Statsmodels
Skill Verifiziert AktivStatistical 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.
To empower AI agents with advanced statistical modeling capabilities, enabling rigorous inference, time series analysis, and diagnostic testing using the comprehensive statsmodels library.
Funktionen
- Fit OLS, WLS, GLS, GLSAR, Quantile Regression models
- Perform Generalized Linear Modeling (Binomial, Poisson, Gamma, etc.)
- Conduct Time Series Analysis (ARIMA, SARIMAX, VAR, Exponential Smoothing)
- Access comprehensive statistical tests and diagnostics
- Utilize R-style formula API for model specification
Anwendungsfälle
- When performing econometrics and needing detailed inference with coefficient tables
- When analyzing time series data for forecasting and understanding temporal dynamics
- When needing specific model classes like OLS, GLM, mixed models, or ARIMA
- When rigorous statistical inference and detailed diagnostic checks are required
Nicht-Ziele
- Guided statistical test selection with APA reporting (use 'statistical-analysis' skill)
- Simple data visualization (though plots are used for diagnostics)
- Basic descriptive statistics without inferential modeling
Workflow
- Explore data and check assumptions (stationarity, homoscedasticity)
- Select and fit appropriate statistical model (e.g., OLS, ARIMA, GLM)
- Perform diagnostic tests on residuals and model fit
- Interpret model coefficients, inference, and forecasts
- Validate model performance using cross-validation or holdout data
Praktiken
- Statistical modeling
- Time series analysis
- Regression analysis
- Model diagnostics
Voraussetzungen
- Python 3.11+
- uv package manager
- Agent Skills compatible client
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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