Simply Statistics has moved to a new platform and so if you’ve been following our blog on Tumblr, you’ll have to update your links/RSS feeds to the new web site. Apologies for the disruption, but this move allows us to add some new features to the blog that will be rolled out soon. Thanks for following us!
Congratulations to Bradley Saul, the winner of the Simply Statistics Logo contest! We had some great entries which made it difficult to choose between them. You can see the new logo to the right of our home page or the full sized version here:
I made some slight modifications to Bradley’s original code (apologies!). The code for his original version is here:
Here’s the code:
#########################################################
# Project: Simply Statistics Logo Design
# Date: 10/17/12
# Version: 0.00001
# Author: Bradley Saul
# Built in R Version: 2.15.0
#########################################################
#Set Graphical parameters
par(mar=c(0, 0, 0, 0), pty='s', cex=3.5, pin=c(6,6))
#Note: I had to hard code the size, so that the text would scale
#on resizing the image. Maybe there is another way to get around font
#scaling issues - I couldn't figure it out.
make_logo <- function(color){
x1 <- seq(0,1,.001)
ncps <- seq(0,10,1)
shapes <- seq(5,15,1)
# Plot Beta distributions to make purty lines.
plot(x1, pbeta(x1, shape1=10, shape2=.1, ncp=0), type='l', xlab='', ylab='',
frame.plot=FALSE, axes=FALSE)
for(i in 1:length(ncps)){
lines(x1, pbeta(x1,shape1=.1, shape2=10, ncp=ncps[i]), col=color)
}
#Shade in area under curve.
coord.x <- c(0,x1,1)
coord.y <- c(0,pbeta(x1,shape1=.1,shape2=10, ncp=10),0)
polygon(coord.x, coord.y, col=color, border="white")
#Lazy way to get area between curves shaded, rather than just area under curve.
coord.y2 <- c(0,pbeta(x1,shape1=10,shape2=.1, ncp=0),0)
polygon(coord.x, coord.y2, col="white", border="white")
#Add text
text(.98,.4,'Simply', col="white", adj=1,family='HersheySerif')
text(.98,.25,'St\\*atistics', col="white", adj=1, family="HersheySerif")
}
Thanks to Bradley for the great logo and congratulations!
Last night this article by Chris Drummond of the Canadian National Research Council (Conseil national de recherches Canada) popped up in my Google Scholar alert. The title of the article, “Reproducible Research: a Dissenting Opinion” would seem to indicate that he disagrees with much that has been circulating out there about reproducible research.
Drummond singles out the Declaration published by a Yale Law School Roundtable on Data and Code Sharing (I was not part of the roundtable) as an example of the main arguments in favor of reproducibility and has four main objections. What I found interesting about his piece is that I think I more or less agree with all his objections and yet draw the exact opposite conclusion from him. In his abstract, he concludes that “I would also contend that the effort necessary to meet the [reproducible research] movement’s aims, and the general attitude it engenders, would not serve any of the research disciplines well.”
I the end, I see reproducibility as not necessarily a new concept, but really an adaptation of an old concept, that is describing materials and methods. The problem is that the standard format for publication—journal articles—has simply not caught up with the growing complexity of data analysis. And so we need to update the standards a bit.
I think the benefit of reproducibility is that if someone wants to question or challenge the findings of a study, they have the materials with which to do so. Providing people with the means to ask questions is how science moves forward.
This is part of the ongoing series of pro tips for graduate students, check out parts one, two and three for the original installments.
Two weeks ago I finished teaching my course Computing for Data Analysis through Coursera. Since then I’ve had some time to think about how it went, what I learned, and what I’d do differently.
First off, let me say that it was a lot of fun. Seeing thousands of people engaged in the material you’ve developed is an incredible experience and unlike any I’ve seen before. I initially had a number of fears about teaching this course, the primary one being that it would be a lot of work. Managing the needs of 50,000 students seemed like it would be a nightmare and making sure everything worked for every single person seemed impossible.
These fears were ultimately unfounded. The Coursera platform is quite nice and is well-designed to scale to very large MOOCs. Everything is run off of Amazon S3 and so scalability is not an issue (although Hurricanes are a different story!) and there are numerous tools provided to help with automatic grading. Quizzes were multiple choice for me, so that gave instant feedback to students, but there are options to grade via regular expressions. For programming assignments, grading was done via unit tests, so students would feed pre-selected inputs into their R functions and the output would be checked on the Coursera server. Again, this allowed for automatic instant feedback without any intervention on my part. Designing programming assignments that would be graded by unit tests was a bit restrictive for me, but I think that was mostly because I wasn’t that used to it. On my end, I had to learn about video editing and screen capture, which wasn’t too bad. I mostly used Camtasia for Mac (highly recommended) for the lecture videos and occasionally used Final Cut Pro X.
Coursera is working hard on their platform and so I imagine there will be many improvements in the near future (some of which were actually rolled out as the course was running). The system feels like it was designed and written by a bunch of Stanford CS grad students—and lo and behold it was! I think it’s a great platform for teaching computing, but I don’t know how well it’ll work for, say, Modern Poetry. But we’ll see, I guess.
Here is some of what I took away from this experience:
I’m grateful for all the students I had in this first offering of the course I thank them for putting up with my own learning process as I taught it. I’m hoping to offer this course again on Coursera but I’m not sure when that’ll be. If you missed the Coursera version of Computing for Data Analysis, I will be offering a version of this course through the blog very shortly. Please check here back for details.
TL: Last winter, then-director Robert Groves (now Provost at Georgetown University) asked if I would be interested in the possibility of becoming the next Associate Director of Research and Methodology (R&M) and Chief Scientist, succeeding Rod Little (Professor of Biostatistics at the University of Michigan) in these roles. I expressed interest and after several discussions with Bob and Rod, decided that if offered, I would accept. It was offered and I did accept.
As background, components of my research, especially Bayesian methods, is Census-relevant. Furthermore, during my time as a member of the National Academies Committee on National Statistics I served on the panel that recommended improvements in small area income and poverty estimates, chaired the panel that evaluated methods for allocating federal and state program funds by formula, and chaired a workshop on facilitating innovation in the Federal statistical system.
Rod and I noted that it’s interesting and possibly not coincidental that with my appointment the first two associate directors are both former chairs of Biostatistics departments. It is the case that R&D’s mission is quite similar to that of a Biostatistics department; methods and collaborative research, consultation and education. And, there are many statisticians at the Census Bureau who are not in the R&D directorship, a sociology quite similar to that in a School of Public Health or a Medical campus.
TL: I became energized by the opportunity for national service, and excited by the scientific, administrative, and sociological responsibilities and challenges. I’ll be engaged in hiring and staff development, and increasing the visibility of the bureau’s pre- and post-doctoral programs. The position will provide the impetus to take a deep dive into finite-population statistical approaches, and contribute to the evolving understanding of the strengths and weakness of design-based, model-based and hybrid approaches to inference. That I could remain a Hopkins employee by working via an Interagency Personnel Agreement, sealed the deal. I will start in January 2013 and serve through 2015, and will continue to participate in some Hopkins-based activities.
In addition to activities within the Census Bureau, I’ll be increasing connections among statisticians in other federal statistical agencies, have a role in relations with researchers funded through the NSF to conduct census-related research.
TL: The Census Bureau designs and conducts the decennial Census, the Current Population Survey, the American Community Survey, many, many other surveys for other Federal Statistical Agencies including the Bureau of Labor Statistics, and a quite extraordinary portfolio of others. Each identifies issues in design and analysis that merit attention, many entail “Big Data” and many require combining information from a variety of sources. I give a few examples, and encourage exploration of www.census.gov/research.
You can get a flavor of the types of research from the titles of the six current centers within R&M: The Center for Adaptive Design, The Center for Administrative Records Research and Acquisition, The Center for Disclosure Avoidance Research, The Center for Economic Studies, The Center for Statistical Research and Methodology and The Center for Survey Measurement. Projects include multi-mode survey approaches, stopping rules for household visits, methods of combining information from surveys and administrative records, provision of focused estimates while preserving identity protection, improved small area estimates of income and of limited english skills (used to trigger provision of election ballots in languages other than English), and continuing investigation of issues related to model-based and design-based inferences.
TL: Some are, some will be, some will never be. Small area estimation, hierarchical modeling with a Bayesian formalism, some aspects of adaptive design, some of combining evidence from a variety of sources, and general statistical modeling are in my power zone. I look forward to getting involved in these and contributing to other projects.
TL: Research innovations enable the bureau to produce more timely and accurate information at lower cost, improve validity (for example, new approaches have at least maintained respondent participation in surveys), enhancing the reputation of the the Census Bureau as a trusted source of information. Estimates developed by Census are used to allocate billions of dollars in school aid, and the provide key planning information for businesses and governments.
TL: The first step is to become aware of the wide variety of activities and their high impact. Visiting the Census website and those of other federal and state agencies, and the Committee on National Statistics (http://sites.nationalacademies.org/DBASSE/CNSTAT/) and the National Institute of Statistical Sciences (http://www.niss.org/) is a good start. Make contact with researchers at the JSM and other meetings and be on the lookout for pre- and post-doctoral positions at Census and other federal agencies.
The night of the presidential elections I wrote a post celebrating the victory of data over punditry. I was motivated by the personal attacks made against Nate Silver by pundits that do not understand Statistics. The post generated a little bit of (justified) nerdrage (see comment section). So here I clarify a couple of things not as a member of Nate Silver’s fan club (my mancrush started with PECOTA not fivethirtyeight) but as an applied statistician.
The main reason fivethrityeight predicts election results so well is mainly due to the idea of averaging polls. This idea was around way before fivethirtyeight started. In fact, it’s a version of meta-analysis which has been around for hundreds of years and is commonly used to improve results of clinical trials. This election cycle several groups, including Sam Wang (Princeton Election Consortium), Simon Jackman (pollster), and Drew Linzer (VOTAMATIC), predicted the election perfectly using this trick.
While each group adds their own set of bells and whistles, most of the gains come from the aggregation of polls and understanding the concept of a standard error. Note that while each individual poll may be a bit biased, historical data shows that these biases average out to 0. So by taking the average you obtain a close to unbiased estimate. Because there are so many pollsters, each one conducting several polls, you can also estimate the standard error of your estimate pretty well (empirically rather than theoretically). I include a plot below that provides evidence that bias is not an issue and that standard errors are well estimated. The dash line is at +/- 2 standard erros based on the average (across all states) standard error reported by fivethirtyeight. Note that the variability is smaller for the battleground states where more polls were conducted (this is consistent with state-specific standard error reported by fivethirtyeight).
Finally, there is the issue of the use of the word “probability”. Obviously one can correctly state that there is a 90% chance of observing event A and then have it not happen: Romney could have won and the aggregators still been “right”. Also frequentists complain when we talk about the probability of something that only will happen once? I actually don’t like getting into this philosophical discussion (Gelman has some thoughts worth reading) and I cut people who write for the masses some slack. If the aggregators consistently outperform the pundits in their predictions I have no problem with them using the word “probability” in their reports. I look forward to some of the post-election analysis of all this.
My favorite statistician did it again! Just like in 2008, he predicted the presidential election results almost perfectly. For those that don’t know, Nate Silver is the statistician that runs the fivethirtyeight blog. He combines data from hundreds of polls, uses historical data to weigh them appropriately and then uses a statistical model to run simulations and predict outcomes.
While the pundits were claiming the race was a “dead heat”, the day before the election Nate gave Obama a 90% chance of winning. Several pundits attacked Nate (some attacks were personal) for his predictions and demonstrated their ignorance of Statistics. Jeff wrote a nice post on this. The plot below demonstrates how great Nate’s prediction was. Note that each of the 45 states (including DC) for which he predicted a 90% probability or higher of winning for candidate A, candidate A won. For the other 6 states the range of percentages was 48-52%. If Florida goes for Obama he will have predicted every single state correctly.
Update: Congratulations also to Sam Wang (Princeton Election Consortium) and Simon Jackman (pollster) that also called the election perfectly. And thanks to the pollsters that provided the unbiased (on average) data used by all these folks. Data analysts won “experts” lost.
Update 2: New plot with data from here. Old graph here.
