andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-290 knowledge-graph by maker-knowledge-mining

290 andrew gelman stats-2010-09-22-Data Thief


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Introduction: John Transue sends along a link to this software for extracting data from graphs. I haven’t tried it out but it could be useful to somebody out there?


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 John Transue sends along a link to this software for extracting data from graphs. [sent-1, score-1.478]

2 I haven’t tried it out but it could be useful to somebody out there? [sent-2, score-0.778]


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Introduction: John Transue sends along a link to this software for extracting data from graphs. I haven’t tried it out but it could be useful to somebody out there?

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Introduction: Xian sends along this link that might be of interest to some of you.

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Introduction: Lee Wilkinson sends me this amusing ad for his new software, AdviseStat: The ad is a parody, but the software is real !

4 0.16843136 848 andrew gelman stats-2011-08-11-That xkcd cartoon on multiple comparisons that all of you were sending me a couple months ago

Introduction: John Transue sent it in with the following thoughtful comment: I’d imagine you’ve already received this, but just in case, here’s a cartoon you’d like. At first blush it seems to go against your advice (more nuanced than what I’m about to say by quoting the paper title) to not worry about multiple comparisons. However, if I understand correctly your argument about multiple comparisons in multilevel models, the situation in this comic might have been avoided if shrinkage toward the grand mean (of all colors) had prevented the greens from clearing the .05 threshold. Is that right?

5 0.12177591 1450 andrew gelman stats-2012-08-08-My upcoming talk for the data visualization meetup

Introduction: Somebody asked me to speak sometime at a data visualization meetup. I think I spoke there a year or two ago but I could do it again. Last time I spoke on Infovis vs Statistical Graphics , this time I could 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 would 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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4 0.71705496 450 andrew gelman stats-2010-12-04-The Joy of Stats

Introduction: Hal Varian sends in this link to a series of educational videos described to be “a journey into the heart of statistics.” It seems to be focused on exploratory data analysis, which it describes as “an extraordinary new method of understanding ourselves and our Universe.”

5 0.71090192 587 andrew gelman stats-2011-02-24-5 seconds of every #1 pop single

Introduction: This is pretty amazing. Now I want to hear volume 3. Also is there a way to download this as I play it so I can listen when I’m offline? P.S. Typo in title fixed. P.P.S. I originally gave a different link but was led to the apparently more definitive link above (which allows direct download) from a commenter . Thanks!

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18 0.56230551 681 andrew gelman stats-2011-04-26-Worst statistical graphic I have seen this year

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Introduction: John Transue sends along a link to this software for extracting data from graphs. I haven’t tried it out but it could be useful to somebody out there?

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4 0.7366637 1992 andrew gelman stats-2013-08-21-Workshop for Women in Machine Learning

Introduction: This might interest some of you: CALL FOR ABSTRACTS Workshop for Women in Machine Learning Co-located with NIPS 2013, Lake Tahoe, Nevada, USA December 5, 2013 http://www.wimlworkshop.org Deadline for abstract submissions: September 16, 2013 WORKSHOP DESCRIPTION The Workshop for Women in Machine Learning is a day-long event taking place on the first day of NIPS. The workshop aims to showcase the research of women in machine learning and to strengthen their community. The event brings together female faculty, graduate students, and research scientists for an opportunity to connect, exchange ideas, and learn from each other. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. While all presenters will be female, all genders are invited to attend. Scholarships will be provided to female students and postdoctoral attendees with accepted abstracts to partially offset travel costs. Workshop

5 0.72557563 136 andrew gelman stats-2010-07-09-Using ranks as numbers

Introduction: David Shor writes: I’m dealing with a situation where I have two datasets, one that assigns each participant a discrete score out of five for a set of particular traits (Dog behavior characteristics by breed), and another from an independent source that ranks each breed by each characteristic. It’s also possible to obtain the results of a survey, where experts were asked to rank 7 randomly picked breeds by characteristics. I’m interested in obtaining estimates for each trait, and intuitively, it seems clear that the second and third dataset provide a lot of information. But it’s unclear how to incorporate them to infer latent variables, since only sample ranks are observed. This seems like it is a common problem, do you have any suggestions? My quick answer is that you can treat ranks as numbers (a point we make somewhere in Bayesian Data Analysis, I believe) and just fit an item-response model from there. Val Johnson wrote an article on this in Jasa a few years ago, “Bayesia

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