What problem does it solve? Running rigorous statistical analysis in Python often requires stitching together model fitting, assumption testing, and inference reporting by hand. This Skill provides guided workflows for statsmodels so you can fit regression, GLM, discrete choice, and time series models with proper diagnostics and publication-ready output. ## Core Features & Use Cases - Regression and GLM Modeling: Fit OLS, WLS, GLS, quantile regression, logistic, Poisson, Negative Binomial, and Gamma models with coefficient tables and robust standard errors. - Time Series Analysis: Build ARIMA, SARIMAX, VAR, and state space models with stationarity tests, ACF/PACF identification, and forecast intervals. - Diagnostics and Inference: Run heteroskedasticity, autocorrelation, normality, and influence tests, plus ANOVA, multiple comparisons, and power analysis. - Use Case: A researcher analyzing count data on hospital visits can fit a Poisson model, detect overdispersion, switch to Negative Binomial, and report rate ratios with confidence intervals. ## Quick Start Ask the agent to fit an OLS regression of your outcome on your predictors using statsmodels and show the full summary with residual diagnostics.
