Fit and diagnose statistical models on tabular and time series data.
npx skills add https://github.com/dralkh/seerai --skill statsmodels-dralkh
Please help me install this Agent Skill. Skill: statsmodels Source: https://github.com/dralkh/seerai/tree/main/skills/statsmodels Command: npx skills add https://github.com/dralkh/seerai --skill statsmodels-dralkh
This Skill helps you build, diagnose, and interpret statistical models when you need reliable inference, not just predictions. It reduces the complexity of choosing the right model for continuous, binary, count, categorical, or time series data.
Use the statsmodels skill to fit the appropriate model to your dataset, then return the summary output, diagnostics, and a plain-English interpretation of the key coefficients and test results.
High-intent search queries and answers about installing and using this skill.
Hypothesis testing and regression analysis on tabular data are executed by fitting OLS, WLS, or GLS models with formula support, generating coefficient tables, confidence intervals, and summary outputs for statistical inference.
Forecasting time series data with ARIMA and SARIMAX models involves using state space methods and ETS to capture temporal patterns, returning prediction intervals and forecast summaries for time-ordered datasets.
Generalized linear models and Poisson regression for count data are supported through GLM, negative binomial, and zero-inflated analyses, allowing you to model discrete and binary outcomes with robust standard errors.
Checking residuals, heteroskedasticity, and autocorrelation is performed through built-in diagnostics that validate model assumptions, test normality, measure influence, and detect multicollinearity in your fitted regression results.
This statistical modeling workflow supports formula syntax and robust standard errors, requiring statsmodels-compatible Python environments to produce publication-ready inference, model comparison metrics, and plain-English interpretations.
Quantile regression or mixed effects models are used instead of standard OLS when your data violates ordinary least squares assumptions, requiring robust handling of hierarchical structures, outliers, or non-normal residual distributions.
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