Writing

Proper Analysis of Salary vs. Performance?

Got an email from Mebane Faber who noted the roughly inverse correlation you currently see in salaryper, and asking about whether I’d done proper year-end analysis. The response follows:

I threw the project together as sort of a fun thing out of curiosity, and haven’t taken the time to do a proper analysis. However you can see in the previous years that the inverse relationship happens each year at the beginning of the season, and then as it progresses, the big market teams tend to mow down the small guys. Or at least those that are successful–the correlation between salary and performance at the end of a season is generally pretty haphazard. In fact, it’s possible that the inverse correlation at the beginning of the season is actually stronger than the positive correlation at the end.

I think the last point is kinda funny, though I’d imagine there’s a less funny statistics term for that phenomenon. Such a fine line between funny and sounding important.

Friday, June 6, 2008 | feedbag, salaryper  
Book

Visualizing Data Book CoverVisualizing Data is my book about computational information design. It covers the path from raw data to how we understand it, detailing how to begin with a set of numbers and produce images or software that lets you view and interact with information. Unlike nearly all books in this field, it is a hands-on guide intended for people who want to learn how to actually build a data visualization.

The text was published by O’Reilly in December 2007 and can be found at Amazon and elsewhere. Amazon also has an edition for the Kindle, for people who aren’t into the dead tree thing. (Proceeds from Amazon links found on this page are used to pay my web hosting bill.)

Examples for the book can be found here.

The book covers ideas found in my Ph.D. dissertation, which is basis for Chapter 1. The next chapter is an extremely brief introduction to Processing, which is used for the examples. Next is (chapter 3) is a simple mapping project to place data points on a map of the United States. Of course, the idea is not that lots of people want to visualize data for each of 50 states. Instead, it’s a jumping off point for learning how to lay out data spatially.

The chapters that follow cover six more projects, such as salary vs. performance (Chapter 5), zipdecode (Chapter 6), followed by more advanced topics dealing with trees, treemaps, hierarchies, and recursion (Chapter 7), plus graphs and networks (Chapter 8).

This site is used for follow-up code and writing about related topics.

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