statsmodels

Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods. Apply this skill for rigorous statistical analysis, from simple linear regression to complex time series models and econometric analyses. This skill should be used when: Comprehensive suite of linear models for continuous outcomes with various error structures. Available models: Key features: When to use: Continuous outcome variable, want inference on coefficients, need diagnostics Reference: See references/linear_models.md for detailed guidance on model selection, diagnostics, and best practices. Flexible framework extending linear models to non-normal distributions. Distribution families: Link functions: Key features: When to use: Non-normal outcomes, need flexible variance and link specifications Reference: See references/glm.md for family selection, link functions, interpretation, and diagnostics. Models for categorical and count outcomes. Binary models: Multinomial models: Count models: Key features: When to use: Binary, categorical, or count outcomes Reference: See references/discrete_choice.md for model selection, interpretation, and evaluation.