Statsmodels - Statistical Modeling & Inference

Statsmodels is the bridge between Python and the rigor of R-style statistical analysis. It allows users to estimate models using formulas (via patsy), perform extensive diagnostic tests, and produce detailed summary tables that are the standard in academic publishing.

When to Use

• Estimating Linear Regression models with detailed diagnostics (OLS, WLS).
• Generalized Linear Models (GLM): Logistic, Poisson, Gamma regression.
• Time Series Analysis (ARIMA, SARIMAX, VAR, State Space models).
• Statistical hypothesis testing (t-tests, ANOVA, normality, heteroscedasticity).
• Survival analysis (Kaplan-Meier, Cox Proportional Hazards).
• Estimating treatment effects and causal inference.
• Non-parametric statistics (Kernel Density Estimation).

Reference Documentation

Official docs: https://www.statsmodels.org/stable/
Formula API: https://www.statsmodels.org/stable/example_formulas.html
Search patterns: sm.OLS, smf.ols, sm.tsa, results.summary(), statsmodels.api