One big challenge we all face is understanding what’s good and what’s bad for us. And it’s harder when published research studies conflict. And so thanks to Roger Peng for posting on his Facebook page an article that led me to this article by Emily Oster: Cellphones Do Not Give You Brain Cancer, from the good folks at the 538 blog. I think this article would make a great classroom discussion, particularly if, before showing your students the article, they themselves brainstormed several possible experimental designs and discussed strengths and weaknesses of the designs.
Many moons ago we wrote about a bit of a kludge to get data from a Fitbit (see here). Now it looks as though there is a much better way. Cory Nissen has written an R package to scrape Fitbit data and posted it on GitHub. He also wrote a blog post on his blog Stats and Things announcing the package and demonstrating its use. While I haven’t tried it yet, it looks pretty straight-forward and much easier than anything else i have seen to date.
Yup. You read it here first. The National Climatic Data Center has a nice overview that compares climate data to Punxsutawney Phil’s predictions. The data aren’t quite (yet) upload-ready, but some links on the page to more raw data might be entertaining.
Last Saturday the Mobilize project hosted a day-long professional development meeting for about 10 high school math teachers and 10 high school science teachers. As always, it was very impressive how dedicated the teachers were, but I was particularly impressed by their creativity as, again and again, they demonstrated that they were able to take our lessons and add dimension to them that I, at least, didn’t initially see.
One important component of Mobilize is to teach the teachers statistical reasoning.
I’m very excited/curious about tomorrow: I’m going to lead about 40 math and science teachers in a data-analysis activities, using one of the Model Eliciting Activities from the University of Minnesota Catalysts for Change Project. (One of our bloggers, Andy, was part of this project.) Specifically, we’re giving them the arrival-delay times for five different airlines into Chicago O’Hare. A random sample of 10 from each airline, and asking them to come up with rules for ranking the airlines from best to worst.
It is that time of year…time to review the previous year; make top 10 lists; and resolve to be a better person in 2015. I will tackle the first, but only of my reading habits. In 2014 I read 46 books for a grand total of 17,480 pages. (Note: I do not count academic books for work in this list, only books I read for recreation.) This is a yearly high, at least since I have been tracking this data on GoodReads (since late 2010).
Apparently our last blog post was in August. Dang. Where did five months go? Blog guilt would be killing me, but I swear it was just yesterday that Mine posted.
I will give a bit of review of some of the books that I read this semester related to statistics. Most recently, I finished Hands-On Matrix Algebra Using R: Active and Motivated Learning with Applications. This was a fairly readable book for those looking to understand a bit of matrix algebra.
