andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-583 knowledge-graph by maker-knowledge-mining
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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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4 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. [sent-4, score-0.951]
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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: I like what Antony Unwin has to say here (start on page 5).
3 0.19727066 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.
Introduction: I continue to struggle to convey my thoughts on statistical graphics so I’ll try another approach, this time giving my own story. For newcomers to this discussion: the background is that Antony Unwin and I wrote an article on the different goals embodied in information visualization and statistical graphics, but I have difficulty communicating on this point with the infovis people. Maybe if I tell my own story, and then they tell their stories, this will point a way forward to a more constructive discussion. So here goes. I majored in physics in college and I worked in a couple of research labs during the summer. Physicists graph everything. I did most of my plotting on graph paper–this continued through my second year of grad school–and became expert at putting points at 1/5, 2/5, 3/5, and 4/5 between the x and y grid lines. In grad school in statistics, I continued my physics habits and graphed everything I could. I did notice, though, that the faculty and the other
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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: 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: Joshua Vogelstein points us to an article by Shaaron Ainsworth, Vaughan Prain, and Russell Tytler: Should science learners be challenged to draw more? Certainly making visualizations is integral to scientific thinking. Scientists do not use words only but rely on diagrams, graphs, videos, photographs, and other images to make discoveries, explain findings, and excite public interest. . . . However, in the science classroom, learners mainly focus on interpreting others’ visualizations; when drawing does occur, it is rare that learners are systematically encouraged to create their own visual forms to develop and show understanding (6). Drawing includes constructing a line graph from a table of values, sketching cells observed through a microscope, or inventing a way to show a scientific phenomenon (e.g., evaporation). Although interpretation of visualizations and other information is clearly critical to learning, becoming proficient in science also requires learners to develop many
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Introduction: Dean Eckles writes: Some of my coworkers at Facebook and I have worked with Udacity to create an online course on exploratory data analysis, including using data visualizations in R as part of EDA. The course has now launched at https://www.udacity.com/course/ud651 so anyone can take it for free. And Kaiser Fung has reviewed it . So definitely feel free to promote it! Criticism is also welcome (we are still fine-tuning things and adding more notes throughout). I wrote some more comments about the course here , including highlighting the interviews with my great coworkers. I didn’t have a chance to look at the course so instead I responded with some generic comments about eda and visualization (in no particular order): - Think of a graph as a comparison. All graphs are comparison (indeed, all statistical analyses are comparisons). If you already have the graph in mind, think of what comparisons it’s enabling. Or if you haven’t settled on the graph yet, think of what
Introduction: I continue to struggle to convey my thoughts on statistical graphics so I’ll try another approach, this time giving my own story. For newcomers to this discussion: the background is that Antony Unwin and I wrote an article on the different goals embodied in information visualization and statistical graphics, but I have difficulty communicating on this point with the infovis people. Maybe if I tell my own story, and then they tell their stories, this will point a way forward to a more constructive discussion. So here goes. I majored in physics in college and I worked in a couple of research labs during the summer. Physicists graph everything. I did most of my plotting on graph paper–this continued through my second year of grad school–and became expert at putting points at 1/5, 2/5, 3/5, and 4/5 between the x and y grid lines. In grad school in statistics, I continued my physics habits and graphed everything I could. I did notice, though, that the faculty and the other
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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: Every once in awhile I get a question that I can directly answer from my published research. When that happens it makes me so happy. Here’s an example. Patrick Lam wrote, Suppose one develops a Bayesian model to estimate a parameter theta. Now suppose one wants to evaluate the model via simulation by generating fake data where you know the value of theta and see how well you recover theta with your model, assuming that you use the posterior mean as the estimate. The traditional frequentist way of evaluating it might be to generate many datasets and see how well your estimator performs each time in terms of unbiasedness or mean squared error or something. But given that unbiasedness means nothing to a Bayesian and there is no repeated sampling interpretation in a Bayesian model, how would you suggest one would evaluate a Bayesian model? My reply: I actually have a paper on this ! It is by Cook, Gelman, and Rubin. The idea is to draw theta from the prior distribution.
Introduction: John Kastellec points me to this blog by Ezra Klein criticizing the following graph from a recent Republican Party report: Klein (following Alexander Hart ) slams the graph for not going all the way to zero on the y-axis, thus making the projected change seem bigger than it really is. I agree with Klein and Hart that, if you’re gonna do a bar chart, you want the bars to go down to 0. On the other hand, a projected change from 19% to 23% is actually pretty big, and I don’t see the point of using a graphical display that hides it. The solution: Ditch the bar graph entirely and replace it by a lineplot , in particular, a time series with year-by-year data. The time series would have several advantages: 1. Data are placed in context. You’d see every year, instead of discrete averages, and you’d get to see the changes in the context of year-to-year variation. 2. With the time series, you can use whatever y-axis works with the data. No need to go to zero. P.S. I l
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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: 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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