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).
Regresses the bias bias = measured - reference on the reference value (bias = intercept + slope * reference). If the slope is not 0, the measurement system is off by a different amount depending on where in the measuring range it is = a linearity problem.
The result is the mean bias per reference value (ref / bias_mean / n), the regression's intercept / slope / each one's standard error, t value and p value, and the overall mean bias.
★ An exact property usable as a gate: given a noise-free bias = a + b*ref, least squares returns a and b exactly (residual 0). The standard error is then 0, so t is inf / p is 0 -- this is not a defect but the correct report of a perfect fit, so it is returned as is.
• 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.