vera-stat-nominal-testing

# 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 1. p-values: "< .001" not "0.000"; exact to 3 decimals otherwise 2. Effect sizes: Cramer's V (Chi-square), eta-squared (ANOVA) — always alongside p 3. 95% CIs: always for effect sizes when available 4. Degrees of freedom: always with Chi-square and F statistics 5. Sample size: final analytic N 6. Decimal places: 2 for frequencies/proportions, 3 for p and effect sizes 7. Non-significance: "not statistically significant at alpha = .05" — never "no association" 8. Chi-square: report observed vs expected when informative 9. 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) ```