andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1146 knowledge-graph by maker-knowledge-mining
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Introduction: Wayne Folta points us to this list .
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same-blog 1 0.99999994 1146 andrew gelman stats-2012-01-30-Convenient page of data sources from the Washington Post
Introduction: Wayne Folta points us to this list .
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Introduction: Wayne Folta writes that the World Bank is opening up some of its data for researchers.
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Introduction: Wayne Folta sends in this . It seems nuts to me (although I was happy to see that no mention was made of this horrible argument of a related sort). But I know nothing about theoretical physics so I suppose it’s all possible. I certainly have no sense of confidence in anything I’d say about the topic.
Introduction: Wayne Folta points me to “EigenBracket 2012: Using Graph Theory to Predict NCAA March Madness Basketball” and writes, “I [Folta] have got to believe that he’s simply re-invented a statistical method in a graph-ish context, but don’t know enough to judge.” I have not looked in detail at the method being presented here—I’m not much of college basketball fan—but I’d like to use this as an excuse to make one of my favorite general point, which is that a good way to characterize any statistical method is by what information it uses. The basketball ranking method here uses score differentials between teams in the past season. On the plus side, that is better than simply using one-loss records (which (a) discards score differentials and (b) discards information on who played whom). On the minus side, the method appears to be discretizing the scores (thus throwing away information on the exact score differential) and doesn’t use any external information such as external ratings. A
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Introduction: Wayne Folta writes: In keeping with your interest in graphs, this might interest or inspire you, if you haven’t seen it already, which features 20 scientific graphs that Wired likes, ranging from drawn illustrations to trajectory plots. My reaction: I looked at the first 10. I liked 1, 3, and 5, I didn’t like 2, 7, 8, 9, and 10. I have neutral feelings about 4 and 6. I won’t explain all these feelings, but, just for example, from my perspective, image 9 fails as a statistical graphic (although it might be fine as an infovis) by trying to cram to much into a single image. I don’t think it works to have all the colors on the single wheels; instead I’d prefer some sort of grid of images. Also, I don’t see the point of the circular display. That makes no sense at all; it’s a misleading feature. That said, the graphs I dislike can still be fine for their purpose. A graph in a journal such as Science or Nature is meant to grab the eye of a busy reader (or to go viral on
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Introduction: Wayne Folta points us to this list .
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Introduction: Wayne Folta sends in this . It seems nuts to me (although I was happy to see that no mention was made of this horrible argument of a related sort). But I know nothing about theoretical physics so I suppose it’s all possible. I certainly have no sense of confidence in anything I’d say about the topic.
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Introduction: Wayne Folta writes that the World Bank is opening up some of its data for researchers.
Introduction: Wayne Folta points me to “EigenBracket 2012: Using Graph Theory to Predict NCAA March Madness Basketball” and writes, “I [Folta] have got to believe that he’s simply re-invented a statistical method in a graph-ish context, but don’t know enough to judge.” I have not looked in detail at the method being presented here—I’m not much of college basketball fan—but I’d like to use this as an excuse to make one of my favorite general point, which is that a good way to characterize any statistical method is by what information it uses. The basketball ranking method here uses score differentials between teams in the past season. On the plus side, that is better than simply using one-loss records (which (a) discards score differentials and (b) discards information on who played whom). On the minus side, the method appears to be discretizing the scores (thus throwing away information on the exact score differential) and doesn’t use any external information such as external ratings. A
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Introduction: Mayo points me to this discussion [link fixed] on parsimony by philosopher Elliott Sober. I don’t really understand what he’s talking about but I am posting the link here because it might interest some of you. P.S. More discussion on this from Mayo here .
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Introduction: Wayne Folta points us to this list .
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Introduction: We have lots of models for overdispersed count data but we rarely see underdispersed data. But now I know what example I’ll be giving when this next comes up in class. From a book review by Theo Tait: A number of shark species go in for oophagy, or uterine cannibalism. Sand tiger foetuses ‘eat each other in utero, acting out the harshest form of sibling rivalry imaginable’. Only two babies emerge, one from each of the mother shark’s uteruses: the survivors have eaten everything else. ‘A female sand tiger gives birth to a baby that’s already a metre long and an experienced killer,’ explains Demian Chapman, an expert on the subject. That’s what I call underdispersion. E(y)=2, var(y)=0. Take that, M. Poisson!
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Introduction: Wayne Folta writes that the World Bank is opening up some of its data for researchers.
Introduction: That’s ok , Krugman earlier slammed Galbraith. (I wonder if Krugman is as big a fan of “tough choices” now as he was in 1996 .) Given Krugman’s politicization in recent years, I’m surprised he’s so dismissive of the political (rather than technical-economic) nature of Hayek’s influence. (I don’t know if he’s changed his views on Galbraith in recent years.) P.S. Greg Mankiw, in contrast, labels Galbraith and Hayek as “two of the great economists of the 20th century” and writes, “even though their most famous works were written many decades ago, they are still well worth reading today.”
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Introduction: Diederik Stapel gives a Ted talk . Sometimes, reality truly is a parody of reality.
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