pg-regression-mediationlisted

# PG Regression Mediation Use for regression-style questions that Pingouin supports directly. ## Load Read: - `../../references/supervision-gates.md` - `../../references/pingouin-api-quickref.md` - `../../references/apa-output-template.md` if writing results. ## Function Choice - Continuous outcome, additive linear predictors -> `pg.linear_regression`. - Binary outcome -> `pg.logistic_regression` with `X, y`. - Single or multiple mediator path model -> `pg.mediation_analysis`. ## Required Inputs - Outcome variable and scale. - Predictor list. - Covariates and whether they are theoretical controls. - Binary coding for logistic regression. - Mediation paths: `x`, `m`, `y`. - Bootstrap count and seed for mediation. ## Code Patterns Linear regression: ```python vars_needed = ["outcome", "x1", "x2"] tmp = df.dropna(subset=vars_needed) res = pg.linear_regression(tmp[["x1", "x2"]], tmp["outcome"], add_intercept=True).round(3) pg.print_table(res) ``` Logistic regression: ```python vars_needed = ["binary_outcome", "x1", "x2"] tmp = df.dropna(subset=vars_needed) res = pg.logistic_regression(tmp[["x1", "x2"]], tmp["binary_outcome"], remove_na=False).round(3) pg.print_table(res) ``` Mediation: ```python res = pg.mediation_analysis(data=df, x="x", m="mediator", y="outcome", covar=["age"], n_boot=5000, seed=42).round(3) pg.print_table(res) ``` ## Reporting - Regr