求教AQL抽样的函数
本帖最后由 wuqing 于 2022-11-1 11:46 编辑求教AQL“抽样数量”的函数data:image/png;base64,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
都用AQL了。还要函数干嘛?不懂。 因为你的问题问得不清不楚,所以只能告诉你个不清不楚的答案:
请你去看GB2828的抽样计划表,下面注解里面是有说明的,例如
“所有表均基于泊松分布“
或者
”下面的表值适合于不合格品百分数检验,并且基于二项分布“
这个就是你要的答案 {:1_97:} :) {:1_180:} tianxue0221 发表于 2022-10-31 16:05
都用AQL了。还要函数干嘛?不懂。
求教AQL“抽样数量”的函数 kamire 发表于 2022-10-31 16:44
因为你的问题问得不清不楚,所以只能告诉你个不清不楚的答案:
请你去看GB2828的抽样计划表,下面注解里面 ...
求教AQL“抽样数量”的函数 本帖最后由 kamire 于 2022-11-1 14:05 编辑
wuqing 发表于 2022-11-1 11:47
求教AQL“抽样数量”的函数
已经告诉你答案了:
GB2828上面说得很清楚,
"所有表均基于泊松分布"
根据你设定的条件,去计算n
kamire 发表于 2022-10-31 16:44
因为你的问题问得不清不楚,所以只能告诉你个不清不楚的答案:
请你去看GB2828的抽样计划表,下面注解里面 ...
估计是他想要EXCEL函数根据抽样表自动抓取抽样数
页:
[1]
2