Engineering Statistics Handbook

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.

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.

It (and the book on which it is based) are amazing. Virtually every older applied mathematician has a copy of the original Abramowitz and Stegun, which was supported by the National Bureau of Standards, prior to NIST. The successor (http://www.cambridge.org/us/academic/subjects/mathematics/ab...) is really an incredible achievement. The reviews are hilarious. One notes "The book is quite heavy; for convenience, one might be inclined to place it on a stand, as with an unabridged dictionary". Another review begins "This is like trying to review the bible".

The online version also lets you get LaTeX for everything, and has many other features. But the print version is a thing of beauty.

In full disclosure, the Editor in Chief and Mathematics Editor, who devoted the last 13 years of his professional life to the project, was my stepfather.