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statsmodels
Maintainer K-Dense Inc. · Last updated April 1, 2026
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.
Original source
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/statsmodels
- Maintainer
- K-Dense Inc.
- License
- BSD-3-Clause license
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- 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.
- X = sm.add_constant(X_data).
Source Doc
Excerpt From SKILL.md
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
Linear Regression (OLS)
import statsmodels.api as sm
import numpy as np
import pandas as pd
## Fit OLS model
model = sm.OLS(y, X)
results = model.fit()
Use cases
- Fitting regression models (OLS, WLS, GLS, quantile regression).
- Performing generali.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
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