RESEARCH USE
Where statsmodels fits
statsmodels supports explicit statistical workflows in Python: define the outcome, predictors, model family, and estimator; fit the model; then examine tests, intervals, residuals, and diagnostics. Preserve the formula or design matrix, missing-data decisions, weights, covariance estimator, software version, and complete output, and assess sensitivity before interpreting parameters.
Research tasks
- Estimate regression, generalized linear, and time-series models
- Run statistical tests and calculate confidence intervals
- Examine residuals, assumptions, and model diagnostics
What to evaluate before use
- Successful estimation does not justify a causal interpretation. Study design, variable definitions, missingness, and model assumptions require separate evidence.
- Covariance estimators, weights, optimization settings, and data encoding can materially change results; report the exact configuration.
Verification note
This entry summarizes the tool's role without assessing scientific accuracy or endorsing its outputs. Features and terms can change; consult the official source before adopting it for consequential work.
statistical modelingeconometrics
Last verified: 2026-09-09
Source: official documentation ↗