statsmodels-statistical-modeling
Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.
npx skills add BioTender-max/awesome-bio-agent-skills --skill statsmodels-statistical-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# statsmodels ## Overview Statsmodels provides classical statistical modeling with rigorous inference for Python. It covers linear models, generalized linear models, discrete choice, time series, and comprehensive diagnostics. Unlike scikit-learn (prediction-focused), statsmodels emphasizes coefficient interpretation, p-values, confidence intervals, and model diagnostics. ## When to Use - Fitting linear regression (OLS, WLS, GLS) with detailed coefficient tables and diagnostics - Running logistic regression with odds ratios and marginal effects for clinical/epidemiological studies - Analyzing count data with Poisson or negative binomial regression - Time series forecasting with ARIMA, SARIMAX, or exponential smoothing - Performing ANOVA, t-tests, or non-parametric tests with proper corrections - Testing model assumptions (heteroskedasticity, autocorrelation, normality of residuals) - Model comparison using AIC/BIC or likelihood ratio tests - Using R-style formula interface (`y ~ x1 + x2 + C(group)`) for intuitive model specification - For prediction-focused ML with cross-validation and hyperparameter tuning, use `scikit-learn` instead - For Bayesian modeling with posterior inferenc
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Linear Regression (OLS, WLS, GLS)
- Module 2: Generalized Linear Models (GLM)
- Module 3: Discrete Choice Models (Logit, Probit, Count)
- Module 4: Time Series (ARIMA, SARIMAX)
- Module 5: Statistical Tests and Diagnostics
- Module 6: Formula API (R-style)
- Common Workflows
- Workflow 1: Complete Regression Analysis
- Workflow 2: Model Comparison
pip install statsmodels numpy pandas matplotlib
What does the statsmodels-statistical-modeling skill do?
Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill statsmodels-statistical-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.