SWASSSWAA 发表于 2024-5-13 14:34:50

关于归一化偏差En值法的讨论


实验室 测得值 扩展不确定度U(k=2)
A(主导实验室) 10.00008 v 0.00009 v
B(参比实验室) 10.00001 v 0.00011 V
C(参比实验室) 10.00017 v 0.00009 v
D(参比实验室) 10.00005 v 0.00010 v
E(参比实验室) 10.00004 V 0.00010 v
                         各实验室比对测量结果汇总表
比对采用各实验室测得值的算术平均值作为参考值,各实验室的测量结果如表所示。求本次比对参考值,和参考值的标准不确定度,并用归一化偏差En值法,对每个实验室的比对结果进行评定。

根据表中数据计算出参考值为10.00007V,标准不确定度u=0.000022V。归一化偏差值公式data:image/png;base64,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其中分母的ui应为data:image/png;base64,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还是data:image/png;base64,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呢?



补充内容 (2024-5-13 15:40):
图片漏了,在4楼

SWASSSWAA 发表于 2024-5-13 14:37:07

答案是加。但是我认为应该是减,因为参比实验室的结果参加了参考值的确定。

补充内容 (2024-5-13 15:48):
见4楼

SWASSSWAA 发表于 2024-5-13 14:45:50

这一题使用加权平均值作为参考值,,计算得到参考值结果为0.497,标准不确定度为0.000929。
在计算G实验室的En值时,为什么分母中的ui用的是标准不确定度为0.000929,而不是用公式计算呢?

SWASSSWAA 发表于 2024-5-13 15:45:40

本帖最后由 SWASSSWAA 于 2024-5-13 15:47 编辑

归一化偏差值公式
其中分母的ui应为还是

答案是加,我认为是减,因为参比实验室结果参加参考值的确定

吾以观复 发表于 2024-5-13 15:54:40

这两题《一级注册计量师资格考试典型习题及解答剖析》2022版里有,参考答案都给的是减。

SWASSSWAA 发表于 2024-5-13 16:20:41

吾以观复 发表于 2024-5-13 15:54
这两题《一级注册计量师资格考试典型习题及解答剖析》2022版里有,参考答案都给的是减。 ...

好的,我找找教材,非常感谢!

丸子123 发表于 2024-5-14 10:26:29

应该是根号下参比实验室的u值的平方+参考 标准u的平方呀,大纲上的列题都是这样的

SWASSSWAA 发表于 2024-5-14 14:58:05

丸子123 发表于 2024-5-14 10:26
应该是根号下参比实验室的u值的平方+参考 标准u的平方呀,大纲上的列题都是这样的 ...

“大纲上的列题都是这样的”是什么意思呢?大纲上列出了相关内容?

liudayang 发表于 2024-6-9 12:11:22

感谢大神分享!!!!!!
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