statsmodels

To build a generalized linear model for count data, apply Statsmodels API patterns for fitting Poisson or Negative Binomial models. This Skill provides formula-based modeling examples and guidance on interpreting model specifications for data analytics workflows.

For residual diagnostics and robust standard errors, use Statsmodels API patterns that support residual analyses, influence measures, and robust SEs. This Skill delivers reference-driven workflows for evaluating model assumptions and comparing information criteria across fitted models.

Yes, you can use Statsmodels for time-series forecasting by applying ARIMA and SARIMAX patterns. This Skill provides reference-driven workflows and code samples for time-series analysis, applicable in finance, economics, and social sciences for forecasting and model comparison.

This Skill supports discrete choice and ordinal models for social science analytics by providing Statsmodels API patterns. It includes guidance on model specification, interpretation, and information criteria for comparing discrete choice models within data science workflows.

To compare OLS, WLS, and GLS models using information criteria, apply Statsmodels API patterns for model evaluation. This Skill provides diagnostic workflows and reference examples for computing information criteria and selecting the best fitting linear model.

Use formula-based modeling in Statsmodels when you need readable model specification and interpretation within data analytics workflows. This Skill provides reference-driven examples for formula-based modeling across regression, GLM, and time-series analyses to accelerate statistical analysis.