statistical-modeling-inference

Guides statistical modeling and hypothesis testing in Python with the statsmodels library. Covers fitting regressions from R-style formulas, generalized linear models, ARIMA-style time series forecasting, ANOVA with post-hoc tests, survival analysis, and residual diagnostics, plus correct reading of coefficients, p-values, and confidence intervals. Use when a task involves OLS or weighted least squares, logistic, Poisson, or negative binomial regression, SARIMAX, stationarity testing, Tukey comparisons, Cox hazards, or explaining relationships rather than pure prediction. Not for cross-validated prediction pipelines, clustering, neural networks, or other scikit-learn territory.

Version

v1.0.0

Size

14 KB

Updated

2026-10-06

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