andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-871 knowledge-graph by maker-knowledge-mining
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Introduction: Robert Bell points me to this blog by Austin Frakt explaining problems in interpreting regressions that control for intermediate outcomes. As Robert notes, we discuss these issues in chapters 9 and 10. But Frakt’s example is a good one.
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1 Robert Bell points me to this blog by Austin Frakt explaining problems in interpreting regressions that control for intermediate outcomes. [sent-1, score-1.204]
2 As Robert notes, we discuss these issues in chapters 9 and 10. [sent-2, score-0.441]
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same-blog 1 0.99999988 871 andrew gelman stats-2011-08-26-Be careful what you control for . . . you just might get it!
Introduction: Robert Bell points me to this blog by Austin Frakt explaining problems in interpreting regressions that control for intermediate outcomes. As Robert notes, we discuss these issues in chapters 9 and 10. But Frakt’s example is a good one.
2 0.14077395 1113 andrew gelman stats-2012-01-11-Toshiro Kageyama on professionalism
Introduction: Following up on our discussion of professionalism (in which Jonathan Chait argued that “the definition of a professional career track” requires pay differentials and the chance to get fired, and I argued the opposite, that a lot of people go into professional careers specifically because of the job security), Austin Frakt pointed me to this description of professionalism from Go master Toshiro Kageyama. This in turn reminds me of a remark of Bill James when he explained lack of surprise that clutch hitting does not show up in the data. He wrote that the underlying idea of clutch hitting is that a player will play particuarly well in an important situation where the game or the season is on the line. But, James pointed out, these guys are pros, and the true sign of a professional is that he can always stay concentrated. This argument applies particuarly for hitting, maybe less so for pitching, where a pitcher can’t necessarily throw his hardest for 100 pitches in a game.
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Introduction: Robert Bell writes: I [Bell] just finished “The Emperor of All Maladies” from 2010. One of the sections is captioned with the “In God we trust, all others bring data” quote which is supposedly from Deming. If you haven’t read it, I think you might like it even though the topic is pretty morbid (i.e. cancer). I was rereading chapter 7 in Bayesian Data Analysis. While the principles there would allow me to “roll my own”, I was wondering whether or not there is now a comprehensive “cookbook” out there that gathers together such analyses for a variety of experimental designs – sort of a Bayesian equivalent to Montgomery’s “Design and Analysis of Experiments”. My reply: I don’t know, but now I’m thinking we should put some of this in our forthcoming book, Statistics for Psychology Research. In the meantime, if anyone has any suggestions, feel free to post them in comments.
4 0.091734365 2363 andrew gelman stats-2014-06-07-“Does researching casual marijuana use cause brain abnormalities?”
Introduction: David Austin points me to a wonderfully-titled post by Lior Pachter criticizing a recent paper on the purported effects of cannabis use. Not the paper criticized here . Someone should send this all to David Brooks. I’ve heard he’s interested in the latest scientific findings, and I know he’s interested in marijuana.
5 0.087655984 970 andrew gelman stats-2011-10-24-Bell Labs
Introduction: Sining Chen told me they’re hiring in the statistics group at Bell Labs . I’ll do my bit for economic stimulus by announcing this job (see below). I love Bell Labs. I worked there for three summers, in a physics lab in 1985-86 under the supervision of Loren Pfeiffer, and by myself in the statistics group in 1990. I learned a lot working for Loren. He was a really smart and driven guy. His lab was a small set of rooms—in Bell Labs, everything’s in a small room, as they value the positive externality of close physical proximity of different labs, which you get by making each lab compact—and it was Loren, his assistant (a guy named Ken West who kept everything running in the lab), and three summer students: me, Gowton Achaibar, and a girl whose name I’ve forgotten. Gowtan and I had a lot of fun chatting in the lab. One day I made a silly comment about Gowton’s accent—he was from Guyana and pronounced “three” as “tree”—and then I apologized and said: Hey, here I am making fun o
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same-blog 1 0.93400329 871 andrew gelman stats-2011-08-26-Be careful what you control for . . . you just might get it!
Introduction: Robert Bell points me to this blog by Austin Frakt explaining problems in interpreting regressions that control for intermediate outcomes. As Robert notes, we discuss these issues in chapters 9 and 10. But Frakt’s example is a good one.
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Introduction: Apparently some of our new blog entries are appearing as old entries on the RSS feed, meaning that those of you who read the blog using RSS may be missing a lot of good stuff. We’re working on this. But, in the meantime, I recommend you click on the blog itself to see what’s been posted in the last few weeks. Enjoy.
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Introduction: Joseph Wilson points me to this blog on applied probability modeling. He sent me the link a couple months ago. If he’s still adding new entries, then his blog is probably already longer-lasting than most!
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Introduction: I’m not sure how the New York Times defines a blog versus an article, so perhaps this post should be called “Bayes in the blogs.” Whatever. A recent NY Times article/blog post discusses a classic Bayes’ Theorem application — probability that the patient has cancer, given a “positive” mammogram — and purports to give a solution that is easy for students to understand because it doesn’t require Bayes’ Theorem, which is of course complicated and confusing. You can see my comment (#17) here.
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Introduction: Good to have gotten that one out of the way already. (Actually, I wrote it a few months ago. This post is itself in the monthlong+ queue.) I don’t know how easy it is to search this blog by date to find the Fools posts from previous years.
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Introduction: Robert Bell points me to this blog by Austin Frakt explaining problems in interpreting regressions that control for intermediate outcomes. As Robert notes, we discuss these issues in chapters 9 and 10. But Frakt’s example is a good one.
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Introduction: . . . sounded exactly like a David Mamet character. I mean, exactly. Or like Eric Bogosian doing a David Mamet character. I only wish I had a good ear for dialogue and could get it down for you. OK, we don’t use the word fuck on this blog but I could substitute something like f*** and you’d get the point. He was on his cell phone and seemed to be talking with his wife or girlfriend, explaining why they should get back together. It was a bit of a cross between Alec Baldwin and Jack Lemmon.
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Introduction: This (by Frans Hofmeester) is excellent. What really makes it work, I think, is that it goes slowly enough. 2 minutes and 45 seconds is enough time for me, as a viewer, to feel like I’m living through each stage of development. If the video were sped up to go from 0 to 12 in only 30 seconds, that would be cool in its own way but would give up the sense of local stability that is characteristic of development.
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Introduction: Last year my commenters and I discussed Ed Glaeser’s claim that the way to create a great city is to “create a great university and wait 200 years.” I passed this on to urbanist Richard Florida and received the following response: This is a tough one with lots of causality issues. Generally speaking universities make places stronger. But this is mainly the case for smaller, college towws. Boulder, Ann Arbor and so on, which also have very high human capital levels and high levels of creative, knowledge and professional workers. For big cities the issue is mixed. Take Pittsburgh with CMU and Pitt or Baltimore with Hopkins, or St Louis. The list goes on and on. Kevin Stolarick and I framed this very crudely as a transmitter reciever issue. The university in a city like this can generate a lot of signal, in terms of innovation or even human capital and the city may not receive it or push it away. A long ago paper by Mike Fogarty showed how innovations in Pittsburgh and Cl
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Introduction: Wayne Folta writes, “In light of your recent blog post on women in higher education, here’s one I just read about on a techie website regarding elementary education”: Why do girls get better grades in elementary school than boys—even when they perform worse on standardized tests? New research . . . suggests that it’s because of their classroom behavior, which may lead teachers to assign girls higher grades than their male counterparts. . . . The study, co-authored by [Christopher] Cornwell and David Mustard at UGA and Jessica Van Parys at Columbia, analyzed data on more than 5,800 students from kindergarten through fifth grade. It examined students’ performance on standardized tests in three categories—reading, math and science-linking test scores to teachers’ assessments of their students’ progress, both academically and more broadly. The data show, for the first time, that gender disparities in teacher grades start early and uniformly favor girls. In every subject area, bo
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