October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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20
｜
第
1
章
图 1-3：美国各州人口直方图
统计学中的矩
在统计学理论中，位置和变异性被称作数据分布的
一阶矩
和
二阶矩
，三阶矩
和四阶矩则称为
偏度
和
峰度
。偏度表示数据是偏向较大的值还是较小的值，
而峰度则表示数据中具有极端值的倾向性。通常，我们不使用某种具体的度
量方式来测量偏度和峰度，而是通过像图
1-2
和图
1-3
这样的可视化方式来
发现它们。
1.5.3
密度图和密度估计
密度图将数据值的分布表示为一条连续的曲线，它与直方图密切相关。尽管密度图通常是
通过
核密度估计
（
kernel
density estimate
）（
[Duong-2001]
中有一个简短的教程）根据数据
直接计算出来的，但完全可以把它看作平滑的直方图。图
1-4
给
出了一个叠加在直方图上
的密度估计。在
R
中，你可以使用
density
函数计算出密度估计：
hist(state[['Murder.Rate']], freq=FALSE)
lines(density(state[['Murder.Rate']]), lwd=3, col='blue')
pandas
提供了
density
方法来创建密度图，使用参数
bw_method
可以控制密度曲线的平滑度：
探索性数据分析
｜
21
ax = state['Murder.Rate'].plot.hist(density=True, ...
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