msa op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.msa_bias_linearity(table, reference='reference', measured='measured') (to call the implementation directly, import spc; spc.msa_bias_linearity(table, reference='reference', measured='measured'); from the registry, opsspc.get("msa_bias_linearity"))
Bias against a reference value and how that bias depends on the reference (linearity) (`table`).
> The detailed description below is the original text — the summary and the headings are translated.
偏り `bias = measured - reference` を基準値に回帰する
(`bias = intercept + slope * reference`)。傾きが 0 でなければ、測定系は
測定範囲の場所によって違う量だけずれている = 直線性の問題。
返りは基準値ごとの平均偏り(`ref / bias_mean / n`)と、回帰の
`intercept / slope` / それぞれの標準誤差・t 値・p 値、全体平均偏り。
★門にできる厳密な性質: 雑音の無い `bias = a + b*ref` を渡すと最小二乗は
a と b を厳密に返す(残差 0)。そのとき標準誤差は 0 なので t は `inf` /
p は 0 —— これは欠陥ではなく完全適合の正しい報告なので、そのまま返す。
• 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_anova_table · msa_gauge_rr · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo · gum_validate
msa)msa_anova_table · msa_gauge_rr · 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.