xxz4804 发表于 2023-11-21 15:10:14

{:1_180:}

doondoon 发表于 2023-11-21 21:00:19

{:1_180:}

cht6688 发表于 2023-11-22 07:26:41

:handshake

铁牛123 发表于 2023-11-22 08:57:38

学习了

MT008 发表于 2023-11-23 09:46:12

{:1_89:}

ZHANG1234 发表于 2023-11-23 14:27:17

{:1_180:}

flyerchang 发表于 2023-11-23 14:39:34

谢谢分享

泛舟吴楚 发表于 2023-11-24 17:09:29

本帖最后由 泛舟吴楚 于 2023-11-24 17:10 编辑

K1/K2/K3的系数怎么取值啊,网上下载的模板,取值不太一样
比如K1有的取3.05,有的取0.59......K2/K3也不一样,哪个是对的啊

小孟庄生 发表于 2023-11-25 08:40:59

本帖最后由 小孟庄生 于 2023-11-25 08:55 编辑

泛舟吴楚 发表于 2023-11-24 17:09
K1/K2/K3的系数怎么取值啊,网上下载的模板,取值不太一样
比如K1有的取3.05,有的取0.59......K2/K3也不一 ...
参考MSA手册第四版:再现性或评价人变差(AV 或 σA)由评价人平均值的最大差值(XDIFF)乘以一个常数 K2来决定。K2 取决于量具研究中的评价人的人数,其值为从附录 C 中查到的 d2*的倒数,d2*取决于评价人的人数(m)且 g=1,因为只有一个极差计算。

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

泛舟吴楚 发表于 2023-11-25 10:08:53

小孟庄生 发表于 2023-11-25 08:40
参考MSA手册第四版:再现性或评价人变差(AV 或 σA)由评价人平均值的最大差值(XDIFF)乘以一个常数 K2 ...

在网上找到2份EXCEL模板,这两个模板中K1/K2/K3的取值完全不一样,哪个是对的呢

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查看完整版本: Excel和Minitab之间的MSA结果不一致,求教如何处理