andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-65 knowledge-graph by maker-knowledge-mining
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Introduction: Alban Zeber writes: I am wondering whether there is a reference (online or book) that you would recommend to someone who is interested in learning how to program in R. Any thoughts? P.S. If I had a name like that, my books would be named, “Bayesian Statistics from A to Z,” “Teaching Statistics from A to Z,” “Regression and Multilevel Modeling from A to Z,” and so forth.
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same-blog 1 1.0000001 65 andrew gelman stats-2010-06-03-How best to learn R?
Introduction: Alban Zeber writes: I am wondering whether there is a reference (online or book) that you would recommend to someone who is interested in learning how to program in R. Any thoughts? P.S. If I had a name like that, my books would be named, “Bayesian Statistics from A to Z,” “Teaching Statistics from A to Z,” “Regression and Multilevel Modeling from A to Z,” and so forth.
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Introduction: Eoin Lawless wrote me: I’ve been reading your blog (and John Kruschke ‘s) for several months now, as a result of starting to learn Bayesian methods from Doing Bayesian Data Analysis [I love the title of that book! --- ed.]. More recently I completed a Coursera course on Data Science. I found learning through the medium of a online course to be an amazing experience. It does not replace books, but learning new material at the same time as other people and discussing it in the forums is very motivational. Additionally it is much easier to work through exercises and projects when there is a deadline and some element of competition than to plow through the end of chapter exercises in a book. This is especially true, I believe, when the learning is for a long term goal, rather than to be used immediately in work, for example. My question: you are obviously evangelical about the benefits that Bayesian statistics brings, have you ever considered producing a Coursera (or similar) cour
3 0.14754042 352 andrew gelman stats-2010-10-19-Analysis of survey data: Design based models vs. hierarchical modeling?
Introduction: Alban Zeber writes: Suppose I have survey data from say 10 countries where by each country collected the data based on different sampling routines – the results of this being that each country has its own weights for the data that can be used in the analyses. If I analyse the data of each country separately then I can incorporate the survey design in the analyses e.g in Stata once can use svyset ….. But what happens when I want to do a pooled analysis of the all the data from the 10 countries: Presumably either 1. I analyse the data from each country separately (using multiple or logistic regression, …) accounting for the survey design and then combine the estimates using a meta analysis (fixed or random) OR 2. Assume that the data from each country is a simple random sample from the population, combine the data from the 10 countries and then use multilevel or hierarchical models My question is which of the methods is likely to give better estimates? Or is the
Introduction: A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could provide data w/ R and/or BUGS codes that I could replicate them? My reply: For Bayesian hierarchical (multi-level) linear regression and causal inference, see my book with Jennifer Hill. For Bayesian structural equation modeling, try google and you’ll find some good stuff. Also, I recommend Stan (http://mc-stan.org/) rather than Bugs.
5 0.12601978 295 andrew gelman stats-2010-09-25-Clusters with very small numbers of observations
Introduction: James O’Brien writes: How would you explain, to a “classically-trained” hypothesis-tester, that “It’s OK to fit a multilevel model even if some groups have only one observation each”? I [O'Brien] think I understand the logic and the statistical principles at work in this, but I’ve having trouble being clear and persuasive. I also feel like I’m contending with some methodological conventional wisdom here. My reply: I’m so used to this idea that I find it difficult to defend it in some sort of general conceptual way. So let me retreat to a more functional defense, which is that multilevel modeling gives good estimates, especially when the number of observations per group is small. One way to see this in any particular example in through cross-validation. Another way is to consider the alternatives. If you try really hard you can come up with a “classical hypothesis testing” approach which will do as well as the multilevel model. It would just take a lot of work. I’d r
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Introduction: Alban Zeber writes: I am wondering whether there is a reference (online or book) that you would recommend to someone who is interested in learning how to program in R. Any thoughts? P.S. If I had a name like that, my books would be named, “Bayesian Statistics from A to Z,” “Teaching Statistics from A to Z,” “Regression and Multilevel Modeling from A to Z,” and so forth.
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Introduction: A student I’m working with writes: I was planning on getting a applied stat text as a desk reference, and for that I’m assuming you’d recommend your own book. Also, being an economics student, I was initially planning on doing my analysis in STATA, but I noticed on your blog that you use R, and apparently so does the rest of the statistics profession. Would you rather I do my programming in R this summer, or does it not matter? It doesn’t look too hard to learn, so just let me know what’s most convenient for you. My reply: Yes, I recommend my book with Jennifer Hill. Also the book by John Fox, An R and S-plus Companion to Applied Regression, is a good way to get into R. I recommend you use both Stata and R. If you’re already familiar with Stata, then stick with it–it’s a great system for working with big datasets. You can grab your data in Stata, do some basic manipulations, then save a smaller dataset to read into R (using R’s read.dta() function). Once you want to make fu
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Introduction: Eoin Lawless wrote me: I’ve been reading your blog (and John Kruschke ‘s) for several months now, as a result of starting to learn Bayesian methods from Doing Bayesian Data Analysis [I love the title of that book! --- ed.]. More recently I completed a Coursera course on Data Science. I found learning through the medium of a online course to be an amazing experience. It does not replace books, but learning new material at the same time as other people and discussing it in the forums is very motivational. Additionally it is much easier to work through exercises and projects when there is a deadline and some element of competition than to plow through the end of chapter exercises in a book. This is especially true, I believe, when the learning is for a long term goal, rather than to be used immediately in work, for example. My question: you are obviously evangelical about the benefits that Bayesian statistics brings, have you ever considered producing a Coursera (or similar) cour
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Introduction: Rama Ganesan writes: I have been using many of your demos from the Teaching Stats book . . . Do you by any chance have a nice easy dataset that I can use to show students how ‘lurking variables’ work using regression? For instance, in your book you talk about the relationship between height and salaries – where gender is the hidden variable. Any suggestions?
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Introduction: Daniel Gerlanc asks: I’ve been reading your Regression and Multilevel Modeling book. Do you have a set of example solutions for the problems in the book? Henning Piezunka, Adam Lynton, and others have asked the same question. My universal response: I’m glad you like our book. Unfortunately, we have no solution sets. I made a bunch of solutions for my earlier book but it was so much work that I decided not to do it a second time!
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Introduction: Alban Zeber writes: I am wondering whether there is a reference (online or book) that you would recommend to someone who is interested in learning how to program in R. Any thoughts? P.S. If I had a name like that, my books would be named, “Bayesian Statistics from A to Z,” “Teaching Statistics from A to Z,” “Regression and Multilevel Modeling from A to Z,” and so forth.
2 0.96855778 1687 andrew gelman stats-2013-01-21-Workshop on science communication for graduate students
Introduction: Nathan Sanders writes: Applications are now open for the Communicating Science 2013 workshop (http://workshop.astrobites.com/), to be held in Cambridge, MA on June 13-15th, 2013. Graduate students at US institutions in all fields of science and engineering are encouraged to apply – funding is available for travel expenses and accommodations. The application can be found here: http://workshop.astrobites.org/application Participants will build the communication skills that technical professionals need to express complex ideas to their peers, experts in other fields, and the general public. There will be panel discussions on the following topics: * Engaging Non-Scientific Audiences * Science Writing for a Cause * Communicating Science Through Fiction * Sharing Science with Scientists * The World of Non-Academic Publishing * Communicating using Multimedia and the Web In addition to these discussions, ample time is allotted for interacting with the experts and with att
3 0.96467036 1491 andrew gelman stats-2012-09-10-Update on Levitt paper on child car seats
Introduction: A few years ago I noted the following quote from applied microeconomist Steven Levitt: Is it surprising that scientists would try to keep work that disagrees with their findings out of journals? When I told my father that I [Levitt] was sending my work saying car seats are not that effective to medical journals, he laughed and said they would never publish it because of the result, no matter how well done the analysis was. (As is so often the case, he was right, and I eventually published it in an economics journal.) Within the field of economics, academics work behind the scenes constantly trying to undermine each other. I’ve seen economists do far worse things than pulling tricks in figures. When economists get mixed up in public policy, things get messier. At the time, I expressed dismay about Levitt’s air of (as I read it) amused, world-weary tolerance of scientists behaving against the interest of science. But I took his story about the car seats at face value. But no
4 0.96430111 1940 andrew gelman stats-2013-07-16-A poll that throws away data???
Introduction: Mark Blumenthal writes: What do you think about the “random rejection” method used by PPP that was attacked at some length today by a Republican pollster. Our just published post on the debate includes all the details as I know them. The Storify of Martino’s tweets has some additional data tables linked to toward the end. Also, more specifically, setting aside Martino’s suggestion of manipulation (which is also quite possible with post-stratification weights), would the PPP method introduce more potential random error than weighting? From Blumenthal’s blog: B.J. Martino, a senior vice president at the Republican polling firm The Tarrance Group, went on an 30-minute Twitter rant on Tuesday questioning the unorthodox method used by PPP [Public Policy Polling] to select samples and weight data: “Looking at @ppppolls new VA SW. Wondering how many interviews they discarded to get down to 601 completes? Because @ppppolls discards a LOT of interviews. Of 64,811 conducted
5 0.96193808 1392 andrew gelman stats-2012-06-26-Occam
Introduction: Cosma Shalizi and Larry Wasserman discuss some papers from a conference on Ockham’s Razor. I don’t have anything new to add on this so let me link to past blog entries on the topic and repost the following from 2004 : A lot has been written in statistics about “parsimony”—that is, the desire to explain phenomena using fewer parameters–but I’ve never seen any good general justification for parsimony. (I don’t count “Occam’s Razor,” or “Ockham’s Razor,” or whatever, as a justification. You gotta do better than digging up a 700-year-old quote.) Maybe it’s because I work in social science, but my feeling is: if you can approximate reality with just a few parameters, fine. If you can use more parameters to fold in more information, that’s even better. In practice, I often use simple models—because they are less effort to fit and, especially, to understand. But I don’t kid myself that they’re better than more complicated efforts! My favorite quote on this comes from Rad
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