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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44
｜
第
2
章
其次，需要确定抽样过程。它可以是“随机选出
100
名客户”。如果涉及从数据流（如实
时的客户交易或网站访问者）中进行抽样，那么就必须考虑到时间因素（例如，工作日上
午
10
点的访问者可能与周末晚上
10
点的访问者有很大差异）
。
在
分层抽样
中，总体被分为多个
层
，随机抽样是在每个层中进行的。政治性民意调查可能
想了解美国白人、非裔美国人和拉美裔美国人的选举倾向。对总体的简单随机抽样会导致
样本中非裔和拉美裔美国人的数量过少，所以在分层抽样中要赋予这些层更高的权重，以
得到大小相等的样本。
2.1.3
数量和质量
：
什么时候数量更重要
令人惊讶的是，在大数据时代，有时候数据量反而越小越好。在随机抽样上花费一些时间
和精力，不仅能减小偏差，还可以让我们集中精力进行数据探索和提高数据质量。例如，
缺失的数据和离群值可能包含非常有用的信息，在数百万条记录中追踪缺失值或估计离
群点需要花费的成本几乎是不可接受的，但在包含几千条记录的样本中，这些操作是可行
的。如果数据过多，那么绘图和人工检查也会十分困难。
那么，什么时候才需要海量数据呢？
体现大数据价值的一个经典情形是数据不仅规模巨大而且还很稀疏。我们看一下
Google
收
到的搜索查询请求，其中的列是名词，行是搜索查询，单元格的值是
0
或
1
，依查询中是否
包含该名词而定。目的是为一个特定查询确定最优的搜索目标。英语中的单词超过
15
万
个，
Google
每年处理的查询也超过
1
万亿次。这造就了一个巨大的矩阵，其中绝大多数元素是
0
。
这是一个真实的大数据问题——只有积累了海量数据，才能为多数查询返回有效的搜索结 ...
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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.