msa op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.msa_anova_table(table, part='part', operator='operator', value='value') (to call the implementation directly, import spc; spc.msa_anova_table(table, part='part', operator='operator', value='value'); from the registry, opsspc.get("msa_anova_table"))
Analysis-of-variance table for a crossed two-factor design (part x operator x replicate) (`table`).
> The detailed description below is the original text — the summary and the headings are translated.
*table* は列 *part* / *operator* / *value* を持つ表。設計は釣り合っている
こと(升目ごとの繰り返し数が同じ)——釣り合っていなければ拒む。
返りは source / ss / df / ms / f / p の 6 列。`f と p` は測定者と交互作用を
交互作用平均平方で、交互作用を誤差平均平方で検定した値(規格の慣行)。
★門にできる厳密な恒等式: `ss_total == ss_part + ss_oper + ss_inter + ss_err`。
これは代数的な分解なので、分散成分をどう出そうと必ず成り立つ —— 片方が壊れれば
一致しない。自由度も `df_total == 和` で閉じる。
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_measurement_system_analysis — py -3.11 examples/poc_measurement_system_analysis.py
table as input)msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo · gum_validate
msa)msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement
*Provenance: spc.py — SPC operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.