statsmodels

Topics in the source

• Statsmodels: Statistical Modeling and Econometrics
• Overview
• When to Use This Skill
• Quick Start Guide
• Linear Regression (OLS)
• Logistic Regression (Binary Outcomes)
• Time Series (ARIMA)
• Generalized Linear Models (GLM)

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: Fitting regression models (OLS, WLS, GLS, quantile regression) Performing generalized linear modeling (logistic, Poisson, Gamma, etc.) Analyzing discrete outcomes (binary, multinomial, count, ordinal) Conducting time series analysis (ARIMA, SARIMAX, VAR, forecasting) Running statistical tests and diagnostics Testing model assumptions (heteroskedasticity, autocorrelation, normality) Detecting outliers and influential observations Comparing models (AIC/BIC, likelihood ratio tests) Estimating causal effects Producing publication-ready statistical tables and inference Comprehensive suite of linear models for continuous outcomes with various error structures. Available models: OLS: Standard linear regression with i.i.d. errors WLS: Weighted least squares for heteroskedastic errors GLS: Generalized least squares for arbitrary covariance structure GLSAR: G

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