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Run or generate Pingouin code for Pearson, Spearman, Kendall, partial, repeated-measures, distance, and pairwise correlations with APA-style reporting.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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-correlations |
| description | Run or generate Pingouin code for Pearson, Spearman, Kendall, partial, repeated-measures, distance, and pairwise correlations with APA-style reporting. |
Use for association analyses between continuous or ordinal variables.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.md../../references/apa-output-template.md if writing results.pg.corr(..., method="pearson").method="spearman" or method="kendall".pg.partial_corr.pg.pairwise_corr with padjust.pg.rm_corr.pg.distance_corr if appropriate.Basic correlation:
res = pg.corr(x=df["x"], y=df["y"], method="pearson",
alternative="two-sided").round(3)
pg.print_table(res)
Partial correlation:
res = pg.partial_corr(data=df, x="x", y="y",
covar=["age", "baseline"],
method="pearson").round(3)
Pairwise correlations:
res = pg.pairwise_corr(data=df, columns=["x", "y", "z"],
method="spearman", padjust="holm").round(3)
Repeated-measures correlation:
res = pg.rm_corr(data=df, x="x", y="y", subject="id").round(3)
Report n, r, CI, p-value, Bayes factor if returned, and method. Include a scatterplot recommendation when interpretation depends on linearity or outliers.
pg.corr when observations repeat within participants.