domains / families / data / statsmodels

statsmodels

combined

Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-analysis. Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

When to use

• 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)

When not to use

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The K-Dense-AI copy is the upstream source (v1.5) and contains material constraints the other copies lack, such as explicit version pinning, the missing='raise' data-preparation rule, and the requirement to use result_object=True for transitioning tests. The zLanqing and foryourhealth111 copies are near-identical reproductions of an earlier version that include inline code examples but lack these specific safety and compatibility checks. The sickn33 copy is a wrapper that defers to a separate guide. Because the upstream copy has unique, mandatory steps that the reproductions omit, crowning one would hide the real alternative of using the inline examples versus the version-pinned constraints.