andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2365 knowledge-graph by maker-knowledge-mining
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Introduction: A recent discussion with Mark Palko [scroll down to the comments at this link ] reminds me that I think that polynomials are way way overrated, and I think a lot of damage has arisen from the old-time approach of introducing polynomial functions as a canonical example of linear regressions ( for example ). There are very few settings I can think of where it makes sense to fit a general polynomial of degree higher than 2. I think that millions of students have been brainwashed into thinking of these as the canonical functions and that this has caused endless trouble later on. I’m not sure how I’d change the high school math curriculum to deal with this, but I do think it’s an issue.
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3 I’m not sure how I’d change the high school math curriculum to deal with this, but I do think it’s an issue. [sent-4, score-0.828]
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Introduction: A recent discussion with Mark Palko [scroll down to the comments at this link ] reminds me that I think that polynomials are way way overrated, and I think a lot of damage has arisen from the old-time approach of introducing polynomial functions as a canonical example of linear regressions ( for example ). There are very few settings I can think of where it makes sense to fit a general polynomial of degree higher than 2. I think that millions of students have been brainwashed into thinking of these as the canonical functions and that this has caused endless trouble later on. I’m not sure how I’d change the high school math curriculum to deal with this, but I do think it’s an issue.
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Introduction: Mark Palko asks : What are the worst examples of curriculum dead wood? Here’s the background: One of the first things that hit me [Palko] when I started teaching high school math was how much material there was to cover. . . . The most annoying part, though, was the number of topics that could easily have been cut, thus giving the students the time to master the important skills and concepts. The example that really stuck with me was synthetic division, a more concise but less intuitive way of performing polynomial long division. Both of these topics are pretty much useless in daily life but polynomial long division does, at least, give the student some insight into the relationship between polynomials and familiar base-ten numbers. Synthetic division has no such value; it’s just a faster but less interesting way of doing something you’ll never have to do. I started asking hardcore math people — mathematicians, statisticians, physicists, rocket scientists — if they.’d ever u
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Introduction: Fun stories here (from Kliph Nesteroff, link from Mark Palko).
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Introduction: Following up on my regression-discontinuity post from the other day, Brad DeLong writes : The feel (and I could well be wrong) as that at some point somebody said: “This is very important, but it won’t get published without a statistically significant headline finding. Torture the data via specification search until we find a statistically significant effect so that this can get published!” I think DeLong is mistaken here. But, before getting to this, here’s the graph: and here are the regression results: So, indeed it is that cubic term that takes the result into statistical significance. The reason I disagree with DeLong is that it’s my impression that, in econometrics and applied economics, it’s considered the safe, conservative choice in regression discontinuity to control for a high-degree polynomial. See the paper discussed a few years ago here , for example, where I criticized a pair of economists for using a fifth-degree specification and they replie
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Introduction: Kaiser asks the interesting question: How do you measure what restaurants are “overrated”? You can’t just ask people, right? There’s some sort of social element here, that “overrated” implies that someone’s out there doing the rating.
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same-blog 1 0.93312848 2365 andrew gelman stats-2014-06-09-I hate polynomials
Introduction: A recent discussion with Mark Palko [scroll down to the comments at this link ] reminds me that I think that polynomials are way way overrated, and I think a lot of damage has arisen from the old-time approach of introducing polynomial functions as a canonical example of linear regressions ( for example ). There are very few settings I can think of where it makes sense to fit a general polynomial of degree higher than 2. I think that millions of students have been brainwashed into thinking of these as the canonical functions and that this has caused endless trouble later on. I’m not sure how I’d change the high school math curriculum to deal with this, but I do think it’s an issue.
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Introduction: Mark Palko writes : You lose information when you go from a vector to a scalar. But what about this trick, which they told me about in high school? Combine two dimensions into one by interleaving the decimals. For example, if a=.11111 and b=.22222, then (a,b) = .1212121212.
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Introduction: We have a new “ Objects of the class ,” and it’s a good one! Here’s what happened. I came across a thoughtful discussion by Mark Palko of how it was that Secretary of Education Arne Duncan ticked off so many people with his recent remarks about “white suburban moms”: To understand why Duncan hit such a nerve, you need to consider the long and complicated role that racial politics have played in this debate. The public face of the education reform movement has always been pictures of eager young African-American and Hispanic children. Not only has the movement been sold as a way of helping these children but people who object to parts of the reform agenda have often been accused, implicitly or explicitly, of not wanting to help children of color. . . . For starters, with certain notable exceptions, the leaders of the reform movement tend to be white or Asian . . . By comparison, the tenured and/or unionized teachers who have paid the highest price in terms of policy changes an
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same-blog 1 0.96022105 2365 andrew gelman stats-2014-06-09-I hate polynomials
Introduction: A recent discussion with Mark Palko [scroll down to the comments at this link ] reminds me that I think that polynomials are way way overrated, and I think a lot of damage has arisen from the old-time approach of introducing polynomial functions as a canonical example of linear regressions ( for example ). There are very few settings I can think of where it makes sense to fit a general polynomial of degree higher than 2. I think that millions of students have been brainwashed into thinking of these as the canonical functions and that this has caused endless trouble later on. I’m not sure how I’d change the high school math curriculum to deal with this, but I do think it’s an issue.
Introduction: Arnaud Trolle (no relation ) writes: I have a question about the interpretation of (non-)overlapping of 95% credibility intervals. In a Bayesian ANOVA (a within-subjects one), I computed 95% credibility intervals about the main effects of a factor. I’d like to compare two by two the main effects across the different conditions of the factor. Can I directly interpret the (non-)overlapping of these credibility intervals and make the following statements: “As the 95% credibility intervals do not overlap, both conditions have significantly different main effects” or conversely “As the 95% credibility intervals overlap, the main effects of both conditions are not significantly different, i.e. equivalent”? I heard that, in the case of classical confidence intervals, the second statement is false, but what happens when working within a Bayesian framework? My reply: I think it makes more sense to directly look at inference for the difference. Also, your statements about equivalence
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Introduction: Eric Mvukiyehe and Cyrus Samii write : We [Mvukiyehe and Samii] use original survey data and administrative data to test a theory of the micro-level impacts of peacekeeping. The theory proposes that through the creation of local security bubbles and also through direct assistance, peacekeeping deployments contribute to economic and social revitalization that may contribute to more durable peace. This theory guides the design of current United Nations peacekeeping operations, and has been proposed as one of the explanations for peacekeeping’s well-documented association with more durable peace. Our evidence paint a complex picture that deviates substantially from the theory. We do not find evidence for local security bubbles around deployment base areas, and we do not find that deployments were substantial contributors to local social infrastructure. In addition, we find a negative relationship between deployment basing locations and NGO contributions to social infrastructure.
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