andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1806 knowledge-graph by maker-knowledge-mining
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Introduction: Choices in Visualizing Data This time, it’s not at the university, it’s at a data science meetup. Here are the slides . I actually prefer the term “statistical graphics” or “visualizing quantitative information” rather than “visualizing data.” I spend a lot of time graphing inferences and fitted models, understanding my fits and doing exploratory model analysis. Graphs aren’t just for raw data. P.S. Mike Stringer, who prepared the blurb for my talk at the above link, wrote that ARM “has the most understandable description of causal inference I’ve ever read.” I appreciate the compliment, but, to be fair, Jennifer deserves most of the credit for the causal chapters of that book.
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Introduction: Choices in Visualizing Data This time, it’s not at the university, it’s at a data science meetup. Here are the slides . I actually prefer the term “statistical graphics” or “visualizing quantitative information” rather than “visualizing data.” I spend a lot of time graphing inferences and fitted models, understanding my fits and doing exploratory model analysis. Graphs aren’t just for raw data. P.S. Mike Stringer, who prepared the blurb for my talk at the above link, wrote that ARM “has the most understandable description of causal inference I’ve ever read.” I appreciate the compliment, but, to be fair, Jennifer deserves most of the credit for the causal chapters of that book.
2 0.28351021 215 andrew gelman stats-2010-08-18-DataMarket
Introduction: It seems that every day brings a better system for exploring and sharing data on the Internet. From Iceland comes DataMarket . DataMarket is very good at visualizing individual datasets – with interaction and animation, although the “market” aspect hasn’t yet been developed, and all access is free. Here’s an example of visualizing rankings of countries competing in WorldCup: And here’s a lovely example of visualizing population pyramids : In the future, the visualizations will also include state of the art models for predicting and imputing missing data, and understanding the underlying mechanisms. Other posts: InfoChimps , Future of Data Analysis
3 0.20064732 1637 andrew gelman stats-2012-12-24-Textbook for data visualization?
Introduction: Dave Choi writes: I’m building a course called “Exploring and visualizing data,” for Heinz college in Carnegie Mellon (public policy and information systems). Do you know any books that might be good for such a course? I’m hoping to get non-statisticians to appreciate the statistician’s point of view on this subject. I immediately thought of Bill Cleveland’s 1985 classic, The Elements of Graphing Data, but I wasn’t sure of what comes next. There are a lot of books on how to make graphics in R, but I’m not quite sure that’s the point. And I’m loath to recommend Tufte since it would be kinda scary if a student were to take all of his ideas too seriously. Any suggestions?
4 0.19190779 1991 andrew gelman stats-2013-08-21-BDA3 table of contents (also a new paper on visualization)
Introduction: In response to our recent posting of Amazon’s offer of Bayesian Data Analysis 3rd edition at 40% off, some people asked what was in this new edition, with more information beyond the beautiful cover image and the brief paragraph I’d posted earlier. Here’s the table of contents. The following sections have all-new material: 1.4 New introduction of BDA principles using a simple spell checking example 2.9 Weakly informative prior distributions 5.7 Weakly informative priors for hierarchical variance parameters 7.1-7.4 Predictive accuracy for model evaluation and comparison 10.6 Computing environments 11.4 Split R-hat 11.5 New measure of effective number of simulation draws 13.7 Variational inference 13.8 Expectation propagation 13.9 Other approximations 14.6 Regularization for regression models C.1 Getting started with R and Stan C.2 Fitting a hierarchical model in Stan C.4 Programming Hamiltonian Monte Carlo in R And the new chapters: 20 Basis function models 2
5 0.15431406 1477 andrew gelman stats-2012-08-30-Visualizing Distributions of Covariance Matrices
Introduction: Since we’ve been discussing prior distributions on covariance matrices, I will recommend this recent article (coauthored with Tomoki Tokuda, Ben Goodrich, Iven Van Mechelen, and Francis Tuerlinckx) on their visualization: We present some methods for graphing distributions of covariance matrices and demonstrate them on several models, including the Wishart, inverse-Wishart, and scaled inverse-Wishart families in different dimensions. Our visualizations follow the principle of decomposing a covariance matrix into scale parameters and correlations, pulling out marginal summaries where possible and using two and three-dimensional plots to reveal multivariate structure. Visualizing a distribution of covariance matrices is a step beyond visualizing a single covariance matrix or a single multivariate dataset. Our visualization methods are available through the R package VisCov.
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Introduction: Choices in Visualizing Data This time, it’s not at the university, it’s at a data science meetup. Here are the slides . I actually prefer the term “statistical graphics” or “visualizing quantitative information” rather than “visualizing data.” I spend a lot of time graphing inferences and fitted models, understanding my fits and doing exploratory model analysis. Graphs aren’t just for raw data. P.S. Mike Stringer, who prepared the blurb for my talk at the above link, wrote that ARM “has the most understandable description of causal inference I’ve ever read.” I appreciate the compliment, but, to be fair, Jennifer deserves most of the credit for the causal chapters of that book.
2 0.64336479 340 andrew gelman stats-2010-10-13-Randomized experiments, non-randomized experiments, and observational studies
Introduction: In the spirit of Dehejia and Wahba: Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates: New Findings from Within-Study Comparisons , by Cook, Shadish, and Wong. Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments, by Shadish, Clark, and Steiner. I just talk about causal inference. These people do it. The second link above is particularly interesting because it includes discussions by some causal inference heavyweights. WWJD and all that.
3 0.62575531 2207 andrew gelman stats-2014-02-11-My talks in Bristol this Wed and London this Thurs
Introduction: 1. Causality and statistical learning (Wed 12 Feb 2014, 16:00, at University of Bristol): Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are a gold standard, yet I have spent almost all my applied career analyzing observational data. In this talk we shall consider various approaches to causal reasoning from the perspective of an applied statistician who recognizes the importance of causal identification, yet must learn from available information. This is a good one. They laughed their asses off when I did it in Ann Arbor. But it has serious stuff too. As George Carlin (or, for that matter, John or Brad) might say, it’s funny because it’s true. Here are some old slides, but I plan to mix in a bit of new material. 2. Theoretical Statistics is the Theory of Applied Statistics
Introduction: Consider two broad classes of inferential questions : 1. Forward causal inference . What might happen if we do X? What are the effects of smoking on health, the effects of schooling on knowledge, the effect of campaigns on election outcomes, and so forth? 2. Reverse causal inference . What causes Y? Why do more attractive people earn more money? Why do many poor people vote for Republicans and rich people vote for Democrats? Why did the economy collapse? When statisticians and econometricians write about causal inference, they focus on forward causal questions. Rubin always told us: Never ask Why? Only ask What if? And, from the econ perspective, causation is typically framed in terms of manipulations: if x had changed by 1, how much would y be expected to change, holding all else constant? But reverse causal questions are important too. They’re a natural way to think (consider the importance of the word “Why”) and are arguably more important than forward questions.
5 0.61028939 1668 andrew gelman stats-2013-01-11-My talk at the NY data visualization meetup this Monday!
Introduction: It’s in midtown at 7pm (on Mon 14 Jan 2013). Last time I talked for this group, I spoke on Infovis vs. Statistical Graphics . This time I plan to just go thru the choices involved in a few zillion graphs I’ve published over the years, to give a sense of the options and choices involved in graphical communication. For this talk there will be no single theme (except, perhaps, my usual “Graphs as comparisons,” “All of statistics as comparisons,” and “Exploratory data analysis as hypothesis testing”), just a bunch of open discussion about what I tried, why I tried it, what worked and what didn’t work, etc. I’ve discussed these sorts of decisions on occasion (and am now writing a paper with Yair about some of this for our voting models), but I’ve never tried to make a talk out of it before. Could be fun.
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1 0.96447515 376 andrew gelman stats-2010-10-28-My talk at American University
Introduction: Red State Blue State: How Will the U.S. Vote? It’s the “annual Halloween and pre-election extravaganza” of the Department of Mathematics and Statistics, and they suggested I could talk on the zombies paper (of course), but I thought the material on voting might be of more general interest. The “How will the U.S. vote?” subtitle was not of my choosing, but I suppose I can add a few slides about the forthcoming election. Fri 29 Oct 2010, 7pm in Ward I, in the basement of the Ward Circle building. Should be fun. I haven’t been to AU since taking a class there, over 30 years ago. P.S. It was indeed fun. Here’s the talk. I did end up briefly describing my zombie research but it didn’t make it into any of the slides.
2 0.95916879 1 andrew gelman stats-2010-04-22-Political Belief Networks: Socio-cognitive Heterogeneity in American Public Opinion
Introduction: Delia Baldassarri and Amir Goldberg write : Americans’ political beliefs present a long observed paradox. Whereas the mainstream political discourse is structured on a clearly defined polarity between conservative and liberal views, in practice, most people exhibit ideologically incoherent belief patterns. This paper challenges the notion that political beliefs are necessarily defined by a singular ideological continuum. It applies a new, network-based method for detecting heterogeneity in collective patterns of opinion, relational class analysis (RCA), to Americans’ political attitudes as captured by the American National Election Studies. By refraining from making a-priori assumptions about how beliefs are interconnected, RCA looks for opinion structures, belief networks, that are not necessarily congruent with received wisdom. It finds that in the twenty years between 1984 and 2004 Americans’ political attitudes were consistently structured by two alternative belief systems: one
3 0.95882368 196 andrew gelman stats-2010-08-10-The U.S. as welfare state
Introduction: My Columbia colleague Irv Garfinkel recently came out with a book (with coauthors Lee Rainwater and Timothy Smeeding), “Mythbusters: The U.S. Social Welfare State,” where they argue: The United States is a capitalist nation that has eschewed Scandinavian-style socialist policies in favor of capitalism and economic growth, right? Wrong. The U.S. is not only one of the largest welfare states in the world, but it is strong economically precisely because of its adoption of some socialist policies–with public education as the primary driver. The American welfare state faces large challenges. Restoring its historical lead in education is the most important but requires investing large sums in education–beginning with universal pre-school–and in complementary programs–including selected cash benefits for families with children–that aid children’s development. The American health insurance system is by far the most costly in the rich world, yet fails to insure one sixth of its population
Introduction: The course outline ZombieCourseOutline.rtf Hints/draft R code for implementing this for a regression example from D. Pena x=c(1:10,17,17,17) y=c(1:10,25,25,25) ZombieAssign1.txt The assignment being to provide a legend that explains all the lines and symbols in this plot ZombieAssign1.pdf With a bonus assignment being to provide better R code and or techniques. And a possible graduate student assignment to investigate what percentage of examples in graduate stats texts (e.g. Cox & Hinkley) could be displayed this way (reducing the number of parameters to least number possible). K? p.s. might have been a better post for Friday the 13th p.s.2 background material from my thesis (passed in 2007) ThesisReprint.pdf
5 0.95118916 26 andrew gelman stats-2010-05-11-Update on religious affiliations of Supreme Court justices
Introduction: When Sonia Sotomayor was nominated for the Supreme Court, and there was some discussion of having 6 Roman Catholics on the court at the same time, I posted the following historical graph: It’s time for an update: It’s still gonna take awhile for the Catholics to catch up. . . . And this one might be relevant too: It looks as if Jews and men have been overrepresented, also Episcopalians (which, as I noted earlier, are not necessarily considered Protestant in terms of religious doctrine but which I counted as such for the ethnic categorization). Religion is an interesting political variable because it’s nominally about religious belief but typically seems to be more about ethnicity.
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