القائمة
Run or generate Pingouin code for linear regression, binary logistic regression, and mediation analysis with reproducible reporting.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Screen psychology datasets before Pingouin analysis: variable types, missingness, duplicates, long/wide shape, assumption checks, and safer preprocessing.
Run or generate Pingouin code for multivariate comparisons — Hotelling's T-squared test (multivariate_ttest) — plus the multivariate assumption checks box_m (equal covariance) and multivariate_normality, for designs with several dependent variables.
Route psychology statistics requests to the smallest Pingouin workflow. Use when the user asks what analysis to run, gives a psychology design, asks to analyze data with Pingouin, or needs lower-token guidance before code generation.
Compute or generate Pingouin Bayes factors for t tests, correlations, and proportions (bayesfactor_ttest, bayesfactor_pearson, bayesfactor_binom), and read the BF10 already returned by pg.ttest and pg.corr.
Run or generate Pingouin code for categorical / contingency-table analyses — chi-square test of independence, McNemar's paired test, 2x2 crosstabs, and chi-square power — for psychology data with nominal variables.
Run or generate Pingouin code for rank-based non-parametric tests — Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, Friedman, and Cochran Q — when outcomes are ordinal or parametric assumptions fail.
| name | pg-regression-mediation |
| description | Run or generate Pingouin code for linear regression, binary logistic regression, and mediation analysis with reproducible reporting. |
Use for regression-style questions that Pingouin supports directly.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.md../../references/apa-output-template.md if writing results.pg.linear_regression.pg.logistic_regression with X, y.pg.mediation_analysis.x, m, y.Linear regression:
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:
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:
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)
np.exp(coef).logistic_regression takes X, y; verify the signature before using examples from older wrappers.