累积分布函数为:
[img]http://dl2.iteye.com/upload/attachment/0102/2695/e36481d6-9d5e-36e6-ae4d-7f10f7552c3e.jpg[/img]
如下图所示:
[img]http://dl2.iteye.com/upload/attachment/0102/2697/2421a167-f796-3213-9eb4-1bd17d86ba94.jpeg[/img]
[img]http://dl2.iteye.com/upload/attachment/0102/2695/e36481d6-9d5e-36e6-ae4d-7f10f7552c3e.jpg[/img]
set.seed(0)
x <- seq(-5,5,length.out=100)
y <- pnorm(x,0,1)
plot(x,y,col="red",xlim=c(-5,5),ylim=c(0,1),type='l',xaxs="i", yaxs="i",ylab='density',xlab='',main="The Normal Cumulative Distribution")
lines(x,pnorm(x,0,0.5),col="green")
lines(x,pnorm(x,0,2),col="blue")
lines(x,pnorm(x,-2,1),col="orange")
legend("bottomright",legend=paste("m=",c(0,0,0,-2)," sd=", c(1,0.5,2,1)), lwd=1,col=c("red", "green","blue","orange"))
如下图所示:
[img]http://dl2.iteye.com/upload/attachment/0102/2697/2421a167-f796-3213-9eb4-1bd17d86ba94.jpeg[/img]

本文通过图表展示了正态分布下不同标准差和均值参数的累积分布函数特性,包括标准正态分布、均值为0、标准差为0.5的分布、均值为0、标准差为2的分布以及均值为-2、标准差为1的分布,帮助读者直观理解正态分布的多样性和变化。
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