andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-546 knowledge-graph by maker-knowledge-mining
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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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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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Introduction: I like what Antony Unwin has to say here (start on page 5).
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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: 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: Antony Unwin writes: I [Unwin] find it an interesting exercise for students to ask them to write headlines (and subheadlines) for graphics, both for ones they have drawn themselves and for published ones. The results are sometimes depressing, often thought-provoking and occasionally highly entertaining. This seems like a great idea, both for teaching students how to read a graph and also for teaching how to make a graph. I’ve long said that when making a graph (or, for that matter, a table), you want to think about what message the reader will get out of it. “Displaying a bunch of numbers” doesn’t cut it.
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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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Introduction: Infovis and Statistical Graphics: Different Goals, Different Looks (and here’s the article) Speaker: Andrew Gelman, Columbia University Date: Thursday, November 29 2012 Time: 4:00PM to 5:00PM Location: 32-D463 (Star Conference Room) Host: Polina Golland, CSAIL Contact: Polina Golland, 6172538005, polina@csail.mit.edu The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey’s Exploratory Data Analysis (1977) and Tufte’s books in the succeeding decades. But statistical graphics still occupies an awkward in-between position: Within statistics, exploratory and graphical methods represent a minor subfield and are not well-integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) is huge, but their purveyors and enthusiasts appear largely to be uninterested in statisti
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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: 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: 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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Introduction: In case you couldn’t come to our panel (with Kaiser Fung, Mark Hansen, Tahir Hemphill, Manuel Lima, and Jonathan Stray, and organized by Isabel Draves), here’s the video:
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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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