andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-685 knowledge-graph by maker-knowledge-mining
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Introduction: With all this data floating around, there are some interesting analyses one can do. I came across “The Association of Tree Pollen Concentration Peaks and Allergy Medication Sales in New York City: 2003-2008″ by Perry Sheffield . There they correlate pollen counts with anti-allergy medicine sales – and indeed find that two days after high pollen counts, the medicine sales are the highest. Of course, it would be interesting to play with the data to see *what* tree is actually causing the sales to increase the most. Perhaps this would help the arborists what trees to plant. At the moment they seem to be following a rather sexist approach to tree planting: Ogren says the city could solve the problem by planting only female trees, which don’t produce pollen like male trees do. City arborists shy away from females because many produce messy – or in the case of ginkgos, smelly – fruit that litters sidewalks. In Ogren’s opinion, that’s a mistake. He says the females only pro
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1 With all this data floating around, there are some interesting analyses one can do. [sent-1, score-0.203]
2 I came across “The Association of Tree Pollen Concentration Peaks and Allergy Medication Sales in New York City: 2003-2008″ by Perry Sheffield . [sent-2, score-0.07]
3 There they correlate pollen counts with anti-allergy medicine sales – and indeed find that two days after high pollen counts, the medicine sales are the highest. [sent-3, score-2.252]
4 Of course, it would be interesting to play with the data to see *what* tree is actually causing the sales to increase the most. [sent-4, score-0.712]
5 Perhaps this would help the arborists what trees to plant. [sent-5, score-0.501]
6 At the moment they seem to be following a rather sexist approach to tree planting: Ogren says the city could solve the problem by planting only female trees, which don’t produce pollen like male trees do. [sent-6, score-2.102]
7 City arborists shy away from females because many produce messy – or in the case of ginkgos, smelly – fruit that litters sidewalks. [sent-7, score-1.059]
8 He says the females only produce fruit because they are pollinated by the males. [sent-9, score-0.699]
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Introduction: With all this data floating around, there are some interesting analyses one can do. I came across “The Association of Tree Pollen Concentration Peaks and Allergy Medication Sales in New York City: 2003-2008″ by Perry Sheffield . There they correlate pollen counts with anti-allergy medicine sales – and indeed find that two days after high pollen counts, the medicine sales are the highest. Of course, it would be interesting to play with the data to see *what* tree is actually causing the sales to increase the most. Perhaps this would help the arborists what trees to plant. At the moment they seem to be following a rather sexist approach to tree planting: Ogren says the city could solve the problem by planting only female trees, which don’t produce pollen like male trees do. City arborists shy away from females because many produce messy – or in the case of ginkgos, smelly – fruit that litters sidewalks. In Ogren’s opinion, that’s a mistake. He says the females only pro
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Introduction: Becky Passonneau and colleagues at the Center for Computational Learning Systems (CCLS) at Columbia have been working on a project for ConEd (New York’s major electric utility) to rank structures based on vulnerability to secondary events (e.g., transformer explosions, cable meltdowns, electrical fires). They’ve been using the R implementation BayesTree of Chipman, George and McCulloch’s Bayesian Additive Regression Trees (BART). BART is a Bayesian non-parametric method that is non-identifiable in two ways. Firstly, it is an additive tree model with a fixed number of trees, the indexes of which aren’t identified (you get the same predictions in a model swapping the order of the trees). This is the same kind of non-identifiability you get with any mixture model (additive or interpolated) with an exchangeable prior on the mixture components. Secondly, the trees themselves have varying structure over samples in terms of number of nodes and their topology (depth, branching, etc
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Introduction: Aleks points me to this research summary from Dan Goldstein. Good stuff. I’ve heard of a lot of this–I actually use some of it in my intro statistics course, when we show the students how they can express probability trees using frequencies–but it’s good to see it all in one place.
Introduction: Ben Hyde sends along this appealing image by Michael Paukner, which represents a nearly perfect distillation of “infographics”: Here are some of the comments on the linked page: Rather than redrawing the picture to make the lines more clear, I’d say: leave the graphic as is, and have a link to a set of statistical graphs that show where the different sorts of old trees are and what they look like. Let’s value the above image for its clean look and its clever Christmas-tree design, and once we have it, take advantage of viewers’ interest in the topic to show them more. P.S. See my comment below which I think further illuminates the appeal of this particular tree.
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Introduction: From Wikipedia (via Jay Livingston ): Newsweek sells only about 40,000 newsstand copies compared with 1.5 million subscriptions. (Both figures are substantially lower than they were a decade ago.) The figures for Time are about double those of Newsweek, but the ratio of newsstand sales to subscriptions is about the same. I guess I’m not surprised that most of the sales are from subscriptions, but I’m surprised the fraction is so close to 100%.
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Introduction: With all this data floating around, there are some interesting analyses one can do. I came across “The Association of Tree Pollen Concentration Peaks and Allergy Medication Sales in New York City: 2003-2008″ by Perry Sheffield . There they correlate pollen counts with anti-allergy medicine sales – and indeed find that two days after high pollen counts, the medicine sales are the highest. Of course, it would be interesting to play with the data to see *what* tree is actually causing the sales to increase the most. Perhaps this would help the arborists what trees to plant. At the moment they seem to be following a rather sexist approach to tree planting: Ogren says the city could solve the problem by planting only female trees, which don’t produce pollen like male trees do. City arborists shy away from females because many produce messy – or in the case of ginkgos, smelly – fruit that litters sidewalks. In Ogren’s opinion, that’s a mistake. He says the females only pro
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Introduction: Aleks pointed me to this recent article by Pablo Mateos, Paul Longley, and David O’Sullivan on one of my favorite topics. The authors produced a potentially cool naming network of the city of Auckland New Zealand . I say “potentially cool” because I have such difficulty reading the article–I speak English, statistics, and a bit of political science and economics, but this one is written in heavy sociologese–that I can’t quite be sure what they’re doing. However, despite my (perhaps unfair) disdain for the particulars of their method, it’s probably good that they’re jumping in with this analysis. Others can take their data (and similar datasets from elsewhere) and do better. Ya gotta start somewhere, and the basic idea (to cluster first names that are associated with the same last names, and to cluster last names that are associated with the same first names) seems good. I have to admit, though, that I was amused by the following line, which, amazingly, led off the paper:
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Introduction: In the spirit of Gapminder , Washington Post created an interactive scatterplot viewer that’s using alpha channel to tell apart overlapping fat dots better than sorting-by-circle-size Gapminder is using: Good news: the rate of fattening of the USA appears to be slowing down. Maybe because of high gas prices? But what’s happening with Oceania?
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Introduction: I Paid a Bribe by Janaagraha, a Bangalore based not-for-profit, harnesses the collective energy of citizens and asks them to report on the nature, number, pattern, types, location, frequency and values of corruption activities. These reports would be used to argue for improving governance systems and procedures, tightening law enforcement and regulation and thereby reduce the scope for corruption. Here’s a presentation of data from the application: Transparency International could make something like this much more widely available around the world . While awareness is good, follow-up is even better. For example, it’s known that New York’s subway signal inspections were being falsified . Signal inspections are pretty serious stuff, as failures lead to disasters , such as the one in Washington. Nothing much happened after: the person responsible (making $163k a year) was merely reassigned .
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Introduction: I stumbled across a chart that’s in my opinion the best way to express a comparison of quantities through time: It compares the new PC companies, such as Apple, to traditional PC companies like IBM and Compaq, but on the same scale. If you’d like to see how iPads and other novelties compare, see here . I’ve tried to use the same type of visualization in my old work on legal data visualization . It comes from a new market research firm Asymco that also produced a very clean income vs expenses visualization (click to enlarge): While the first figure is pure perfection, Tufte purists might find the second one too colorful. But to a busy person, color helps tell things apart: when I know that pink means interest, it takes a fraction of the second to assess the situation. We live in 2012, not in 1712 to have to think black and white. Finally, they have a few other interesting uses of interactive visualization, such as cellular-broadband infrastructure around
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Introduction: With all this data floating around, there are some interesting analyses one can do. I came across “The Association of Tree Pollen Concentration Peaks and Allergy Medication Sales in New York City: 2003-2008″ by Perry Sheffield . There they correlate pollen counts with anti-allergy medicine sales – and indeed find that two days after high pollen counts, the medicine sales are the highest. Of course, it would be interesting to play with the data to see *what* tree is actually causing the sales to increase the most. Perhaps this would help the arborists what trees to plant. At the moment they seem to be following a rather sexist approach to tree planting: Ogren says the city could solve the problem by planting only female trees, which don’t produce pollen like male trees do. City arborists shy away from females because many produce messy – or in the case of ginkgos, smelly – fruit that litters sidewalks. In Ogren’s opinion, that’s a mistake. He says the females only pro
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Introduction: John Cook and Joseph Delaney point to an article by Yurii Aulchenko et al., who write: 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people. . . . In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. . . . The message is that the simple approach of predicting child’s height using a regression model given parents’ average height performs much better than the method they have based on combining 54 genes. They also find that, if you start with the prediction based on parents’ heigh
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Introduction: The Organisation for Economic Co-operation and Development reports that the following project from Krisztina Szucs and Mate Cziner has won their visualization challenge, “launched in September 2012 to solicit visualisations based on the OECD’s data-rich Education at a Glance report”: (The graph is interactive. Click on the above image and click again to see the full version.) From the press release: Entries from around the world focused on data related to the economic costs and return on investment in education . . . [The winning entry] takes a detailed look at public vs. private and men vs. women for selected countries . . . The judges were particularly impressed by the angled slope format of the visualisation, which encourages comparison between the upper-secondary and tertiary benefits of education. Szucs and Cziner were also lauded for their striking visual design, which draws users into exploring their piece [emphasis added]. I used boldface to highlight a p
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