spc_xbar_r — SPC chart op

• 資料種類:matrix → table

• 呼叫: import fullseye as fs; fs.ledger.spc_xbar_r(subgroups)(要直接呼叫實作,import spc; spc.spc_xbar_r(subgroups);從台帳取用則 opsspc.get("spc_xbar_r"))

用法

> 該運算子的說明尚無譯文,以下照原文給出。

Shewhart Xbar-R control chart from subgroup measurements.

subgroups is a 2-D array of shape (m, n) — m subgroups of n measurements each, 2 <= n <= 10 (the range chart is only calibrated for small subgroups). For each subgroup j the plotted statistics are the mean Xbar_j and the range R_j = max - min. The centre lines are the grand mean Xbarbar and the mean range Rbar, and with the ISO 8258 constants (A2, D3, D4) for that n::

xbar_ucl = Xbarbar + A2*Rbar xbar_lcl = Xbarbar - A2*Rbar

r_ucl = D4*Rbar r_lcl = D3*Rbar

Returns a dict (a "table") with the per-subgroup xbar / r arrays, the six limits and three centre lines, the integer indices out_of_control of subgroups outside either chart, and in_control (True when that list is empty). The constants are fixed by n: for n=5 they are exactly A2=0.577, D3=0.000, D4=2.115 (pinned in the tests).

Raises ValueError: a non-2-D / empty *subgroups*, a subgroup size outside [2, 10], or non-finite / mislabelled input.

參考(範例資料、文獻)

• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。

• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。

• 演算法的正典(作者、年份)與用途見上面的族使用指南。

可執行的範例(實際呼叫該運算子並已驗證的樣例)

• poc_spc — py -3.11 examples/poc_spc.py

型別可銜接的下一個運算子(可接受 table 作為輸入)

msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo

同類別(chart)

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*Provenance: spc.py — SPC 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.