科学与数学itl.nist.gov编辑精选
工程统计手册
Engineering Statistics Handbook
2015 — 2021，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
美国国家标准与技术研究院（NIST）发布的《工程统计手册》，是一本免费在线的统计学参考书，涵盖探索性数据分析、测量过程建模、质量控制等工程中常用的统计方法。多次登上 HN 首页，被视为经得起时间检验的权威资料。
为什么经久不衰：由权威机构编写、免费公开且内容扎实，是工程师和数据从业者随时可查的统计学案头参考。
HN 评论精选
推荐这本手册，认为 NIST 的资源被大众低估
NIST 的人实在被低估了。我数学底子不好，尤其统计薄弱，已经把这本书收藏了。直到上了计算语言学的统计方法课，我才真正对统计产生兴趣。
616c查看英文原文
I have to say, NIST dudes are/were so underappreciated by the likes of me. Is it just me? I was in a LUG in the DC area and one of the dudes there was a NIST guy. His use of Linux and Unix was not surprising, but when he would tangentially mention what they were doing with their boxes I would always wonder: how the hell do more people not pay attention to NIST? But maybe that is just me. I have done a lot of sysadmin in the education industry, where we are famous for listening to no one and being the butt of security nightmare jokes. As someone who is weak in all post-high-school maths, and stats in general, I have definitely bookmarked this for later. I did not care for stats until I finally took my first true computation linguistics class, Statistical Methods for NLP, and years of wannabe linguistics without math seemed pretty retarded after that. Thanks for a cool link.
推荐 NIST 另一部数学函数手册 DLMF
这本手册很好，但还可以看看 NIST 的数学函数手册在线版（DLMF），它是 Abramowitz and Stegun 经典著作的现代继任者。
gtuckerkellogg查看英文原文
As good as that is, see this: http://dlmf.nist.gov/, the online companion to the truly epic NIST Handbook of Mathematical Functions, itself the modern successor to Abramowitz and Stegun.
以实习生身份印证 NIST 研究质量高
我十年前在 NIST 物理部门做过暑期实习：科学家一流、设施不错、经费充足、研究目标明确，管理也基本放手，是很棒的工作地方。
arcanus查看英文原文
I worked at NIST about 10 years ago, for a summer internship in the Physics division. Absolutely top notch scientists, some good facilities, a decent budget and very well scoped research objectives. Management was also largely hands-off. Great place to work.
指出 EDA 与商业智能行业的关联
书里探索性数据分析（EDA）一章讲的各种图形方法，其实和商业智能（BI）行业做的事本质相同——把运营数据聚合起来支持分析决策，QlikView、Tableau 等都是这个领域的产品。
bmh100查看英文原文
Looking at the chapter on Exploratory Data Analysis (EDA), it mentions that "EDA techniques are generally graphical. They include scatter plots, character plots, box plots, histograms, bihistograms, probability plots, residual plots, and mean plots." There is another term for EDA, and it has a whole industry built around it: business intelligence (BI). BI is focused on aggregating data from operational systems (OLTP) and producing new systems optimized for analytics and decision making (OLAP). Top products in this space include: QlikView, Cognos, Tableau, Hana, etc. One can make a decent hourly rate working as a consultant in these systems.
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