What problem does it solve? Running rigorous statistical analysis in Python often requires choosing the right model class, validating assumptions, and producing publication-ready inference tables, which is error-prone without structured guidance. ## Core Features & Use Cases - Regression and GLM Modeling: Fit OLS, WLS, GLS, quantile regression, logistic, Poisson, Negative Binomial, and mixed effects models with full coefficient tables and p-values. - Time Series Analysis: Build ARIMA, SARIMAX, VAR, and state space models with stationarity tests, ACF/PACF identification, and forecast intervals. - Diagnostics and Testing: Run heteroskedasticity, autocorrelation, normality, and influence tests, plus robust standard errors and power analysis. - Use Case: An economist analyzing survey data can fit a logistic regression, interpret odds ratios, check for influential observations with Cook's distance, and report cluster-robust standard errors in one workflow. ## Quick Start Fit an OLS regression of y on my predictors with statsmodels, show the summary table, and check the residuals for heteroskedasticity.
