andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1011 knowledge-graph by maker-knowledge-mining
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Introduction: Julyan Arbel plots world record running times vs. distance (on the log-log scale): The line has a slope of 1.1. I think it would be clearer to plot speed vs. distance—then you’d get a slope of -0.1, and the numbers would be more directly interpretable. Indeed, this paper by Sandra Savaglio and Vincenzo Carbone (referred to in the comments on Julyan’s blog) plots speed vs. time. Graphing by speed gives more resolution: The upper-left graph in the grid corresponds to the human running records plotted by Arbel. It’s funny that Arbel sees only one line whereas Savaglio and Carbone see two—but if you remove the 100m record at one end and the 100km at the other end, you can see two lines in Arbel’s graph as well. The bottom two graphs show swimming records. Knut would probably have something to say about all this.
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4 Graphing by speed gives more resolution: The upper-left graph in the grid corresponds to the human running records plotted by Arbel. [sent-9, score-1.014]
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6 Knut would probably have something to say about all this. [sent-12, score-0.124]
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same-blog 1 1.0 1011 andrew gelman stats-2011-11-15-World record running times vs. distance
Introduction: Julyan Arbel plots world record running times vs. distance (on the log-log scale): The line has a slope of 1.1. I think it would be clearer to plot speed vs. distance—then you’d get a slope of -0.1, and the numbers would be more directly interpretable. Indeed, this paper by Sandra Savaglio and Vincenzo Carbone (referred to in the comments on Julyan’s blog) plots speed vs. time. Graphing by speed gives more resolution: The upper-left graph in the grid corresponds to the human running records plotted by Arbel. It’s funny that Arbel sees only one line whereas Savaglio and Carbone see two—but if you remove the 100m record at one end and the 100km at the other end, you can see two lines in Arbel’s graph as well. The bottom two graphs show swimming records. Knut would probably have something to say about all this.
2 0.14454603 1773 andrew gelman stats-2013-03-21-2.15
Introduction: Jake Hofman writes that he saw my recent newspaper article on running (“How fast do we slow down? . . . For each doubling of distance, the world record time is multiplied by about 2.15. . . . for sprints of 200 meters to 1,000 meters, a doubling of distance corresponds to an increase of a factor of 2.3 in world record running times; for longer distances from 1,000 meters to the marathon, a doubling of distance increases the time by a factor of 2.1. . . . similar patterns for men and women, and for swimming as well as running”) and writes: If you’re ever interested in getting or playing with Olympics data, I [Jake] wrote some code to scrape it all from sportsreference.com this past summer for a blog post . Enjoy!
3 0.13990307 1085 andrew gelman stats-2011-12-27-Laws as expressive
Introduction: June Carbone points out sometimes people want laws to express a sentiment. This isn’t just about Congress passing National Smoked Meats Week or San Francisco establishing itself as a nuclear-free zone, it also includes things such as laws against gay marriage, where, as Carbone writes, “we ‘care too much,’ when in fact we can do so little.” I don’t have anything to add here, and I expect many of you are familiar with this idea, but it’s new to me. I’d always been puzzled by people who want to use the law to express a sentiment, but perhaps it makes sense to be open-minded and to consider this as one of the purposes of the legislative process.
4 0.13281499 252 andrew gelman stats-2010-09-02-R needs a good function to make line plots
Introduction: More and more I’m thinking that line plots are great. More specifically, two-way grids of line plots on common scales, with one, two, or three lines per plot (enough to show comparisons but not so many that you can’t tell the lines apart). Also dot plots, of the sort that have been masterfully used by Lax and Phillips to show comparisons and trends in support for gay rights. There’s a big step missing, though, and that is to be able to make these graphs as a default. We have to figure out the right way to structure the data so these graphs come naturally. Then when it’s all working, we can talk the Excel people into implementing our ideas. I’m not asking to be paid here; all our ideas are in the public domain and I’m happy for Microsoft or Google or whoever to copy us. P.S. Drew Conway writes: This could be accomplished with ggplot2 using various combinations of the grammar. If I am understanding what you mean by line plots, here are some examples with code . In fact,
5 0.10061189 61 andrew gelman stats-2010-05-31-A data visualization manifesto
Introduction: Details matter (at least, they do for me), but we don’t yet have a systematic way of going back and forth between the structure of a graph, its details, and the underlying questions that motivate our visualizations. (Cleveland, Wilkinson, and others have written a bit on how to formalize these connections, and I’ve thought about it too, but we have a ways to go.) I was thinking about this difficulty after reading an article on graphics by some computer scientists that was well-written but to me lacked a feeling for the linkages between substantive/statistical goals and graphical details. I have problems with these issues too, and my point here is not to criticize but to move the discussion forward. When thinking about visualization, how important are the details? Aleks pointed me to this article by Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky, “A Tour through the Visualization Zoo: A survey of powerful visualization techniques, from the obvious to the obscure.” Th
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Introduction: Julyan Arbel plots world record running times vs. distance (on the log-log scale): The line has a slope of 1.1. I think it would be clearer to plot speed vs. distance—then you’d get a slope of -0.1, and the numbers would be more directly interpretable. Indeed, this paper by Sandra Savaglio and Vincenzo Carbone (referred to in the comments on Julyan’s blog) plots speed vs. time. Graphing by speed gives more resolution: The upper-left graph in the grid corresponds to the human running records plotted by Arbel. It’s funny that Arbel sees only one line whereas Savaglio and Carbone see two—but if you remove the 100m record at one end and the 100km at the other end, you can see two lines in Arbel’s graph as well. The bottom two graphs show swimming records. Knut would probably have something to say about all this.
2 0.86909401 1439 andrew gelman stats-2012-08-01-A book with a bunch of simple graphs
Introduction: Howard Friedman sent me a new book, The Measure of a Nation, subtitled How to Regain America’s Competitive Edge and Boost Our Global Standing. Without commenting on the substance of Friedman’s recommendations, I’d like to endorse his strategy of presentation, which is to display graph after graph after graph showing the same message over and over again, which is that the U.S. is outperformed by various other countries (mostly in Europe) on a variety of measures. These aren’t graphs I would ever make—they are scatterplots in which the x-axis conveys no information. But they have the advantage of repetition: once you figure out how to read one of the graphs, you can read the others easily. Here’s an example which I found from a quick Google: I can’t actually figure out what is happening on the x-axis, nor do I understand the “star, middle child, dog” thing. But I like the use of graphics. Lots more fun than bullet points. Seriously. P.S. Just to be clear: I am not trying
3 0.85631084 672 andrew gelman stats-2011-04-20-The R code for those time-use graphs
Introduction: By popular demand, here’s my R script for the time-use graphs : # The data a1 <- c(4.2,3.2,11.1,1.3,2.2,2.0) a2 <- c(3.9,3.2,10.0,0.8,3.1,3.1) a3 <- c(6.3,2.5,9.8,0.9,2.2,2.4) a4 <- c(4.4,3.1,9.8,0.8,3.3,2.7) a5 <- c(4.8,3.0,9.9,0.7,3.3,2.4) a6 <- c(4.0,3.4,10.5,0.7,3.3,2.1) a <- rbind(a1,a2,a3,a4,a5,a6) avg <- colMeans (a) avg.array <- t (array (avg, rev(dim(a)))) diff <- a - avg.array country.name <- c("France", "Germany", "Japan", "Britain", "USA", "Turkey") # The line plots par (mfrow=c(2,3), mar=c(4,4,2,.5), mgp=c(2,.7,0), tck=-.02, oma=c(3,0,4,0), bg="gray96", fg="gray30") for (i in 1:6){ plot (c(1,6), c(-1,1.7), xlab="", ylab="", xaxt="n", yaxt="n", bty="l", type="n") lines (1:6, diff[i,], col="blue") points (1:6, diff[i,], pch=19, col="black") if (i>3){ axis (1, c(1,3,5), c ("Work,\nstudy", "Eat,\nsleep", "Leisure"), mgp=c(2,1.5,0), tck=0, cex.axis=1.2) axis (1, c(2,4,6), c ("Unpaid\nwork", "Personal\nCare", "Other"), mgp=c(2,1.5,0),
4 0.84982526 671 andrew gelman stats-2011-04-20-One more time-use graph
Introduction: Evan Hensleigh sens me this redesign of the cross-national time use graph : Here was my version: And here was the original: Compared to my graph, Evan’s has better fonts, and that’s important–good fonts can make a display look professional. But I’m not sure about his other innovations. To me, the different colors for the different time-use categories are more of a distraction than a visual aid, and I also don’t like how he made the bars fatter. As I noted in my earlier entry, to me this draws unwanted attention to the negative space between the bars. His country labels are slightly misaligned (particularly Japan and USA), and I really don’t like his horizontal axis at all! He removed the units of hours and put + and – on the edges so that the axes run into each other. What was the point of that? It’s bad news. Also I don’t see any advantage at all to the prehensile tick marks. On the other hand, if Evgn and I were working together on such a graph, we w
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Introduction: I continue to be the go-to guy for bad graphs. Today (i.e., 22 Feb), I received an email from Gary Rosin: I [Rosin] thought you might be interested in this graph showing the decline in median prices of homes since 1997. It exaggerates the proportions by using $150,000 as the floor, rather than zero. Indeed. Here’s the graph: A line plot, rather than a bar plot, would be appropriate here. Also, it’s weird that the headline says “10 years” but the graph has only 6 years. Why not give some perspective and show, say, 30 years?
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Introduction: Julyan Arbel plots world record running times vs. distance (on the log-log scale): The line has a slope of 1.1. I think it would be clearer to plot speed vs. distance—then you’d get a slope of -0.1, and the numbers would be more directly interpretable. Indeed, this paper by Sandra Savaglio and Vincenzo Carbone (referred to in the comments on Julyan’s blog) plots speed vs. time. Graphing by speed gives more resolution: The upper-left graph in the grid corresponds to the human running records plotted by Arbel. It’s funny that Arbel sees only one line whereas Savaglio and Carbone see two—but if you remove the 100m record at one end and the 100km at the other end, you can see two lines in Arbel’s graph as well. The bottom two graphs show swimming records. Knut would probably have something to say about all this.
2 0.75629681 1929 andrew gelman stats-2013-07-07-Stereotype threat!
Introduction: Colleen Ganley, Leigh Mingle, Allison Ryan, Katherine Ryan, Marian Vasilyeva, and Michelle Perry write : Stereotype threat has been proposed as 1 potential explanation for the gender difference in standardized mathematics test performance among high-performing students. At present, it is not entirely clear how susceptibility to stereotype threat develops, as empirical evidence for stereotype threat effects across the school years is inconsistent. In a series of 3 studies, with a total sample of 931 students, we investigated stereotype threat effects during childhood and adolescence. Three activation methods were used, ranging from implicit to explicit. Across studies, we found no evidence that the mathematics performance of school-age girls was impacted by stereotype threat. In 2 of the studies, there were gender differences on the mathematics assessment regardless of whether stereotype threat was activated. Potential reasons for these findings are discussed, including the possibil
3 0.75017023 1045 andrew gelman stats-2011-12-07-Martyn Plummer’s Secret JAGS Blog
Introduction: Martyn Plummer , the creator of the open-source, C++, graphical-model compiler JAGS (aka “Just Another Gibbs Sampler”), runs a forum on the JAGS site that has a very similar feel to the mail-bag posts on this blog. Martyn answers general statistical computing questions (e.g., why slice sampling rather than Metropolis-Hastings?) and general modeling (e.g., why won’t my model converge with this prior?). Here’s the link to the top-level JAGS site, and to the forum: JAGS Forum JAGS Home Page The forum’s pretty active, with the stats page showing hundreds of views per day and very regular posts and answers. Martyn’s last post was today. Martyn also has a blog devoted to JAGS and other stats news: JAGS News Blog
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