using information criteria, likelihood ratio tests, and cross-validation-style checks. These model evaluation tools help validate assumptions and select the most appropriate GLM or regression model.
Yes, this approach supports generalized linear models (GLM) and discrete choice analysis. It provides comprehensive reference materials to guide model selection and interpretation for econometrics, finance, and social science applications.
Yes, you need numpy and pandas installed as dependencies to run statsmodels workflows. These libraries provide the foundational data structures and array processing required for statistical modeling and reproducible inference.
Axel-DaMageAutomate pandas DataFrame operations for cleaning, aggregation, and time series analysis.
jg-youGenerate ArviZ diagnostic and substantive figures for Bayesian analyses.
galeepFit statistical models and run diagnostics in Python with statsmodels.
HermeticOrmusImplement ML testing patterns with pytest, pandera, and scikit-learn metrics.
