andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1937 knowledge-graph by maker-knowledge-mining
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Introduction: I’ve said it here so often, this time I put it on the sister blog. . . .
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Introduction: I’ve said it here so often, this time I put it on the sister blog. . . .
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Introduction: At the sister blog .
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Introduction: I put it at the sister blog so the politics-haters among you could skip it. . . .
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Introduction: From the sister blog, some reasons why the political reaction might be different this time.
Introduction: I put it on the sister blog so you loyal readers here wouldn’t be distracted by it.
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Introduction: I’ve said it here so often, this time I put it on the sister blog. . . .
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Introduction: I put it at the sister blog so the politics-haters among you could skip it. . . .
Introduction: I put it on the sister blog so you loyal readers here wouldn’t be distracted by it.
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Introduction: At the sister blog .
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Introduction: Tutu forecast and religion and torture (from the sister blog). P.S. For partisan balance, don’t forget this projection from 1961.
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19 0.44875243 805 andrew gelman stats-2011-07-16-Hey–here’s what you missed in the past 30 days!
20 0.44612297 1773 andrew gelman stats-2013-03-21-2.15
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Introduction: Two positions open in the statistics group at the NYU education school. If you get the job, you get to work with Jennifer HIll! One position is a postdoctoral fellowship, and the other is a visiting professorship. The latter position requires “the demonstrated ability to develop a nationally recognized research program,” which seems like a lot to ask for a visiting professor. Do they expect the visiting prof to develop a nationally recognized research program and then leave it there at NYU after the visit is over? In any case, Jennifer and her colleagues are doing excellent work, both applied and methodological, and this seems like a great opportunity.
2 0.95865601 480 andrew gelman stats-2010-12-21-Instead of “confidence interval,” let’s say “uncertainty interval”
Introduction: I’ve become increasingly uncomfortable with the term “confidence interval,” for several reasons: - The well-known difficulties in interpretation (officially the confidence statement can be interpreted only on average, but people typically implicitly give the Bayesian interpretation to each case), - The ambiguity between confidence intervals and predictive intervals. (See the footnote in BDA where we discuss the difference between “inference” and “prediction” in the classical framework.) - The awkwardness of explaining that confidence intervals are big in noisy situations where you have less confidence, and confidence intervals are small when you have more confidence. So here’s my proposal. Let’s use the term “uncertainty interval” instead. The uncertainty interval tells you how much uncertainty you have. That works pretty well, I think. P.S. As of this writing, “confidence interval” outGoogles “uncertainty interval” by the huge margin of 9.5 million to 54000. So we
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Introduction: I’ve said it here so often, this time I put it on the sister blog. . . .
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Introduction: Tools worth knowing about: Google Refine is a power tool for working with messy data, cleaning it up, transforming it from one format into another, extending it with web services, and linking it to databases like Freebase. A recent discussion on the Polmeth list about the ANES Cumulative File is a setting where I think Refine might help (admittedly 49760×951 is bigger than I’d really like to deal with in the browser with js… but on a subset yes). [I might write this example up later.] Go watch the screencast videos for Refine. Data-entry problems are rampant in stuff we all use — leading or trailing spaces; mixed decimal-indicators; different units or transformations used in the same column; mixed lettercase leading to false duplicates; that’s only the beginning. Refine certainly would help find duplicates, and it counts things for you too. Just counting rows is too much for researchers sometimes (see yesterday’s post )! Refine 2.0 adds some data-collection tools for
Introduction: Gur Huberman points to an article on the financial crisis by Bethany McLean, who writes : lthough our understanding of what instigated the 2008 global financial crisis remains at best incomplete, there are a few widely agreed upon contributing factors. One of them is a 2004 rule change by the U.S. Securities and Exchange Commission that allowed investment banks to load up on leverage. This disastrous decision has been cited by a host of prominent economists, including Princeton professor and former Federal Reserve Vice-Chairman Alan Blinder and Nobel laureate Joseph Stiglitz. It has even been immortalized in Hollywood, figuring into the dark financial narrative that propelled the Academy Award-winning film Inside Job. . . . Here’s just one problem with this story line: It’s not true. Nor is it hard to prove that. Look at the historical leverage of the big five investment banks — Bear Stearns, Lehman Brothers, Merrill Lynch, Goldman Sachs and Morgan Stanley. The Government Accou
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