咨询各位1个关于双样本P检验的事情
背景:产线生产A产品改善前:投入74,有8个不良
改善后:投入66,有2个不良
想看一下改善前后效果是否显著
原假设H0:不显著,没有差异。备择假设:显著,有差异
用minitab,统计--->基本统计--->双比率
选择汇总数据,输入数据,按确定,结果处理P值居然是0.063>0.05,所以选择接受原假设
所以结论是,虽然不良率降低了,但是我们还是认为这个改善不显著,不能接受?
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data:image/png;base64,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
这是我输入数据的方式 对。
你们需要增加样本数量,继续验证。 kamire 发表于 2023-5-9 16:03
对。
你们需要增加样本数量,继续验证。
哈哈哈确实
我把实验数和样本数等比率提高了
不良率还是一样,但是对应的P值只有0.03 zzr0208 发表于 2023-5-9 16:30
哈哈哈确实
我把实验数和样本数等比率提高了
不良率还是一样,但是对应的P值只有0.03 ...
不知道你是真的来请教问题还是来寻开心的。
错误发生的概率理论上是泊松分布,数据量大也可以近似为正态分布,你不管数据的分布直接线性所谓"等比率"提高,当然是已经改变了原始数据的分布,这就不是你想象的”等比率“了,其结果当然会和第一次的不一样。
你要是玩数据,把2改成1那更是直接可以得到你要的结果了,当然自己玩得开心就好。 kamire 发表于 2023-5-9 17:01
不知道你是真的来请教问题还是来寻开心的。
错误发生的概率理论上是泊松分布,数据量大也可以近似为正态 ...
人家不懂啊
kamire 发表于 2023-5-9 17:01
不知道你是真的来请教问题还是来寻开心的。
错误发生的概率理论上是泊松分布,数据量大也可以近似为正态 ...
谢谢分享 kamire 发表于 2023-5-9 17:01
不知道你是真的来请教问题还是来寻开心的。
错误发生的概率理论上是泊松分布,数据量大也可以近似为正态 ...
发帖的初衷当然是来请教的
得到一个回馈后发现原来可以自己编数据
就觉得挺有趣,就形成了你所谓的“寻开心” 不错 双比率检验时要注意满足样本量,n*p⩾15, n*(1-p)⩾15,以保证数据正态分布
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