regression-and-forecasting-review

name	regression-and-forecasting-review
description	Review or design a regression model or a time-series forecast so it's defensible — pick the model family (OLS / logistic / Poisson GLM; ARIMA / SARIMAX / ETS), check the assumptions that matter for that family, report honest prediction/confidence intervals, and screen for overfitting, data leakage, and "coefficient = cause" overreach. Used by `applied-statistician` (primary).

Skill: regression-and-forecasting-review

Invoked by: applied-statistician (primary). Escalate model-as-product / heavy ML feature-engineering to ravenclaude-core/data-engineer; financial-model structure to finance (when installed).

When to invoke: "what drives ?"; "build/forecast for next quarter"; "review this regression — is it sound?"

Output: the model-family choice + rationale, the assumption checks for that family, the intervals to report, and a pitfall screen (leakage / overfitting / causal overreach).

Regression

1. Pick the family by outcome type (../../knowledge/test-selection-decision-tree.md): continuous → OLS; binary → logistic GLM; count → Poisson / negative-binomial GLM.
2. Check the assumptions that matter: linearity (residual-vs-fitted), independence, homoscedasticity (else robust SEs), no severe multicollinearity (VIF), influential points (Cook's distance). Normality of residuals matters for small-sample inference, not for the point estimates.
3. Report coefficients with CIs, and translate to plain language ("each +1 in X is associated with +β in Y, 95% CI […]").
4. Causal overreach guard: a regression coefficient is association, not cause, unless the data came from a randomized/causal design. If the user says "drives/causes," route to ../../knowledge/causal-inference-primer.md.

Forecasting (time series)

1. Inspect first: trend, seasonality, level shifts. Plot before modelling.
2. Pick the model: ETS / exponential smoothing for trend+seasonality without exogenous drivers; (S)ARIMA / SARIMAX when you need autocorrelation structure and/or exogenous regressors. statsmodels.tsa is the Tier-1 home.
3. Validate honestly: time-based train/test split (never shuffle), backtest on held-out recent periods, report error (MAE/MAPE) on the holdout — not the training fit.
4. Report prediction intervals, not just the point forecast. A forecast line with no band is overconfident; the band is the deliverable.

Pitfall screen (always run)

• Data leakage (pitfall #9): any feature unavailable at prediction time? transforms fit on the full set? time series shuffled? → fix the split.
• Overfitting: too many predictors for the n; great train fit, poor holdout → simplify / regularize / report holdout error.
• Causal overreach: coefficient reported as a lever to pull, with no causal design → downgrade to "associated with."
• Extrapolation: forecasting far beyond the data's support, or a regression prediction outside the observed predictor range → flag the uncertainty.

Guardrails

• Tooling: statsmodels (regression, GLM, ARIMA/SARIMAX/ETS) is Tier 1; linearmodels (panel/IV) is Tier 2 — reach for it only when the method needs it (../../knowledge/statistics-tooling-2026.md).
• The deliverable is the interval, not the point. Always.