Sunday data/statistics link roundup (12/16/12)

  1. A directory of open access journals. Very cool idea to aggregate them. Here is a blog post from one of my favorite statistics bloggers about why open-access journals are so cool. Just like in a lot of other areas, open access journals can be thought of as an open data initiative.
  2. Here is a website that displays data on the relative wealth of neighborhoods, broken down by census track. It's pretty fascinating to take a look and see what the income changes are, even in regions pretty close to each other.
  3. More citizen science goodness. Zooniverse has a new project where you can look through a bunch of pictures in the Serengeti and see if you can find animals.
  4. Nate Silver talking about his new book with Hal Varian. (via). I have skimmed the book and found that the parts about baseball/politics are awesome and the other parts seem a little light. But maybe that's just my pre-conceived bias? I'd love to hear what other people thought...
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  • anthony damico

    nate silver wants to learn R someone should send him a coursera link :)

  • Dan Scharfstein

    reading the book now. on page 197, with regards to Big Data, he writes "Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where data is so noisy. Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes." What say you Jeffery??

    • jtleek

      I say that on some basic level he is right. Without any structure at all, it is unlikely that predictions will be successful. On the other hand, the "theory" he is talking about is likely very different than the "theory" we would discuss as professors of statistics. He is referring to very basic theoretical ideas like Bayes theorem, cross-validation, and out of sample accuracy. These concepts are incredibly useful. It is not clear what Silver would think of say, asymptotics or MCMC mixing theory.