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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数据与抽样分布
｜
71
rweibull(100, 1.5, 5000)
要在
Python
中得到同样的结果，可以使用函数
stats.weibull_min.rvs
：
stats.weibull_min.rvs(1.5, scale=5000, size=100)
本节要点
•
对于发生率固定的事件，每时间单位或每空间单位的事件数量可以使用泊松分布进
行建模。
•
还可以将事件与下一个事件之间的时间或距离建模为指数分布。
•
随着时间而改变的事件发生率（例如，不断增大的设备故障概率）可以使用韦布尔
分布进行建模。
2.12.5
扩展阅读
• Thomas Ryan
的著作
Modern Engineering Statistics
中有一章专门介绍了工程应用中使用
的概率分布。
•
两篇从工程角度讨论韦布尔分布的应用的文章：“
Predicting
Equipment Failures Using
Weibull Analysis and SAS Software
”和“
Estimation
the System Reliability Using Weibull
Distribution
”。
2.13
小结
在大数据时代，如果需要准确的估计，那么随机抽样的原则依然非常重要。与使用未经处
理的数据相比，随机选取数据可以减小偏差，得到一个更高质量的数据集。掌握与各种抽
样分布和数据生成分布相关的知识，便可以量化估计中因随机变动而产生的潜在误差。同
时，
Bootstrap
（
从一个观测数据集进行有放回抽样）是一种非常强大的确定样本估计中误 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.