andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1138 knowledge-graph by maker-knowledge-mining
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Introduction: Chris Schmid is a statistician at New England Medical Center who is an expert on evidence-based medicine. I invited him to present an introductory overview lecture on the topic at last year’s Joint Statistical Meetings, and here are his slides . All 123 of them. I don’t know how he expected to go though all of these in an hour. You could teach a semester-long course based on this material. Good stuff, I recommend you all read it.
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Introduction: Chris Schmid is a statistician at New England Medical Center who is an expert on evidence-based medicine. I invited him to present an introductory overview lecture on the topic at last year’s Joint Statistical Meetings, and here are his slides . All 123 of them. I don’t know how he expected to go though all of these in an hour. You could teach a semester-long course based on this material. Good stuff, I recommend you all read it.
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Introduction: Try this link . . . . OK, it worked (as well as might be expected given that we don’t have any professional audiovisual people involved). Tomorrow 8h30, I’ll post a new link with the new G+ hangout. We’ll be going through the first two sets of slides (class1a.pdf and class1b.pdf) following the link for the slides here .
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Introduction: Good stuff.
Introduction: See if you can interpolate the talk from the slides . The background is: I was invited to speak in this seminar on “big data.” I said I didn’t know anything about big data, I worked on little data. They said that was ok. Actually it was probably a crowd-pleasing move to tell these people that little-data ideas remain relevant.
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Introduction: Chris Schmid is a statistician at New England Medical Center who is an expert on evidence-based medicine. I invited him to present an introductory overview lecture on the topic at last year’s Joint Statistical Meetings, and here are his slides . All 123 of them. I don’t know how he expected to go though all of these in an hour. You could teach a semester-long course based on this material. Good stuff, I recommend you all read it.
2 0.69461888 313 andrew gelman stats-2010-10-03-A question for psychometricians
Introduction: Don Coffin writes: A colleague of mine and I are doing a presentation for new faculty on a number of topics related to teaching. Our charge is to identify interesting issues and to find research-based information for them about how to approach things. So, what I wondered is, do you know of any published research dealing with the sort of issues about structuring a course and final exam in the ways you talk about in this blog post ? Some poking around in the usual places hasn’t turned anything up yet. I don’t really know the psychometrics literature but I imagine that some good stuff has been written on principles of test design. There are probably some good papers from back in the 1920s. Can anyone supply some references?
Introduction: She was assigned to teach a class in “evidence-based medicine”! ( link from my usual news source). I wonder what was in the syllabus? If anyone has a copy, feel free to send to me and I will post it here. My favorite part of the story, though, is this: Almost all physician assistant students refused to comment to a reporter Tuesday, saying they’d been told by the department not to talk to media. Talk about obedience to authority! They’re studying in a program that offers nonexistent courses, but then they follow the department’s gag order.
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Introduction: I spoke at the University of Kansas the other day. Kansas is far away so I gave the talk by video. We did it using a G+ hangout, and it worked really well, much much better than when I gave a talk via Skype . With G+, I could see and hear the audience clearly, and they could hear me just fine while seeing my slides (or my face, I went back and forth). Not as good as a live presentation but pretty good, considering. P.S. And here’s how to do it! Conflict of interest disclaimer: I was paid by Google last year to give a short course.
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Introduction: A colleague asked if I had any material for a course in sample surveys. And indeed I do. See here . It’s all the slides for a 14-week course, also the syllabus (“surveyscourse.pdf”), the final exam (“final2012.pdf”) and various misc files. Also more discussion of final exam questions here (keep scrolling thru the “previous entries” until you get to Question 1). Enjoy! This is in no way a self-contained teach-it-yourself course, but I do think it could be helpful for anyone who is trying to teach a class on this material.
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Introduction: Chris Schmid is a statistician at New England Medical Center who is an expert on evidence-based medicine. I invited him to present an introductory overview lecture on the topic at last year’s Joint Statistical Meetings, and here are his slides . All 123 of them. I don’t know how he expected to go though all of these in an hour. You could teach a semester-long course based on this material. Good stuff, I recommend you all read it.
Introduction: Paul Pudaite writes in response to my discussion with Bartels regarding effect sizes and measurement error models: You [Gelman] wrote: “I actually think there will be some (non-Gaussian) models for which, as y gets larger, E(x|y) can actually go back toward zero.” I [Pudaite] encountered this phenomenon some time in the ’90s. See this graph which shows the conditional expectation of X given Z, when Z = X + Y and the probability density functions of X and Y are, respectively, exp(-x^2) and 1/(y^2+1) (times appropriate constants). As the magnitude of Z increases, E[X|Z] shrinks to zero. I wasn’t sure it was worth the effort to try to publish a two paragraph paper. I suspect that this is true whenever the tail of one distribution is ‘sufficiently heavy’ with respect to the tail of the other. Hmm, I suppose there might be enough substance in a paper that attempted to characterize this outcome for, say, unimodal symmetric distributions. Maybe someone can do this? I think i
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Introduction: Geen Tomko asks: Can you recommend a good introductory book for statistical computation? Mostly, something that would help make it easier in collecting and analyzing data from student test scores. I don’t know. Usually, when people ask for a starter statistics book, my recommendation (beyond my own books) is The Statistical Sleuth. But that’s not really a computation book. ARM isn’t really a statistical computation book either. But the statistical computation books that I’ve seen don’t seems so relevant for the analyses that Tomko is looking for. For example, the R book of Venables and Ripley focuses on nonparametric statistics, which is fine but seems a bit esoteric for these purposes. Does anyone have any suggestions?
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Introduction: In his review in 1938 of Historical Development of the Graphical Representation of Statistical Data , by H. Gray Funkhauser, for The Economic Journal , the great economist writes: Perhaps the most striking outcome of Mr. Funkhouser’s researches is the fact of the very slow progress which graphical methods made until quite recently. . . . In the first fifty volumes of the Statistical Journal, 1837-87, only fourteen graphs are printed altogether. It is surprising to be told that Laplace never drew a graph of the normal law of error . . . Edgeworth made no use of statistical charts as distinct from mathematical diagrams. Apart from Quetelet and Jevons, the most important influences were probably those of Galton and of Mulhall’s Dictionary, first published in 1884. Galton was indeed following his father and grandfather in this field, but his pioneer work was mainly restricted to meteorological maps, and he did not contribute to the development of the graphical representation of ec
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