Agent skill · research science · k-dense-ai
statsmodels
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Why this skill is useful
Provides specific statistical modeling techniques and diagnostics that the AI wouldn't reliably generate on its own.
What it needs
Requires python, statsmodels installed locally. About 5k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
Statsmodels: Statistical Modeling and Econometrics Overview 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. Current Compatibility Examples target statsmodels 0.14.6, released Dec 5, 2025. For reproducible environments, pin the primary package: Use statsmodels.api and statsmodels.formula.api for stable high-level imports, and direct module imports when examples require newer or specialized classes such as HurdleCountModel. When to Use This Skill 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 Quick Start, Capabilities, and Model Selection references/quickstartguide.md: minimal worked examples for OLS, logistic regression, ARIMA, and GLM, and how to read the summary. references/modelingcapabilities.md: linear models, GLMs, discrete choice, time series, and the statistical tests and diagnostics. references/modelselection.md: the R-style formula API and model comparison. Per-topic detail: references/linearmodels.md, references/glm.md, references/discretechoice.md, references/timeseries.md, and references/statsdiagnostics.md. statsmodels is for inference — standard errors, confidence intervals, and hypothesis tests. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/statsmodels