andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-424 knowledge-graph by maker-knowledge-mining
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Introduction: Hal Varian writes: You might find this a useful tool for cleaning data. I haven’t tried it out yet, but data cleaning is a hugely important topic and so this could be a big deal.
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Introduction: Hal Varian writes: You might find this a useful tool for cleaning data. I haven’t tried it out yet, but data cleaning is a hugely important topic and so this could be a big deal.
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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.”
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Introduction: Hi all. We’ve moved the blog and are still working out some bugs. For example, we delete spam comments but sometimes they remain on the blog. A few other things. We should be cleaning it up more in the next few days.
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Introduction: After reading Rachel and Cathy’s book , I wrote that “Statistics is the least important part of data science . . . I think it would be fair to consider statistics as a subset of data science. . . . it’s not the most important part of data science, or even close.” But then I received “Data Science for Business,” by Foster Provost and Tom Fawcett, in the mail. I might not have opened the book at all (as I’m hardly in the target audience) but for seeing a blurb by Chris Volinsky, a statistician whom I respect a lot. So I flipped through the book and it indeed looked pretty good. It moves slowly but that’s appropriate for an intro book. But what surprised me, given the book’s title and our recent discussion on the nature of data science, was that the book was 100% statistics! It had some math (for example, definitions of various distance measures), some simple algebra, some conceptual graphs such as ROC curve, some tables and graphs of low-dimensional data summaries—but almost
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Introduction: In response to the latest controversy , a statistics professor writes: It’s somewhat surprising to see Very Serious Researchers (apologies to Paul Krugman) using Excel. Some years ago, I was consulting on a trademark infringement case and was trying (unsuccessfully) to replicate another expert’s regression analysis. It wasn’t until I had the brainstorm to use Excel that I was able to reproduce his results – it may be better now, but at the time, Excel could propagate round-off error and catastrophically cancel like no other software! Microsoft has lots of top researchers so it’s hard for me to understand how Excel can remain so crappy. I mean, sure, I understand in some general way that they have a large user base, it’s hard to maintain backward compatibility, there’s feature creep, and, besides all that, lots of people have different preferences in data analysis than I do. But still, it’s such a joke. Word has problems too, but I can see how these problems arise from its d
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Introduction: Ben Hyde sends along this : Stuck in the middle of the supplemental data, reporting the total workup for their compounds, was this gem: Emma, please insert NMR data here! where are they? and for this compound, just make up an elemental analysis . . . I’m reminded of our recent discussions of coauthorship, where I argued that I see real advantages to having multiple people taking responsibility for the result. Jay Verkuilen responded: “On the flipside of collaboration . . . is diffusion of responsibility, where everybody thinks someone else ‘has that problem’ and thus things don’t get solved.” That’s what seems to have happened (hilariously) here.
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Introduction: This post is by David K. Park As we have witnessed, the term “big data” has been thrusted onto the zeitgeist in the past several years, however, when one pushes beyond the hype, there seems to be little substance there. We’ve always had “data” so what so unique about it this time? Yes, we recognize it’s “big” but is there anything unique about data this time around? I’ve spend some time thinking about this and the answer seems to be yes, and it falls on three dimensions: Capturing Conversations & Relationships : Individuals have always communicated with one another, but now we can capture some of that conversation – email, blogs, social media (Facebook, Twitter, Pinterest) – and we can now do it with machines via sensors, ie “the internet of things” as we hear so much about; Granularity : We can now understand individuals at a much finer level of analysis. No longer do we need to rely on a sample size of 500 people to “represent” the nation, but instead we can acc
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