Sunday data/statistics link roundup (2/24/2013)

  1. An attempt to create a version of knitr for stata (via John M.). I  like the direction that reproducible research is moving - toward easier use and wider spread adoption. The success of iPython notebook is another great sign for the whole research area.

    1. Email is always a problem for me. In the last week I’ve been introduced to a couple of really nice apps that give me insight into my email habits (Gmail meter - via John M.) and that help me to send reminders to myself with minimal hassle (Boomerang - via Brian C.)
    2. Andrew Lo proposes a new model for cancer research funding based on his research in financial engineering. In light of the impending sequester I’m interested in alternative funding models for data science/statistics in biology. But the concerns I have about both crowd-funding and Lo’s idea are whether the basic scientists get hosed and whether sustained funding at a level that will continue to attract top scientists is possible.
    3. This is a really nice rundown of why medical costs are so high. They key things in the article to me are that: (1) he chased down the data about actual costs versus charges and (2) he highlights the role of the chargemaster - the price setter in medical centers - and how the prices are often set historically with yearly markups (not based on estimates of costs, etc.), and (3) he discusses key nuances like medical liability if the “best” tests aren’t run on everyone. Overall, it is definitely worth a read and this seems like a hugely important problem a statistician could really help with (if they could get their hands on the data).
    4. A really cool applied math project where flying robot helicopters toss and catch a stick. Applied math can be super impressive, but they always still need a little boost from statistics, ““This also involved bringing the insights gained from their initial

    and many subsequent experiments to bear on their overall system

    design. For example, a learning algorithm was added to account for

    model inaccuracies.” (via Rafa via MR). 6. We’ve talked about trying to reduce meetings to increase producitivity before. Here is an article in the NYT talking about the same issue (via Rafa via Karl B.). Brian C. made an interesting observation though, that in a soft money research environment there should be evolutionary pressure against anything that doesn’t improve your ability to obtain research funding. Despite this, meetings proliferate in soft-money environments. So there must be some selective advantage to them! Another interesting project for a stats/evolutionary biology student. 7. If you have read all the Simply Statistics interviews and still want more, check out


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