aibot88 Nominal Outcome — Class Distribution Diagnostics & Primary Association Test
Open-source skill.
Workflow
Read each step file in workflow/ before executing that step.
| Step | File | Executor | Output |
|---|---|---|---|
| Collect | workflow/01-collect-inputs.md |
Main Agent | Structured input summary |
| Diagnose | workflow/02-check-distribution.md |
Main Agent | PART 1 code block |
| Test | workflow/03-run-primary-test.md |
Main Agent | PART 2-3 code blocks |
Decision Tree
1. VALIDATE OUTCOME TYPE
├── Only 2 levels → redirect to vera-stat-binary-testing
├── Ordered levels → redirect to vera-stat-ordinal-testing
└── 3+ unordered levels → proceed
2. PRIMARY ASSOCIATION TEST
├── Primary predictor is categorical → Chi-square + Cramér's V
└── Primary predictor is continuous → One-way ANOVA (predictor ~ outcome) or Kruskal-Wallis
Required Inputs
| Role | What to collect |
|---|---|
| Outcome (Y) | Variable name, what categories represent, confirm NO ordering |
| Number of levels | Confirm 3+ unordered categories |
| Primary predictor | Variable name, type (categorical or continuous) |
| Predictors | For recommendation block (not executed) |
| Covariates | For recommendation block (not executed) |
Code Structure
PART 0: Setup & Data Loading
PART 1: Class Distribution Diagnostics → plot_01_class_distribution.png
PART 2: Primary Association Test → plot_02_association_[var].png
PART 3: Recommendation Block → text listing additional analyses available
Reporting Standards
- p-values: "< .001" not "0.000"; exact to 3 decimals otherwise
- Effect sizes: Cramer's V (Chi-square), eta-squared (ANOVA) — always alongside p
- 95% CIs: always for effect sizes when available
- Degrees of freedom: always with Chi-square and F statistics
- Sample size: final analytic N
- Decimal places: 2 for frequencies/proportions, 3 for p and effect sizes
- Non-significance: "not statistically significant at alpha = .05" — never "no association"
- Chi-square: report observed vs expected when informative
- Always state which category is the reference (for later multinomial modeling)
Association Tests
| Predictor Type | Primary Test | Confirmation |
|---|---|---|
| Categorical predictor | Chi-square + Cramer's V | Fisher's exact (if any cell < 5) |
| Continuous predictor | One-way ANOVA (predictor ~ outcome as factor) | Kruskal-Wallis |
Example Dataset
R built-in iris: outcome = Species (setosa/versicolor/virginica, 3 unordered classes).
Predictors: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width.
Python: from sklearn.datasets import load_iris (preferred — fully offline) or sm.datasets.get_rdataset("iris").
Cross-Skill Interface
Output:
├── code_r → .R script
├── code_python → .py script
├── figures/ → 2 PNGs (class distribution + association plot)
└── recommendations → text block (additional analyses available)