质量复习---抽样统计知识(2)
发布于:2009-3-31 已被阅读: 次 

在诸如汽车业的一些产业中,人们将对CPK的计算称作PPK。至于人们为什么用32或者更多的数据来计算CPK,我对此做了一些研究。人们在运算中视cp1。33为普通理解与当前能力与4sigma的离差保持一致。如果你用这个数据做一些计算然后对照美国质量出版社出版的表格。(我曾尝试着邮寄那张表,但都没有成行。这简直太糟了,我猜想管理部门遗失了该邮件)。你可以取值接近32,但即使32有时候也不足以得到一个没有误差的加工能力比率

What I wanna stress again is that capability ratio is not everything, there are too many misuses in the industry, don’t count all on it.我想再一次强调的是加工能力比率并不是万能的,在工业上有很多的误用,不要全部依靠它来计算。

Here is my answer to the question of 32 sample size:这里是我对样本尺寸为32的问题的回答。

A practice that is increasingly common in industry is to require a supplier to demonstrate process capability as part of the

contractual agreement.

Thus, it is frequently necessary to prove that the process capability ratio Cp meets or exceeds some particular target value---say, Cp0. This problem may be formulated as a hypothesis testing problem:

一个要在工业中日渐成熟的练习是需要一个供应者示范如契约的协议部份般的程序能力。 因此,有必要经常证明加工能力比率CP等于或者超过如CP0的一些特殊目标价值。这个问题可能被制定为一个假设的测试问题:

H0: Cp= Cp0 (or the process is not capable)

H1: Cp≥ Cp0 (or the process is capable)

We would like to reject H0 (recall that in statistical hypothesis testing rejection of Null hypothesis is always a strong conclusion),

thereby demonstrating that the process is capable. We can formulate the statistical test in terms of Cp’,

so that we will reject H0 if Cp’ exceeds a critical value C.

我们想要否定H0( 取消对统计的假设中无效力假设的测试否定一直是一个强大的结论)。因此,示范加工是有能力的。我们可以根据 Cp’ 制定统计的测试, 所以如果 Cp’超过一个关键的价值 C,那么我们会否定H0 。

Kane(1986) has investigated this test, and provide a table of sample sizes and critical values for C to assist in testing process capability.

We may define Cp(High) as a process capability that we would like to accept with probability (1-α) and Cp(low) as a process capability that we’d like to reject with probability (1-β). Please refer to the table created by Kane and used by American Society for Quality Control.

凯恩 (1986) 已经调查这上述测试, 而且向C提供一张有样品大小和关键值的表给来协助测试的加工能力。就如我们喜欢接受(1-α)的可能性和CP(低)作为程序能力和否定(1-β)的可能性一样,我们可以将CP(高)定义为一个加工能力。请查阅凯恩所创建的并为美国社会质量控制所用的表格

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