andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-830 knowledge-graph by maker-knowledge-mining
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Introduction: Political arguments and political representation often rely on statistics, whether it’s counting votes, measuring public opinion, or assessing the effects of policies. Sunday afternoon, Andrew Gelman discusses how models of political behavior can be improved by moving toward a more data-based perspective. Evidence-based medicine has the potential to transform health care, to focus resources on what works, and what works best for individual people. Monday morning, Chris Schmid discusses some of the statistical tools that can make this happen. Data visualization lives at the intersection of science, graphic design, and online journalism. Tuesday morning, Dianne Cook takes us from the fundamentals of information display to the future of dynamic graphics. Statistics is central to understanding gene mapping and gene expression. Wednesday morning, Cavan Reilly talks about the technical challenges involved in moving from data to genes to learning about genetic variation relating to
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2 Sunday afternoon, Andrew Gelman discusses how models of political behavior can be improved by moving toward a more data-based perspective. [sent-2, score-0.594]
3 Evidence-based medicine has the potential to transform health care, to focus resources on what works, and what works best for individual people. [sent-3, score-0.457]
4 Monday morning, Chris Schmid discusses some of the statistical tools that can make this happen. [sent-4, score-0.272]
5 Data visualization lives at the intersection of science, graphic design, and online journalism. [sent-5, score-0.431]
6 Tuesday morning, Dianne Cook takes us from the fundamentals of information display to the future of dynamic graphics. [sent-6, score-0.335]
7 Statistics is central to understanding gene mapping and gene expression. [sent-7, score-0.717]
8 Wednesday morning, Cavan Reilly talks about the technical challenges involved in moving from data to genes to learning about genetic variation relating to disease. [sent-8, score-0.881]
9 These sessions are open to all registered attendees, no special tickets required. [sent-9, score-0.403]
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Introduction: Political arguments and political representation often rely on statistics, whether it’s counting votes, measuring public opinion, or assessing the effects of policies. Sunday afternoon, Andrew Gelman discusses how models of political behavior can be improved by moving toward a more data-based perspective. Evidence-based medicine has the potential to transform health care, to focus resources on what works, and what works best for individual people. Monday morning, Chris Schmid discusses some of the statistical tools that can make this happen. Data visualization lives at the intersection of science, graphic design, and online journalism. Tuesday morning, Dianne Cook takes us from the fundamentals of information display to the future of dynamic graphics. Statistics is central to understanding gene mapping and gene expression. Wednesday morning, Cavan Reilly talks about the technical challenges involved in moving from data to genes to learning about genetic variation relating to
2 0.17306185 706 andrew gelman stats-2011-05-11-The happiness gene: My bottom line (for now)
Introduction: I had a couple of email exchanges with Jan-Emmanuel De Neve and James Fowler, two of the authors of the article on the gene that is associated with life satisfaction which we blogged the other day. (Bruno Frey, the third author of the article in question, is out of town according to his email.) Fowler also commented directly on the blog. I won’t go through all the details, but now I have a better sense of what’s going on. (Thanks, Jan and James!) Here’s my current understanding: 1. The original manuscript was divided into two parts: an article by De Neve alone published in the Journal of Human Genetics, and an article by De Neve, Fowler, Frey, and Nicholas Christakis submitted to Econometrica. The latter paper repeats the analysis from the Adolescent Health survey and also replicates with data from the Framingham heart study (hence Christakis’s involvement). The Framingham study measures a slightly different gene and uses a slightly life-satisfaction question com
3 0.098684609 702 andrew gelman stats-2011-05-09-“Discovered: the genetic secret of a happy life”
Introduction: I took the above headline from a news article in the (London) Independent by Jeremy Laurance reporting a study by Jan-Emmanuel De Neve, James Fowler, and Bruno Frey that reportedly just appeared in the Journal of Human Genetics. One of the pleasures of blogging is that I can go beyond the usual journalistic approaches to such a story: (a) puffing it, (b) debunking it, (c) reporting it completely flatly. Even convex combinations of (a), (b), (c) do not allow what I’d like to do, which is to explore the claims and follow wherever my exploration takes me. (And one of the pleasures of building my own audience is that I don’t need to endlessly explain background detail as was needed on a general-public site such as 538.) OK, back to the genetic secret of a happy life. Or, in the words the authors of the study, a gene that “explains less than one percent of the variation in life satisfaction.” “The genetic secret” or “less than one percent of the variation”? Perhaps the secre
4 0.092110194 855 andrew gelman stats-2011-08-16-Infovis and statgraphics update update
Introduction: To continue our discussion from last week , consider three positions regarding the display of information: (a) The traditional tabular approach. This is how most statisticians, econometricians, political scientists, sociologists, etc., seem to operate. They understand the appeal of a pretty graph, and they’re willing to plot some data as part of an exploratory data analysis, but they see their serious research as leading to numerical estimates, p-values, tables of numbers. These people might use a graph to illustrate their points but they don’t see them as necessary in their research. (b) Statistical graphics as performed by Howard Wainer, Bill Cleveland, Dianne Cook, etc. They–we–see graphics as central to the process of statistical modeling and data analysis and are interested in graphs (static and dynamic) that display every data point as transparently as possible. (c) Information visualization or infographics, as performed by graphics designers and statisticians who are
5 0.091959313 2157 andrew gelman stats-2014-01-02-2013
Introduction: There’s lots of overlap but I put each paper into only one category. Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later. So you can can think of this list as representing roughly two years’ work. Political science: [2014] The twentieth-century reversal: How did the Republican states switch to the Democrats and vice versa? {\em Statistics and Public Policy}. (Andrew Gelman) [2013] Hierarchical models for estimating state and demographic trends in U.S. death penalty public opinion. {\em Journal of the Royal Statistical Society A}. (Kenneth Shirley and Andrew Gelman) [2013] Deep interactions with MRP: Election turnout and voting patterns among small electoral subgroups. {\em American Journal of Political Science}. (Yair Ghitza and Andrew Gelman) [2013] Charles Murray’s {\em Coming Apart} and the measurement of social and political divisions. {\em Statistics, Politics and Policy}.
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Introduction: Political arguments and political representation often rely on statistics, whether it’s counting votes, measuring public opinion, or assessing the effects of policies. Sunday afternoon, Andrew Gelman discusses how models of political behavior can be improved by moving toward a more data-based perspective. Evidence-based medicine has the potential to transform health care, to focus resources on what works, and what works best for individual people. Monday morning, Chris Schmid discusses some of the statistical tools that can make this happen. Data visualization lives at the intersection of science, graphic design, and online journalism. Tuesday morning, Dianne Cook takes us from the fundamentals of information display to the future of dynamic graphics. Statistics is central to understanding gene mapping and gene expression. Wednesday morning, Cavan Reilly talks about the technical challenges involved in moving from data to genes to learning about genetic variation relating to
2 0.68433768 2221 andrew gelman stats-2014-02-23-Postdoc with Huffpost Pollster to do Bayesian poll tracking
Introduction: Mark Blumenthal writes: HuffPost Pollster has an immediate opening for a social and data scientist to join us full time, preferably in our Washington D.C. bureau, to work on development and improvement of our poll tracking models and political forecasts. You are someone who has: * A passion for electoral politics, * Advanced training in statistics and dynamic Bayesian data analysis, * A Ph.D. in statistics, political science, economics or the social sciences or comparable high level training or experience, * A desire to make a lasting contribution in the way the news media cover polls and elections. We are: * The award-winning website formerly known as Pollster.com , which joined the Huffington Post in 2010 and remains the internet’s premier source for uniquely interactive polling charts and electorate forecasts and a running daily commentary that explains, demystifies and critiques political polling. * Home to the open source Pollster API, which provides academic
3 0.67147088 1909 andrew gelman stats-2013-06-21-Job openings at conservative political analytics firm!
Introduction: After posting that announcement about Civis Analytics, I wrote, “If a reconstituted Romney Analytics team is hiring, let me know and I’ll post that ad too.” Adam Schaeffer obliged : Not sure about Romney’s team, but Evolving Strategies is looking for sharp folks who lean right: Evolving Strategies is a political communications research firm specializing in randomized controlled experiments in the “lab” and in the “field.” ES is bringing a scientific revolution to free-market/conservative politics. We are looking for people who are obsessive about getting things right and creative in their work. A ideal candidate will have a deep understanding of the academic literature in their field, highly developed skills, a commitment to academic rigor, but an intuitive understanding of practical political concerns and objectives as well. We’re looking for new talent to help with our fast-growing portfolio in these areas: High-level data processing, statistical analysis and modelin
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Introduction: Stephanie Evergreen writes: Media, web design, and marketing have all created an environment where stakeholders – clients, program participants, funders – all expect high quality graphics and reporting that effectively conveys the valuable insights from evaluation work. Some in statistics and mathematics have used data visualization strategies to support more useful reporting of complex ideas. Global growing interest in improving communications has begun to take root in the evaluation field as well. But as anyone who has sat through a day’s worth of a conference or had to endure a dissertation-worthy evaluation report knows, evaluators still have a long way to go. To support the development of researchers and evaluators, some members of the American Evaluation Association are proposing a new TIG (Topical Interest Group) on Data Visualization and Reporting. If you are a member of AEA (or want to be) and you are interested in joining this TIG, contact Stephanie Evergreen.
5 0.63903922 1594 andrew gelman stats-2012-11-28-My talk on statistical graphics at Mit this Thurs aft
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: Political arguments and political representation often rely on statistics, whether it’s counting votes, measuring public opinion, or assessing the effects of policies. Sunday afternoon, Andrew Gelman discusses how models of political behavior can be improved by moving toward a more data-based perspective. Evidence-based medicine has the potential to transform health care, to focus resources on what works, and what works best for individual people. Monday morning, Chris Schmid discusses some of the statistical tools that can make this happen. Data visualization lives at the intersection of science, graphic design, and online journalism. Tuesday morning, Dianne Cook takes us from the fundamentals of information display to the future of dynamic graphics. Statistics is central to understanding gene mapping and gene expression. Wednesday morning, Cavan Reilly talks about the technical challenges involved in moving from data to genes to learning about genetic variation relating to
Introduction: Wayne Folta points me to “EigenBracket 2012: Using Graph Theory to Predict NCAA March Madness Basketball” and writes, “I [Folta] have got to believe that he’s simply re-invented a statistical method in a graph-ish context, but don’t know enough to judge.” I have not looked in detail at the method being presented here—I’m not much of college basketball fan—but I’d like to use this as an excuse to make one of my favorite general point, which is that a good way to characterize any statistical method is by what information it uses. The basketball ranking method here uses score differentials between teams in the past season. On the plus side, that is better than simply using one-loss records (which (a) discards score differentials and (b) discards information on who played whom). On the minus side, the method appears to be discretizing the scores (thus throwing away information on the exact score differential) and doesn’t use any external information such as external ratings. A
3 0.91759467 303 andrew gelman stats-2010-09-28-“Genomics” vs. genetics
Introduction: John Cook and Joseph Delaney point to an article by Yurii Aulchenko et al., who write: 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people. . . . In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. . . . The message is that the simple approach of predicting child’s height using a regression model given parents’ average height performs much better than the method they have based on combining 54 genes. They also find that, if you start with the prediction based on parents’ heigh
4 0.90763187 1626 andrew gelman stats-2012-12-16-The lamest, grudgingest, non-retraction retraction ever
Introduction: In politics we’re familiar with the non-apology apology (well described in Wikipedia as “a statement that has the form of an apology but does not express the expected contrition”). Here’s the scientific equivalent: the non-retraction retraction. Sanjay Srivastava points to an amusing yet barfable story of a pair of researchers who (inadvertently, I assume) made a data coding error and were eventually moved to issue a correction notice, but even then refused to fully admit their error. As Srivastava puts it, the story “ended up with Lew [Goldberg] and colleagues [Kibeom Lee and Michael Ashton] publishing a comment on an erratum – the only time I’ve ever heard of that happening in a scientific journal.” From the comment on the erratum: In their “erratum and addendum,” Anderson and Ones (this issue) explained that we had brought their attention to the “potential” of a “possible” misalignment and described the results computed from re-aligned data as being based on a “post-ho
Introduction: Someone who wants to remain anonymous writes: I am working to create a more accurate in-game win probability model for basketball games. My idea is for each timestep in a game (a second, 5 seconds, etc), use the Vegas line, the current score differential, who has the ball, and the number of possessions played already (to account for differences in pace) to create a point estimate probability of the home team winning. This problem would seem to fit a multi-level model structure well. It seems silly to estimate 2,000 regressions (one for each timestep), but the coefficients should vary at each timestep. Do you have suggestions for what type of model this could/would be? Additionally, I believe this needs to be some form of logit/probit given the binary dependent variable (win or loss). Finally, do you have suggestions for what package could accomplish this in Stata or R? To answer the questions in reverse order: 3. I’d hope this could be done in Stan (which can be run from R)
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