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.
npx skills add LeonChaoX/qinyan-academic-skills --skill statsmodels --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.
What it does
Describes a Python-based statistical modeling toolkit that provides specific model classes (OLS, GLM, mixed models, ARIMA) with diagnostics, residuals, and inference capabilities. It targets econometrics, time series, and rigorous coefficient-based analysis, and supports guided test selection and APA-style reporting.
How it works
Outlines a broad set of modeling capabilities and concrete usage patterns:
- Linear Regression: OLS, WLS, GLS, GLSAR, Quantile Regression, Mixed Effects, Recursive/Rolling; includes diagnostics, robust SEs, influence measures, hypothesis tests, model comparison, and prediction with intervals.
- Generalized Linear Models: families (Binomial, Poisson, Negative Binomial, Gamma, Inverse Gaussian, Gaussian, Tweedie) with IRLS estimation, residuals, goodness-of-fit, pseudo R-squared, and robust SEs.
- Discrete Choice: Logit, Probit, MNLogit, Conditional Logit, Ordered Model, Poisson, Negative Binomial, Zero-Inflated, Hurdle; with marginal effects, predicted probabilities, and model evaluation.
- Time Series: univariate (AR, ARIMA, SARIMAX, Exponential Smoothing, ETS) and multivariate (VAR, VARMAX, Dynamic Factor Models, VECM); includes identification, forecasting with intervals, and residual diagnostics.
- GLMs and Diagnostics: Poisson modeling with rate ratios, overdispersion checks, and fallback to Negative Binomial if needed.
- Formula API: supports R-style formulas for OLS, Logit, Poisson, etc., with automatic handling of categoricals and interactions.
- Model Selection: AIC/BIC, likelihood ratio tests, cross-validation, and information criteria comparisons.
The skill provides example code blocks for common models and workflows, including data prep, fitting, prediction, and basic diagnostics.
When to use it
Use for fitting regression models (OLS, WLS, GLS, quantile), GLMs, discrete outcomes, time series models, and for performing tests and diagnostics, with an emphasis on inference, diagnostics, and publication-ready results.
What it can touch
Commands and APIs include:
sm.add_constant(...)to add interceptsm.OLS(...),Logit(...),sm.GLM(...),ARIMA(...), and other model classesresults.summary(),results.params,results.pvalues,results.aic,results.bic,results.llf,results.get_prediction(...),results.get_margeff()smf.ols(...),smf.logit(...),smf.poisson(...)from the Formula API- Diagnostics functions like
plot_acf,plot_pacf,adfuller, and tests such as Breusch-Pagan, Ljung-Box
Limitations and specifics are described in the sections under each modeling area rather than implicit.
Caveats
License is BSD-3-Clause; the skill notes robust standard errors, potential need for model-specific adjustments (e.g., overdispersion in Poisson prompting Negative Binomial alternative), and reliance on formula APIs and diagnostics. No outcomes guaranteed; emphasizes appropriate model selection, diagnostics, and reporting.
# 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. ## 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 Guide ### Linear Regression (OLS) ```python import statsmodels.api as sm import numpy as np import pandas as pd # Prepare data - ALWAYS add constant for intercept X
- Overview
- When to Use This Skill
- Quick Start Guide
- Linear Regression (OLS)
- Logistic Regression (Binary Outcomes)
- Time Series (ARIMA)
- Generalized Linear Models (GLM)
- Core Statistical Modeling Capabilities
- 1. Linear Regression Models
- 2. Generalized Linear Models (GLM)
- 3. Discrete Choice Models
- 4. Time Series Analysis
- 5. Statistical Tests and Diagnostics
- Formula API (R-style)
Find information about specific models grep -r "Quantile Regression" references/ Find diagnostic tests grep -r "Breusch-Pagan" references/stats_diagnostics.md Find time series guidance grep -r "SARIMAX" references/time_series.md
What does the statsmodels skill do?
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.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill statsmodels --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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.