Infogroup - one of the leading providers of business listings - has an interesting post on their site about the problem of errors in local data. In this article they talk specifically about the error of business closure and the...

Editor's note: This post was written by Nick Carchedi, a Master's degree student in the Department of Biostatistics at Johns Hopkins. He is working with us to develop software for interactive learning of R and statistics. Inspired by the relative … Continue reading →

I use just-in-time teaching assignments in all my classes now. Vince helpfully sent along these instructions for setting these up on Google. See below. I think Jitts are just wonderful, and they’re so easy to set up, you should definitely be doing them in your classes too. I’ve had more difficulty with Peer Instruction (the […]The post Setting up Jitts online appeared first on Statistical Modeling, Causal Inference, and Social…

August 2013 (8/1) Blogging (flogging?) the SLP: Response to Reply- Xi’an Robert (8/5) At the JSM: 2013 International Year of Statistics (8/6) What did Nate Silver just say? Blogging the JSM (8/9) 11th bullet, multiple choice question, and last thoughts on the JSM (8/11) E.S. Pearson: “Ideas came into my head as I sat on […]

A short post to get back – for my nonlife insurance course – on the interpretation of the output of a regression when there is a categorical covariate. Consider the following dataset > db = read.table("http://freakonometrics.free.fr/db.txt",header=TRUE,sep=";") > attach(db) > tail(db) Y X1 X2 X3 995 1 4.801836 20.82947 A 996 1 9.867854 24.39920 C 997 1 5.390730 21.25119 D 998 1 6.556160 20.79811 D 999 1 4.710276 21.15373 A 1000…

The publishing platform I set up for my forecasting book has now been extended to cover more books and greater functionality. Check it out at www.otexts.org. So far, we have three complete books: Forecasting: principles and practice, by Rob J Hyndman and George Athanasopoulos Statistical foundations of machine learning, by Gianluca Bontempi and Souhaib Ben Taieb Modal logic of strict necessity and possbibility, by Evgeni Latinov and one book currently…

Jeff Leek just posted the discussions of his paper (with Leah Jager), “An estimate of the science-wise false discovery rate and application to the top medical literature,” along with some further comments of his own. Here are my original thoughts on an earlier version of their article. Keith O’Rourke and I expanded these thoughts into […]The post Difficulties in making inferences about scientific truth from distributions of published p-values appeared…

Thomson Reuters makes annual Nobel Prize forecasts in chemistry, physics, medicine and economics, based on citation counts from its Web of Science database (no surprise). Of course the exercise is largely a marketing tool for their database, but it's s...

From Nathan Yau. I love this stuff. It’s just wonderful, a great set of visualizations on a great topic. Offhand, the only suggestions I have are to scale the graphs or indicate in some way the trends in the total popularity of each name (as it is, I wonder if some of the variation is […]The post Great graphs of names appeared first on Statistical Modeling, Causal Inference, and Social…

That is what I will be speaking about when I give a keynote talk a the Predictive Analytics World conference on Monday, September 30th in Boston.For one thing, data has always been big. Big is a relative concept and data has always been big relative to...

Nothing brings out the silliness in smart people like Quantum Mechanics; a subject I always associate with … R. A. Fisher. I confess to liking Fisher more than Bayesians should. Unlike the forgettable p-value conjurers I’ve known in person,...

Sheldon Hackney, Penn's president 1981-1993, recently passed away. See the fine coverage in the Almanac and Daily Pennsylvanian.In my younger days as a Penn undergrad, Hackney took a lot of abuse. People felt that he didn't have much backbone. Exh...

Some of you may recall that a few months ago my colleague and I posted a paper to the ArXiv on estimating the rate of false discoveries in the scientific literature. The paper was picked up by the Tech Review and … Continue reading →

There was an interesting news item in Nature on code review. It describes a project by some folks at Mozilla to review the code (well, really just 200-line snippets) from 6 selected papers in computational biology. There are very brief quotes from Titus Brown and Roger Peng. I expect that the author of the item, […]

James Robins, Tyler VanderWeele, and Richard Gill write: Neyman introduced a formal mathematical theory of counterfactual causation that now has become standard language in many quantitative disciplines, but not in physics. We use results on causal interaction and interference between treatments (derived under the Neyman theory) to give a simple new proof of a well-known […]The post Classical probability does not apply to quantum systems (causal inference edition) appeared first…

Note: Act quickly. Looks like you can still get a free book courtesy of SAS from here. *** The New York Times features Acxiom, one of several data vendors that purportedly know a lot about you and me. Other key names in this sector include Experian and Equifax. What's new is that Acxiom will allow consumers to proactively "correct errors", or at least learn what is being bought and sold…

Diederik Stapel gives a Ted talk.Sometimes, reality truly is a parody of reality.The post Harmonic convergence appeared first on Statistical Modeling, Causal Inference, and Social Science.

For all the flak China receives about its greenhouse gas emissions, the average Chinese produces less than a third the amount of CO2 than his American counterpart. It just so happens that there are 1.3 billion Chinese, and 0.3 billion Americans, so China ends up producing more CO2. Carbon dioxide and other greenhouse gases, such […]

Attention conservation notice: Only of interest if you care a lot about computational statistics. For our first seminar of the year, we are very pleased to have a talk which will combine two themes close to the heart of the statistics department: ...

In which we meet the jackknife, by way of seeing how much error there is in our estimates from the last lab. Lab 4 (R) Introduction to Statistical Computing