v0.11.0 — Week 11 · DOE screening on SECOM

Week 11 · DOE screening on SECOM — an honest screening analysis of which SECOM signals move the pass/fail response, capstoning the real-data story.

Feature (#72 · W11-1)

• DOE screening analysis — secom_app/doe_screening.py: per-signal univariate screen over the select_signals() candidate set. Effect = Cohen's d (pooled SD, FAIL−PASS direction); significance = Welch's two-sample t (correct for the 104-fail-vs-1463-pass groups); multiple comparisons = Benjamini–Hochberg FDR, significant = q < 0.05. Reuses scipy.stats — no statistics re-derived.

Honesty over invention (the series line)

SECOM is observational process-monitoring data — factor levels are never set or randomized — so a real DOE screening design is impossible. This is a screening analysis of association, labelled unmistakably as not a designed experiment and not causal. Thresholds (α=0.05) are labelled screening conventions, not quality standards; methods cite Cohen 1988 / Welch 1947 / Benjamini–Hochberg 1995.

Result on the vendored data

463 candidate signals, 23 significant (BH q<0.05); top signal sensor_059 (Cohen's d 0.632).

Quality

All packages → 0.11.0 (v0.10.0 skipped — Week 10 had no issues). 1069 tests; coverage bars 100% — quality_core.io, quality_core.schema (line+branch), SPC, SECOM (incl. doe_screening), MSA, Control Plan. ruff + mypy clean.

Full detail: CHANGELOG.md ## [0.11.0].