andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1927 knowledge-graph by maker-knowledge-mining
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Introduction: Business statistician Kaiser Fung just came out with another book, this one full of stories about how organizations use data: 1. Why do law school deans send each other junk mail? 2. Can a new statistic make us less fat? 3. How can sellouts ruin a business? 4. Will personalizing deals save Groupon? 5. Why do marketers send you mixed messages? 6. Are they new jobs if no one can apply? 7. How much did you pay for the eggs? 8. Are you a better coach or manager? Unlike most books of this sort, there’s no hero: These are not stories about a fabulous businessman who made millions of dollars by following his dream and taking the customer seriously, nor are they Gladwellian sagas of brilliant scientists, nor are they auto-Gladwellian tales of the Ariely variety. In some ways, the stories in Fung’s book have the form of opened-up business reporting, in which you get to see the statistical models underlying various assumptions and conclusions. In that sense, this b
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1 Business statistician Kaiser Fung just came out with another book, this one full of stories about how organizations use data: 1. [sent-1, score-0.464]
2 Why do law school deans send each other junk mail? [sent-2, score-0.35]
3 In some ways, the stories in Fung’s book have the form of opened-up business reporting, in which you get to see the statistical models underlying various assumptions and conclusions. [sent-18, score-0.766]
4 In that sense, this book could be a good way for people to learn some fundamental statistical concepts. [sent-19, score-0.205]
5 The next step will be to fold such ideas into statistics (or “data science”) classes. [sent-20, score-0.137]
6 As it is, our examples seem to oscillate between happytalk success stories (with a pretty regression model or a comfortably statistically-significant conclusion) or stories of selection bias, with no good integration into the how-to-do-it material. [sent-21, score-1.456]
7 I tried to put this all together the last time I taught intro stat, but it didn’t work so well. [sent-22, score-0.188]
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Introduction: Business statistician Kaiser Fung just came out with another book, this one full of stories about how organizations use data: 1. Why do law school deans send each other junk mail? 2. Can a new statistic make us less fat? 3. How can sellouts ruin a business? 4. Will personalizing deals save Groupon? 5. Why do marketers send you mixed messages? 6. Are they new jobs if no one can apply? 7. How much did you pay for the eggs? 8. Are you a better coach or manager? Unlike most books of this sort, there’s no hero: These are not stories about a fabulous businessman who made millions of dollars by following his dream and taking the customer seriously, nor are they Gladwellian sagas of brilliant scientists, nor are they auto-Gladwellian tales of the Ariely variety. In some ways, the stories in Fung’s book have the form of opened-up business reporting, in which you get to see the statistical models underlying various assumptions and conclusions. In that sense, this b
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Introduction: No joke. See here (from Kaiser Fung). At the Statistics Forum.
3 0.16011733 1318 andrew gelman stats-2012-05-13-Stolen jokes
Introduction: Fun stories here (from Kliph Nesteroff, link from Mark Palko).
Introduction: In “Story: A Definition,” visual analysis researcher Robert Kosara writes : A story ties facts together. There is a reason why this particular collection of facts is in this story, and the story gives you that reason. provides a narrative path through those facts. In other words, it guides the viewer/reader through the world, rather than just throwing them in there. presents a particular interpretation of those facts. A story is always a particular path through a world, so it favors one way of seeing things over all others. The relevance of these ideas to statistical graphics is apparent. From a completely different direction, in “When do stories work? Evidence and illustration in the social sciences,” Thomas Basbøll and I write : Storytelling has long been recognized as central to human cognition and communication. Here we explore a more active role of stories in social science research, not merely to illustrate concepts but also to develop new ideas and evalu
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Introduction: Answer here (courtesy of Kaiser Fung).
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Introduction: Business statistician Kaiser Fung just came out with another book, this one full of stories about how organizations use data: 1. Why do law school deans send each other junk mail? 2. Can a new statistic make us less fat? 3. How can sellouts ruin a business? 4. Will personalizing deals save Groupon? 5. Why do marketers send you mixed messages? 6. Are they new jobs if no one can apply? 7. How much did you pay for the eggs? 8. Are you a better coach or manager? Unlike most books of this sort, there’s no hero: These are not stories about a fabulous businessman who made millions of dollars by following his dream and taking the customer seriously, nor are they Gladwellian sagas of brilliant scientists, nor are they auto-Gladwellian tales of the Ariely variety. In some ways, the stories in Fung’s book have the form of opened-up business reporting, in which you get to see the statistical models underlying various assumptions and conclusions. In that sense, this b
2 0.72661185 1634 andrew gelman stats-2012-12-21-Two reviews of Nate Silver’s new book, from Kaiser Fung and Cathy O’Neil
Introduction: People keep asking me what I think of Nate’s book, and I keep replying that, as a blogger, I’m spoiled. I’m so used to getting books for free that I wouldn’t go out and buy a book just for the purpose of reviewing it. (That reminds me that I should post reviews of some of those books I’ve received in the mail over the past few months.) I have, however, encountered a couple of reviews of The Signal and the Noise so I thought I’d pass them on to you. Both these reviews are by statisticians / data scientists who work here in NYC in the non-academic “real world” so in that sense they are perhaps better situated than me to review the book (also, they have not collaborated with Nate so they have no conflict of interest). Kaiser Fung gives a positive review : It is in the subtitle—“why so many predictions fail – but some don’t”—that one learns the core philosophy of Silver: he is most concerned with the honest evaluation of the performance of predictive models. The failure to look
3 0.72024524 1001 andrew gelman stats-2011-11-10-Three hours in the life of a statistician
Introduction: Kaiser Fung tells what it’s really like . Here’s a sample: As soon as I [Kaiser] put the substring-concatenate expression together with two lines of code that generate data tables, it choked. Sorta like Dashiell Hammett without the broads and the heaters. And here’s another take, from a slightly different perspective.
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Introduction: According to Kaiser : EdLab at Teachers’ College does a lot of interesting things, like creating technologies for the classroom and libraries, blogging, and analyzing data sets in the education sector. They’re 2 blocks from my office and I’ve never heard of them! And I even work with people at Teachers College. Columbia’s a big place. P.S. Kaiser also makes an excellent point: The most intriguing and unexpected question was: to do well in this business, do you have to read a lot? This is where I stumbled into a spaghetti carbonara analogy while mixing metaphors with the gray flannel, with which I have already been associated. Basically, statistics is not pure mathematics, there is not one correct way of doing things, there are many different methodologies, like there are hundreds of recipes for making carbonara. What statisticians do is to try many different recipes (methods), and based on tasting the food (evaluating the outcomes), we determine which recipe to use. Be
5 0.68805617 543 andrew gelman stats-2011-01-28-NYT shills for personal DNA tests
Introduction: Kaiser nails it . The offending article , by John Tierney, somehow ended up in the Science section rather than the Opinion section. As an opinion piece (or, for that matter, a blog), Tierney’s article would be nothing special. But I agree with Kaiser that it doesn’t work as a newspaper article. As Kaiser notes, this story involves a bunch of statistical and empirical claims that are not well resolved by P.R. and rhetoric.
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1 0.94613039 1343 andrew gelman stats-2012-05-25-And now, here’s something we hope you’ll really like
Introduction: This came in the email: Postdoctoral Researcher (3 years) in State-Space Modeling of Animal Movement and Population Dynamics in Universities of Turku and Helsinki, Finland We seek for a statistician/mathematician with experience in ecological modeling or an ecologist with strong quantitative training to join an interdisciplinary research team focusing on dispersal and dynamics of the Siberian flying squirrel (Pteromys volans). The Postdoctoral Researcher will develop modeling approaches (from individual based models to population level models) to assess the dispersal and population dynamics of the flying squirrel. A key challenge will be the integration of different kinds of data (census data, telemetry data, mark-recapture data, life-history data, and data on environmental covariates such as forest structure) into the modeling framework using Bayesian State-Space models or other such approaches. The project will be supervised by Dr. Vesa Selonen (a flying squirrel specialist;
2 0.94443655 443 andrew gelman stats-2010-12-02-Automating my graphics advice
Introduction: After seeing this graph : I have the following message for Sharad: Rotate the graph 90 degrees so you can see the words. Also you can ditch the lines. Then what you have is a dotplot, following the principles of Cleveland (1985). You can lay out a few on one page to see some interactions with demographics. The real challenge here . . . . . . is to automate this sort of advice. Or maybe we just need a really nice dotplot() function and enough examples, and people will start doing it? P.S. Often a lineplot is better. See here for a discussion of another Sharad example.
3 0.93022639 844 andrew gelman stats-2011-08-07-Update on the new Handbook of MCMC
Introduction: It’s edited by Steve Brooks, Galin Jones, Xiao-Li Meng, and myself. Here’s the information and some sample chapters (including my own chapter with Ken Shirley on inference and monitoring convergence and Radford’s instant classic on Hamiltonian Monte Carlo). Sorry about the $100 price tag–nobody asked me about that! But if you’re doing these computations as part of your work, I think the book will be well worth it.
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Introduction: Business statistician Kaiser Fung just came out with another book, this one full of stories about how organizations use data: 1. Why do law school deans send each other junk mail? 2. Can a new statistic make us less fat? 3. How can sellouts ruin a business? 4. Will personalizing deals save Groupon? 5. Why do marketers send you mixed messages? 6. Are they new jobs if no one can apply? 7. How much did you pay for the eggs? 8. Are you a better coach or manager? Unlike most books of this sort, there’s no hero: These are not stories about a fabulous businessman who made millions of dollars by following his dream and taking the customer seriously, nor are they Gladwellian sagas of brilliant scientists, nor are they auto-Gladwellian tales of the Ariely variety. In some ways, the stories in Fung’s book have the form of opened-up business reporting, in which you get to see the statistical models underlying various assumptions and conclusions. In that sense, this b
5 0.90755606 1622 andrew gelman stats-2012-12-14-Can gambling addicts be identified in gambling venues?
Introduction: Mark Griffiths, a psychologist who apparently is Europe’s only Professor of Gambling Studies, writes: You made the comment about how difficult it is to spot problem gamblers. I and a couple of colleagues [Paul Delfabbro and Daniel Kingjust] published this review of all the research done on spotting problem gamblers in online and offline gaming venues (attached) that I covered in one of my recent blogs .
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