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giscus-bot giscus-bot 2022-12-16 15:59:51
访客 *lixiaoxu* @ 2009-02-25 14:46:21 写道:

这个问题文献中被引滥的成语是 if all you have is a hammer, everything looks like a nail

@lixiaoxu

yihui yihui 2022-12-16 15:59:56

哈哈,所言极是!

——原帖发布于 2009-02-25 14:57:56

giscus-bot giscus-bot 2022-12-16 15:59:52
访客 *lixiaoxu* @ 2009-02-25 14:49:18 写道:

如有引用需要,可参考 wiki 之 Golden_hammer 条目考证出典

@lixiaoxu

giscus-bot giscus-bot 2022-12-16 15:59:53
Guest *xiyting* @ 2009-02-25 15:12:09 originally posted:

Nonparametric statistics is distribution-free.

giscus-bot giscus-bot 2022-12-16 15:59:54
访客 *cloud_wei* @ 2009-02-25 17:59:49 写道:
统计分析并同于找对象,而是带有探索的意味。
似乎笔误了,应为“统计分析并不同于找对象……”

@taiyun

yihui yihui 2022-12-16 15:59:57

呃……谢谢,谢谢!貌似我经常把话写反……汗

——原帖发布于 2009-02-25 19:02:40

giscus-bot giscus-bot 2022-12-16 15:59:55
访客 *wind* @ 2009-02-25 19:13:51 写道:

不熟悉对各个分布进行检验。干脆画个图。直观一点。就我的实践而言,通常我宁可使用直接从数据本身产生的密度函数,而不是找一个接近的标准分布。主要因为对各种分布也不太熟悉。 😳

hist(len, breaks = 50, col = "blue", border = "blue", 
    freq = FALSE)
lines(density(len), col = "blue")
x = seq(1, 100, 1)
lines(x, dnorm(x, mean = mean(len), sd = sd(len)), 
    type = "l", col = "red")
lines(x, dpois(x, lambda = sd(len)), type = "l", col = "black")
legend(50, 0.07, c(" 数据密度", "正态分布", "泊松分布"), 
    text.col = c("blue", "red", "black")) 

http://picasaweb.google.com/lh/photo/BdBUd_QCmXQRMenSn6ZOJA?feat=directlink

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