andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-225 knowledge-graph by maker-knowledge-mining
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Introduction: I like what Antony Unwin has to say here (start on page 5).
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same-blog 1 1.0 225 andrew gelman stats-2010-08-23-Getting into hot water over hot graphics
Introduction: I like what Antony Unwin has to say here (start on page 5).
2 0.3848519 583 andrew gelman stats-2011-02-21-An interesting assignment for statistical graphics
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.
3 0.29307836 546 andrew gelman stats-2011-01-31-Infovis vs. statistical graphics: My talk tomorrow (Tues) 1pm at Columbia
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.
4 0.23392257 816 andrew gelman stats-2011-07-22-“Information visualization” vs. “Statistical graphics”
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
5 0.21334928 1246 andrew gelman stats-2012-04-04-Data visualization panel at the New York Public Library this evening!
Introduction: I’ll be participating in a panel (along with Kaiser Fung, Mark Hansen, Tahir Hemphill, and Manuel Lima), “What Makes Good Data Visualization?”, at the 42nd St. library this evening. The event is organized by Isabel Walcott Draves and is part of the Leaders in Software and Art series. This article with Antony Unwin should be relevant (although I won’t be “presenting”; I’ll be part of a panel and we’ll be having a wide-ranging conversation).
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same-blog 1 0.92743236 225 andrew gelman stats-2010-08-23-Getting into hot water over hot graphics
Introduction: I like what Antony Unwin has to say here (start on page 5).
2 0.63749301 546 andrew gelman stats-2011-01-31-Infovis vs. statistical graphics: My talk tomorrow (Tues) 1pm at Columbia
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.
3 0.59622622 1584 andrew gelman stats-2012-11-19-Tradeoffs in information graphics
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
4 0.56910789 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.
5 0.55793232 1275 andrew gelman stats-2012-04-22-Please stop me before I barf again
Introduction: Pointing to some horrible graphs, Kaiser writes, “The Earth Institute needs a graphics adviser.” I agree. The graphs are corporate standard, neither pretty or innovative enough to qualify as infographics, not informational enough to be good statistical data displays. Some examples include the above exploding pie chart, which, as Kaiser notes, is not merely ugly and ridiculously difficult to read (given that it is conveying only nine data points) but also invites suspicion of its numbers, and pages and pages of graphs that could be better compressed into a compact displays (see pages 25-65 of the report). Yes, this is all better than tables of numbers, but I don’t see that much thought went into displaying patterns of information or telling a story. It’s more graph-as-data-dump. To be fair, the report does have some a clean scatterplot (on page 65). But, overall, the graphs are not well-integrated with the messages in the text. I feel a little bit bad about this, beca
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1 0.8849386 720 andrew gelman stats-2011-05-20-Baby name wizards
Introduction: The other day I noticed a car with the improbable name of Nissan Rogue, from Darien, Connecticut (at least that’s what the license plate frame said). And, after all, what could be more “rogue”-like than a suburban SUV? I can’t blame the driver of the car for this one; I’m just amused that the marketers and Nissan thought this was an appropriate name for the car.
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Introduction: I like what Antony Unwin has to say here (start on page 5).
Introduction: John Kastellec, Jeff Lax, and Justin Phillips write : Do senators respond to the preferences of their states’ median voters or only to the preferences of their co-partisans? We [Kastellec et al.] study responsiveness using roll call votes on ten recent Supreme Court nominations. We develop a method for estimating state-level public opinion broken down by partisanship. We find that senators respond more powerfully to their partisan base when casting such roll call votes. Indeed, when their state median voter and party median voter disagree, senators strongly favor the latter. [emphasis added] This has significant implications for the study of legislative responsiveness, the role of public opinion in shaping the personnel of the nations highest court, and the degree to which we should expect the Supreme Court to be counter-majoritarian. Our method can be applied elsewhere to estimate opinion by state and partisan group, or by many other typologies, so as to study other important qu
4 0.73792708 127 andrew gelman stats-2010-07-04-Inequality and health
Introduction: Several people asked me for my thoughts on Richard Wilkinson and Kate Pickett’s book, “The Spirit Level: Why Greater Equality Makes Societies Stronger.” I’ve outsourced my thinking on the topic to Lane Kenworthy .
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Introduction: Last week, as I walked into Andrew’s office for a meeting, he was formulating some misgivings about applying an ideal-point model to budgetary bills in the U.S. Senate. Andrew didn’t like that the model of a senator’s position was an indifference point rather than at their optimal point, and that the effect of moving away from a position was automatically modeled as increasing in one direction and decreasing in the other. Executive Summary The monotonicity of inverse logit entails that the expected vote for a bill among any fixed collection of senators’ ideal points is monotonically increasing (or decreasing) with the bill’s position, with direction determined by the outcome coding. The Ideal-Point Model The ideal-point model’s easy to write down, but hard to reason about because of all the polarity shifting going on. To recapitulate from Gelman and Hill’s Regression book (p. 317), using the U.S. Senate instead of the Supreme Court, and ignoring the dis
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