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).
*table* is a table with columns *part* / *operator* / *value*. The design must be balanced (the same number of repeats in every cell) -- if it is not balanced, it is rejected.
The result has 6 columns: source / ss / df / ms / f / p. f and p test the operator and the interaction against the interaction mean square, and the interaction against the error mean square (the convention of the standard).
★ An exact identity usable as a gate: ss_total == ss_part + ss_oper + ss_inter + ss_err. This is an algebraic decomposition, so it always holds however the variance components are obtained -- if one side breaks, they do not match. The degrees of freedom also close with df_total == sum.
• 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.