这是一个缓慢的,逐步的版本.
这是你的数据.
population_mean <- 0
population_sd <- 1
n <- 1000
x <- rnorm(n, population_mean, population_sd)
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这些是x绘制曲线的一些坐标.注意使用qnorm从正态分布中获得较低和较高的分位数.
population_x <- seq(
qnorm(0.001, population_mean, population_sd),
qnorm(0.999, population_mean, population_sd),
length.out = 1000
)
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为了从密度转换为计数,我们需要知道binwidth.如果我们自己指定它,这是最简单的.
binwidth <- 0.5
breaks <- seq(floor(min(x)), ceiling(max(x)), binwidth)
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这是我们的直方图.
hist(x, breaks)
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计数曲线是正常密度乘以数据点数除以binwidth.
lines(
population_x,
n * dnorm(population_x, population_mean, population_sd) * binwidth,
col = "red"
)
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让我们再看一下样本分布而不是人口分布.
sample_mean <- mean(x)
sample_sd <- sd(x)
sample_x <- seq(
qnorm(0.001, sample_mean, sample_sd),
qnorm(0.999, sample_mean, sample_sd),
length.out = 1000
)
lines(
population_x,
n * dnorm(sample_x, sample_mean, sample_sd) * binwidth,
col = "blue"
)
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