andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1851 knowledge-graph by maker-knowledge-mining
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Introduction: Tom Salvesen asks, is this the worst info-graphic of the year? I say, no. Nobody really cares about these numbers. It’s an amusing feature. The alternative would not be a better display of these data, the alternative would be some photo or cartoon. They’re just having fun. I wouldn’t give it any design awards but it’s fine, it is what it is.
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Introduction: Tom Salvesen asks, is this the worst info-graphic of the year? I say, no. Nobody really cares about these numbers. It’s an amusing feature. The alternative would not be a better display of these data, the alternative would be some photo or cartoon. They’re just having fun. I wouldn’t give it any design awards but it’s fine, it is what it is.
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Introduction: Amusing authorship analysis.
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Introduction: In case you just can’t get enough, check out this amusing interview. The interview is from the year 2000 (I think) but it reads like it could’ve been done yesterday.
Introduction: Hey, here’s a book I’m not planning to read any time soon! As Bill James wrote, the alternative to good statistics is not “no statistics,” it’s bad statistics. (I wouldn’t have bothered to bring this one up, but I noticed it on one of our sister blogs.)
5 0.12775761 2251 andrew gelman stats-2014-03-17-In the best alternative histories, the real world is what’s ultimately real
Introduction: This amusing-yet-so-true video directed by Eléonore Pourriat shows a sex-role-reversed world where women are in charge and men don’t get taken seriously. It’s convincing and affecting, but the twist that interests me comes at the end, when the real world returns. It’s really creepy. And this in turn reminds me of something we discussed here several years ago, the idea that alternative histories are made particularly compelling when they are grounded in the fact that the alternate world is not the real world. Pourriat’s video would have been excellent even without its final scene, but that scene drives the point home in a way that I don’t think would’ve been possible had the video stayed entirely within its artificial world. The point here is that the real world is indeed what is real. This alternative sex-role-reversed world is not actually possible, and what makes it interesting to think about is the contrast to what really is. If you set up an alternative history but you do
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Introduction: Tom Salvesen asks, is this the worst info-graphic of the year? I say, no. Nobody really cares about these numbers. It’s an amusing feature. The alternative would not be a better display of these data, the alternative would be some photo or cartoon. They’re just having fun. I wouldn’t give it any design awards but it’s fine, it is what it is.
2 0.69591534 1536 andrew gelman stats-2012-10-16-Using economics to reduce bike theft
Introduction: Rohin Dhar writes : While bike theft is an epidemic in major US cities, most people seem resigned that it’s just a fact of life. . . . at Priceonomics, we thought we’d take a crack at trying to reduce bike theft. Could we use software to help people fight back against bike thieves? Professional bike thieves exist because they can make a profit. Luckily, this author went to business school and remembers exactly one equation from the experience: Profit = Revenue – Cost From a criminal’s perspective, the “Cost” of bike theft is about zero. The odds of getting caught are negligible and the penalty is about zero as well. Most commentators suggest that in order to prevent bike theft, the government should increase the penalties to make it a less attractive crime. As we stated earlier, we somehow doubt government intervention is going to happen any time soon. We decided to focus on the revenue half of the equation. Could we make it harder for bike thieves to turn their contraband
3 0.67423671 1127 andrew gelman stats-2012-01-18-The Fixie Bike Index
Introduction: Where are the fixed-gear bike riders? Rohin Dhar explains : At Priceonomics, in order to build our bicycle price guide, we measure what kind of used bikes people are trying to sell and the quantity sold in any city. By mining our database of 1.3 million bicycle listings, we can tell what are the largest markets for used bicycles, how the prices vary by region, and where people who prize fixed gear bikes live. Fixies (fixed gear bikes) are considered to be a strong indicator of hipsterness. For those unfamiliar, a fixed gear bike requires riding in a single gear and the only way to stop the bike is to pedal backwards to help skid the bike to a halt. You can’t “coast” on a fixie; when you are biking downhill, your pedals will keep moving so you better keep pedaling too. Because of the minimalism of this fixed gear system, the bikes tend to be aesthetically pleasing but somewhat challenging to ride. . . . In short, fixed gear bikes = hipsters, and New York boroug
4 0.65442979 1154 andrew gelman stats-2012-02-04-“Turn a Boring Bar Graph into a 3D Masterpiece”
Introduction: Jimmy sends in this . Steps include “Make whimsical sparkles by drawing an ellipse using the Ellipse Tool,” “Rotate the sparkles . . . Give some sparkles less Opacity by using the Transparency Palette,” and “Add a haze around each sparkle by drawing a white ellipse using the Ellipse Tool.” The punchline: Now, the next time you need to include a boring graph in one of your designs you’ll be able to add some extra emphasis and get people to really pay attention to those numbers! P.S. to all the commenters: Yeah, yeah, do your contrarian best and tell me why chartjunk is actually a good thing, how I’m just a snob, etc etc.
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Introduction: We had a recent discussion about statistics packages where people talked about the structure and capabilities of different computer languages. One thing I wanted to add to this discussion is some sociology. To me, a statistics package is not just its code, it’s also its community, it’s what people do with it. R, for example, is nothing special for graphics (again, I think in retrospect my graphs would be better if I’d been making them in Fortran all these years); what makes R graphics work so well is that there’s a clear path from the numbers to the graphs, there’s a tradition in R of postprocessing. In comparison, consider Sas. I’ve never directly used Sas but whenever I’ve seen it used, whether by people working for me or with me or just people down the hall who left Sas output sitting in the printer, in all these cases there’s no postprocessing. It doesn’t look interactive at all. The user runs some procedure and then there are pages and pages and pages of output. The po
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Introduction: Tom Salvesen asks, is this the worst info-graphic of the year? I say, no. Nobody really cares about these numbers. It’s an amusing feature. The alternative would not be a better display of these data, the alternative would be some photo or cartoon. They’re just having fun. I wouldn’t give it any design awards but it’s fine, it is what it is.
2 0.88187921 1476 andrew gelman stats-2012-08-30-Stan is fast
Introduction: 10,000 iterations for 4 chains on the (precompiled) efficiently-parameterized 8-schools model: > date () [1] "Thu Aug 30 22:12:53 2012" > fit3 <- stan (fit=fit2, data = schools_dat, iter = 1e4, n_chains = 4) SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1). Iteration: 10000 / 10000 [100%] (Sampling) SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2). Iteration: 10000 / 10000 [100%] (Sampling) SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3). Iteration: 10000 / 10000 [100%] (Sampling) SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4). Iteration: 10000 / 10000 [100%] (Sampling) > date () [1] "Thu Aug 30 22:12:55 2012" > print (fit3) Inference for Stan model: anon_model. 4 chains: each with iter=10000; warmup=5000; thin=1; 10000 iterations saved. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat mu 8.0 0.1 5.1 -2.0 4.7 8.0 11.3 18.4 4032 1 tau 6.7 0.1 5.6 0.3 2.5 5.4 9.3 21.2 2958 1 eta[1] 0.4 0.0 0.9 -1.5 -0
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Introduction: OK, fine . Maybe they could work Stan on to the show next? I thought I could retire once I’d successfully inserted the phrase “multilevel regression and poststratification” into the NYT, but now I want more more more. Maybe a cage match between Stan and Mister P on the Itchy and Scratchy show?
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Introduction: Philip Cohen asks , “Why are mothers becoming moms?” These aren’t just two words for the same thing: in political terms “mother” is merely descriptive while “mom” is more positive. Indeed, we speak of “mom and apple pie” as unquestionable American icons. Cohen points out that motherhood is sometimes but not always respected in political discourse: On the one hand, both President Obama and pundit Hilary Rosen have now called motherhood the world’s hardest job. And with the Romneys flopping onto the all-mothers-work bandwagon, it appears we’re reaching a rare rhetorical consensus. On the other hand, the majority in both major political parties agrees that poor single mothers and their children need one thing above all – a (real) job, one that provides the “dignity of an honest day’s work.” For welfare purposes, taking care of children is not only not the toughest job in the world, it is more akin to nothing at all. When Bill Clinton’s endorsed welfare-to-work he famously decla
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Introduction: Someone told me he ran into someone who said his goal was to be Tyler Cowen. OK, fine, it’s a worthy goal, but I don’t think it’s so easy .
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