andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-914 knowledge-graph by maker-knowledge-mining
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Introduction: “Most Popular Infographics you can find around the web” by designer and illustrator Alberto Antoniazzi.
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Introduction: “Most Popular Infographics you can find around the web” by designer and illustrator Alberto Antoniazzi.
2 0.11797663 599 andrew gelman stats-2011-03-03-Two interesting posts elsewhere on graphics
Introduction: Have data graphics progressed in the last century? The first addresses familiar subjects to readers of the blog, with some nice examples of where infographics emphasize the obvious, or increase the probability of an incorrect insight. Your Help Needed: the Effect of Aesthetics on Visualization I borrow the term ‘insight’ from the second link, a study by a group of design & software researchers based around a single interactive graphic. This is similar in spirit to Unwin’s ‘caption this graphic’ assignment.
3 0.10098506 1700 andrew gelman stats-2013-01-31-Snotty reviewers
Introduction: I had a submission a couple years ago that was rejected by a journal. One of the reviewers began with the following snotty aside: In this manuscript Gelman and Shalizi (there’s no anonymity here; this thing has been floating around the web for some time) . . . Actually, we posted it on the same day we submitted it to the journal. But double-blindness allowed the reviewer to act as if we had done something wrong! And, even if it had been “floating around the web for some time,” that’s not necessarily a bad thing. Perhaps it just meant that the article had previously been rejected by a bad-attitude reviewer!
Introduction: Ben Hyde sends along this appealing image by Michael Paukner, which represents a nearly perfect distillation of “infographics”: Here are some of the comments on the linked page: Rather than redrawing the picture to make the lines more clear, I’d say: leave the graphic as is, and have a link to a set of statistical graphs that show where the different sorts of old trees are and what they look like. Let’s value the above image for its clean look and its clever Christmas-tree design, and once we have it, take advantage of viewers’ interest in the topic to show them more. P.S. See my comment below which I think further illuminates the appeal of this particular tree.
Introduction: Our discussion on data visualization continues. One one side are three statisticians–Antony Unwin, Kaiser Fung, and myself. We have been writing about the different goals served by information visualization and statistical graphics. On the other side are graphics experts (sorry for the imprecision, I don’t know exactly what these people do in their day jobs or how they are trained, and I don’t want to mislabel them) such as Robert Kosara and Jen Lowe , who seem a bit annoyed at how my colleagues and myself seem to follow the Tufte strategy of criticizing what we don’t understand. And on the third side are many (most?) academic statisticians, econometricians, etc., who don’t understand or respect graphs and seem to think of visualization as a toy that is unrelated to serious science or statistics. I’m not so interested in the third group right now–I tried to communicate with them in my big articles from 2003 and 2004 )–but I am concerned that our dialogue with the graphic
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Introduction: “Most Popular Infographics you can find around the web” by designer and illustrator Alberto Antoniazzi.
Introduction: Our discussion on data visualization continues. One one side are three statisticians–Antony Unwin, Kaiser Fung, and myself. We have been writing about the different goals served by information visualization and statistical graphics. On the other side are graphics experts (sorry for the imprecision, I don’t know exactly what these people do in their day jobs or how they are trained, and I don’t want to mislabel them) such as Robert Kosara and Jen Lowe , who seem a bit annoyed at how my colleagues and myself seem to follow the Tufte strategy of criticizing what we don’t understand. And on the third side are many (most?) academic statisticians, econometricians, etc., who don’t understand or respect graphs and seem to think of visualization as a toy that is unrelated to serious science or statistics. I’m not so interested in the third group right now–I tried to communicate with them in my big articles from 2003 and 2004 )–but I am concerned that our dialogue with the graphic
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Introduction: Here . Indeed, I’d much rather be a legend than a myth. I just want to clarify one thing. Walter Hickey writes: [Antony Unwin and Andrew Gelman] collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics. The takeaway is that while there have been great leaps in visualization technology, some of the visualizations that have garnered the highest praises have actually been lacking in a number of key areas. Specifically, the pair does a takedown of the top visualizations of 2008 as decided by the popular statistics blog Flowing Data. This is a fair summary, but I want to emphasize that, although our dislike of some award-winning visualizations is central to our argument, it is only the first part of our story. As Antony and I worked more on our paper, and especially after seeing the discussions by Robert Kosara, Stephen Few, Hadley Wickham, and Paul Murrell (all to appear in Journal of Computati
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Introduction: The visual display of quantitative information (to use Edward Tufte’s wonderful term) is a diverse field or set of fields, and its practitioners have different goals. The goals of software designers, applied statisticians, biologists, graphic designers, and journalists (to list just a few of the important creators of data graphics) often overlap—but not completely. One of our aims in writing our article [on Infovis and Statistical Graphics] was to emphasize the diversity of graphical goals, as it seems to us that even experts tend to consider one aspect of a graph and not others. Our main practical suggestion was that, in the internet age, we should not have to choose between attractive graphs and informational graphs: it should be possible to display both, via interactive displays. But to follow this suggestion, one must first accept that not every beautiful graph is informative, and not every informative graph is beautiful. . . . Yes, it can sometimes be possible for a graph to
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Introduction: By now you all must be tired of my one-sided presentations of the differences between infovis and statgraphics (for example, this article with Antony Unwin). Today is something different. Courtesy of Martin Theus, editor of the Statistical Computing and Graphics Newsletter, we have two short articles offering competing perspectives: Robert Kosara writes from an Infovis view: Information visualization is a field that has had trouble defining its boundaries, and that consequently is often misunderstood. It doesn’t help that InfoVis, as it is also known, produces pretty pictures that people like to look at and link to or send around. But InfoVis is more than pretty pictures, and it is more than statistical graphics. The key to understanding InfoVis is to ignore the images for a moment and focus on the part that is often lost: interaction. When we use visualization tools, we don’t just create one image or one kind of visualization. In fact, most people would argue that there is
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Introduction: Catherine Bueker writes: I [Bueker] am analyzing the effect of various contextual factors on the voter turnout of naturalized Latino citizens. I have included the natural log of the number of Spanish Language ads run in each state during the election cycle to predict voter turnout. I now want to calculate the predicted probabilities of turnout for those in states with 0 ads, 500 ads, 1000 ads, etc. The problem is that I do not know how to handle the beta coefficient of the LN(Spanish language ads). Is there someway to “unlog” the coefficient? My reply: Calculate these probabilities for specific values of predictors, then graph the predictions of interest. Also, you can average over the other inputs in your model to get summaries. See this article with Pardoe for further discussion.
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Introduction: Pawel Sobkowicz writes: How many zombies do you know?’ Using indirect survey methods to measure alien attacks and outbreaks of the undead, Arxiv preprint arXiv:1003.6087, 2010 I hope you would find interesting the following paper, recently posted on arXiv: Aliens on Earth. Are reports of close encounters correct?, arXiv:1203.6805 This is soooooo much better than getting links to bad graphs or to papers on sex ratios!
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Introduction: Infovis vs. statistical graphics . Tues 1 Feb 2011 1pm, Avery Hall room 114. It’s for the Lectures in Planning Series at the School of Architecture, Planning, and Preservation. Background on the talk (joint with Antony Unwin) is here . And here are more of my thoughts on statistical graphics.
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