# Statsmodels

Statistical models library for Python.

- Repository: https://github.com/zLanqing/codex-claude-academic-skills (5550 stars)
- Category: Testing
- License: BSD-3-Clause license
- Last updated: 2026-05-14
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add zLanqing/codex-claude-academic-skills --skill statsmodels -g -a claude-code -y`
Codex: `npx skills add zLanqing/codex-claude-academic-skills --skill statsmodels -g -a codex -y`
Cursor: `npx skills add zLanqing/codex-claude-academic-skills --skill statsmodels -g -a cursor -y`
Gemini CLI: `npx skills add zLanqing/codex-claude-academic-skills --skill statsmodels -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 = sm.add_constant(X_data)

# Fit OLS model
model = sm.OLS(y, X)
results = model.fit()

# View comprehensive results
print(results.summary())

# Key results
print(f"R-squared: {results.rsquared:.4f}")
print(f"Coefficients:\\n{results.params}")
print(f"P-values:\\n{results.pvalues}")

# Predictions with confidence intervals
predictions = results.get_prediction(X_new)
pred_summary = predictions.summary_frame()
print(pred_summary)  # includes mean, CI, prediction intervals

# Diagnostics
from statsmodels.stats.diagnostic import het_breuschpagan
bp_test = het_breuschpagan(results.resid, X)
print(f"Breusch-Pagan p-value: {bp_test[1]:.4f}")

The rest: https://github.com/zLanqing/codex-claude-academic-skills/blob/HEAD/scientific-toolkit-skill/references/scientific-skills/statsmodels/SKILL.md