Did I already mention this MOOC watching habit I developed over the summer? I got sucked in to watching lectures online from all sort of classes. It is sort of like being in college again, but when I fall asleep during lecture, I can rewind when I wake up (if I want to).
One of the classes that I devoured video lectures from is , taught by Duke neuroscience prof Dale Purves. It’s got a little bit of that evolutionary-psychologist-explains-everything flavor, and a lot of visual illusions to use-not-abuse in data visualizations.
I heard an interesting talk a few weeks ago about “age-heaping” in survey responses, the phenomenon where people remember ages imprecisely and say that their siblings are ages that are divisible by 5 much more often than expected. There are some nice theory challenges here, with a big dose of stats modeling, but I’ll have to share some more thoughts on that later.
In the talk, the age-heaping was also referred to a a hedgehog or porcupine plot, because of the spikey histogram that the data produces. I was looking for a nice picture of one, or some additional background reading, and when I searched for “hedgehog statistical plots”, all google would give me was a bunch of pages about stats on actual hedgehogs. Cute!
Too bad for me, my first global health paper will have to be revised and resubmitted. In addition to some more substantive objections, the negative reviewer said “It is unclear what software was used to carry out the Bayesian estimation by MCMC. This is not possible in STATA and would be extremely difficult in the scripting language, Python.” It was difficult in Python! I doubt that any software would make it much easier, though.
One of the first things on Obama’s agenda after being sworn in as President last week was lifting the “global gag rule”, a Regan-era innovation that tied US aid to strict anti-choice regulations. Meanwhile, the TCS reading group at UW has been studying matching problems and Edmond’s blossom algorithm. Together, this has been the motivation I needed to launch a series of posts about applications of matchings in reproductive health metrics. Part 1 will have more about matchings.