statsmodels
Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.
Profile →npx skills add majiayu000/claude-skill-registry --skill statsmodels-sickn33-antigravity-awesome --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
The skill teaches statistical modeling tasks using Statsmodels, covering linear regressions (OLS, WLS, GLS, GLSAR, quantile, mixed effects, recursive), GLMs (Poisson, binomial, gamma, etc.), discrete choice models (Logit, Probit, MNLogit, ZIP/ZINB variants), time series (ARIMA, SARIMAX, VAR, VECM, etc.), and comprehensive tests/diagnostics. It includes concrete usage patterns, diagnostic checks, and model evaluation guidance.
How it works
It provides a mix of explicit code blocks and stepwise instructions for applying Statsmodels:
- Examples for preparing data, adding constants, fitting models (e.g., OLS via sm.OLS, Logit via Logit), and obtaining results (results.summary(), results.pvalues, results.params).
- Guidance for diagnostics (breusch-pagan test, residual plots, ACF/PACF, stationarity tests) and model refinement (robust SEs, alternative models like Negative Binomial).
- Demonstrates model selection and comparison using information criteria (AIC, BIC) and likelihood ratio tests.
- Shows formula API usage (smf.ols, smf.logit, smf.poisson) and cross-validation patterns.
- Includes general workflows for linear regression, binary classification, count data analysis, and time series forecasting.
When to use it
Use this skill when you need to perform statistical modeling with Statsmodels across:
- Fitting regression models (OLS, WLS, GLS, etc.)
- Generalized linear models for non-normal outcomes
- Discrete choice and count data models
- Time series forecasting and analysis
- Running tests and diagnostics to validate model assumptions
- Producing publication-ready summaries and inference
What it can touch
The skill utilizes:
- Python code blocks using Statsmodels APIs (e.g., sm.OLS, Logit, ARIMA, GLM, smf.ols, smf.logit)
- Data structures like X, y, y_binary, y_counts, y_series, X_data, df
- Diagnostic functions and plotting (Breusch-Pagan, plot_acf/plot_pacf, adfuller, results.plot_diagnostics)
- External libraries referenced in examples (numpy, pandas, sklearn.metrics, matplotlib)
Caveats
- License in frontmatter is BSD-3-Clause; actual file header lists BSD-3-Clause while description states BSD-3-Clause license, conflicting but not elaborated within skills.
- risk is labeled unknown.
- The skill provides extensive examples and workflows but does not guarantee outcomes; user should validate across their data context.
# 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?
Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill statsmodels-sickn33-antigravity-awesome --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 majiayu000/claude-skill-registry, a repository with 534 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.