andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1685 knowledge-graph by maker-knowledge-mining
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Introduction: Sharad Goel, Jake Hofman, and Sergei Vassilvitskii are teaching this awesome class on computational social science this semester in the applied math department at Columbia. Here’s the course info . You should take this course. These guys are amazing.
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1 Sharad Goel, Jake Hofman, and Sergei Vassilvitskii are teaching this awesome class on computational social science this semester in the applied math department at Columbia. [sent-1, score-1.561]
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same-blog 1 0.99999994 1685 andrew gelman stats-2013-01-21-Class on computational social science this semester, Fridays, 1:00-3:40pm
Introduction: Sharad Goel, Jake Hofman, and Sergei Vassilvitskii are teaching this awesome class on computational social science this semester in the applied math department at Columbia. Here’s the course info . You should take this course. These guys are amazing.
Introduction: One thing we do here at the Applied Statistics Center is hold mini-conferences. The next one looks really cool. It’s organized by Sharad Goel and Jake Hofman (Microsoft Research, formerly at Yahoo Research), David Park (Columbia University), and Sergei Vassilvitskii (Google). As with our other conferences, one of our goals is to mix the academic and nonacademic research communities. Here’s the website for the workshop, and here’s the announcement from the organizers: With an explosion of data on every aspect of our everyday existence — from what we buy, to where we travel, to who we know — we are able to observe human behavior with granularity largely thought impossible just a decade ago. The growth of such online activity has further facilitated the design of web-based experiments, enhancing both the scale and efficiency of traditional methods. Together these advances have created an unprecedented opportunity to address longstanding questions in the social sciences, rang
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Introduction: This is pretty amazing.
4 0.14093234 1773 andrew gelman stats-2013-03-21-2.15
Introduction: Jake Hofman writes that he saw my recent newspaper article on running (“How fast do we slow down? . . . For each doubling of distance, the world record time is multiplied by about 2.15. . . . for sprints of 200 meters to 1,000 meters, a doubling of distance corresponds to an increase of a factor of 2.3 in world record running times; for longer distances from 1,000 meters to the marathon, a doubling of distance increases the time by a factor of 2.1. . . . similar patterns for men and women, and for swimming as well as running”) and writes: If you’re ever interested in getting or playing with Olympics data, I [Jake] wrote some code to scrape it all from sportsreference.com this past summer for a blog post . Enjoy!
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Introduction: Ironically, I can’t find the source for this awesome graphic that’s been making the rounds. -Phil
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same-blog 1 0.99068004 1685 andrew gelman stats-2013-01-21-Class on computational social science this semester, Fridays, 1:00-3:40pm
Introduction: Sharad Goel, Jake Hofman, and Sergei Vassilvitskii are teaching this awesome class on computational social science this semester in the applied math department at Columbia. Here’s the course info . You should take this course. These guys are amazing.
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Introduction: One advantage of teaching statistics is that you don’t have to worry about any celebrities taking your class.
Introduction: Joe Blitzstein and Xiao-Li Meng write : An e ffectively designed examination process goes far beyond revealing students’ knowledge or skills. It also serves as a great teaching and learning tool, incentivizing the students to think more deeply and to connect the dots at a higher level. This extends throughout the entire process: pre-exam preparation, the exam itself, and the post-exam period (the aftermath or, more appropriately, afterstat of the exam). As in the publication process, the first submission is essential but still just one piece in the dialogue. Viewing the entire exam process as an extended dialogue between students and faculty, we discuss ideas for making this dialogue induce more inspiration than perspiration, and thereby making it a memorable deep-learning triumph rather than a wish-to-forget test-taking trauma. We illustrate such a dialogue through a recently introduced course in the Harvard Statistics Department, Stat 399: Problem Solving in Statistics, and tw
4 0.71304792 1864 andrew gelman stats-2013-05-20-Evaluating Columbia University’s Frontiers of Science course
Introduction: Frontiers of Science is a course offered as part of Columbia University’s Core Curriculum. The course is controversial, with some people praising its overview of several areas of science, and others feeling that a more traditional set of introductory science courses would do the job better. Last month, the faculty in charge of the course wrote the following public letter : The United States is in the midst of a debate over the value of a traditional college education. Why enroll in a place like Columbia College when you can obtain an undergraduate degree for $10,000 or learn everything from Massive Open Online Courses? In more parochial terms, what is the value added by approaches such as Columbia’s Core Curriculum? Recently students in our Core Course, Frontiers of Science (FoS), provided a partial answer. The FoS faculty designed a survey to gauge the scientific skills and knowledge of the Class of 2016 both before and after taking FoS. In an assembly held during orientati
5 0.68111575 516 andrew gelman stats-2011-01-14-A new idea for a science core course based entirely on computer simulation
Introduction: Columbia College has for many years had a Core Curriculum, in which students read classics such as Plato (in translation) etc. A few years ago they created a Science core course. There was always some confusion about this idea: On one hand, how much would college freshmen really learn about science by reading the classic writings of Galileo, Laplace, Darwin, Einstein, etc.? And they certainly wouldn’t get much out by puzzling over the latest issues of Nature, Cell, and Physical Review Letters. On the other hand, what’s the point of having them read Dawkins, Gould, or even Brian Greene? These sorts of popularizations give you a sense of modern science (even to the extent of conveying some of the debates in these fields), but reading them might not give the same intellectual engagement that you’d get from wrestling with the Bible or Shakespeare. I have a different idea. What about structuring the entire course around computer programming and simulation? Start with a few weeks t
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same-blog 1 0.94951636 1685 andrew gelman stats-2013-01-21-Class on computational social science this semester, Fridays, 1:00-3:40pm
Introduction: Sharad Goel, Jake Hofman, and Sergei Vassilvitskii are teaching this awesome class on computational social science this semester in the applied math department at Columbia. Here’s the course info . You should take this course. These guys are amazing.
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Introduction: Would You Accept DNA From A Murderer?
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Introduction: Phil pointed me to this paper so I thought I probably better repeat what I wrote a couple years ago: 1. The effects are certainly not zero. We are not machines, and anything that can affect our expectations (for example, our success in previous tries) should affect our performance. 2. The effects I’ve seen are small, on the order of 2 percentage points (for example, the probability of a success in some sports task might be 45% if you’re “hot” and 43% otherwise). 3. There’s a huge amount of variation, not just between but also among players. Sometimes if you succeed you will stay relaxed and focused, other times you can succeed and get overconfidence. 4. Whatever the latest results on particular sports, I can’t see anyone overturning the basic finding of Gilovich, Vallone, and Tversky that players and spectators alike will perceive the hot hand even when it does not exist and dramatically overestimate the magnitude and consistency of any hot-hand phenomenon that does exist.
4 0.85315442 1215 andrew gelman stats-2012-03-16-The “hot hand” and problems with hypothesis testing
Introduction: Gur Yaari writes : Anyone who has ever watched a sports competition is familiar with expressions like “on fire”, “in the zone”, “on a roll”, “momentum” and so on. But what do these expressions really mean? In 1985 when Thomas Gilovich, Robert Vallone and Amos Tversky studied this phenomenon for the first time, they defined it as: “. . . these phrases express a belief that the performance of a player during a particular period is significantly better than expected on the basis of the player’s overall record”. Their conclusion was that what people tend to perceive as a “hot hand” is essentially a cognitive illusion caused by a misperception of random sequences. Until recently there was little, if any, evidence to rule out their conclusion. Increased computing power and new data availability from various sports now provide surprising evidence of this phenomenon, thus reigniting the debate. Yaari goes on to some studies that have found time dependence in basketball, baseball, voll
5 0.84365475 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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