Tuesday, November 18, 2014
I came across this nice online portal on introductory statistics: OpenIntro Stats. It has a textbook, labs on R or SAS, teachers resources (slides, learning objectives), videos, and much more. Everything is laid out in a nice accessible platform, including LaTex source files. It is a nice resource for learning intro stat, R/R studio and LaTex.
Saturday, November 15, 2014
Tom Ireland wrote
The average American's alarm clock goes off at about 7am to get to work just in time for a 9 to 5 job, only to drive back home, have dinner at 6pm and watch a bit of TV before bed at 10:30pm. But how typical is this routine, really?
After reading your blog, I thought you might be interested to know that at peak times, over 1/3 of Americans are watching TV. You can find this and more fascinating information on our visualization, Busy States of America. With new data as yet unpublished from the Bureau of Labor Statistics, you can see how many Americans are doing common, everyday activities right now. View the real-time visualization here: http://www.retale.com/info/
I hope you find our display of the typical American's day interesting and share it with your readers. Let me know if you have any questions.I think the visualization is pretty nice.
Thursday, November 06, 2014
So, Google is scanning all the books ever published and is making good progress. An interesting project span off from all text scanned is the Google books NGram viewer project that curated all the words/phrases' traces in the publishing history. The raw data is also available for anyone interested in playing with a big set of interesting data. Here is my take on "Statistical learning" vs. "Statistical modeling" vs "predictive analytics".
Friday, April 04, 2014
In an online interview, Professor Chris Wiggins said
For years, this has been my understanding of the distinction between Statistical Learning and Machine Learning but it means more when it came from a machine learning expert. Statisticians are indeed obsessed with finding explanations of the variations we observe in a data set, through modeling, visualization, simulation-based model checking and etc. We are interested in which few features can explain away a large proportion of variation in the outcome of interest, and through what forms of mechanisms. These features are mostly likely related to the scientific mechanisms behind the data. Understanding such mechanisms will be of importance to any data-driven decision making process such as policy making, intervention, and e.g., personalized medicine. We do not believe that our models are correct but we do think they are often very useful. For example, we use model-based framework to understand what assumptions were made for specific machine learning algorithms. Such understanding is then used to evaluate these algorithms under the situations where the assumptions do not hold and to suggest extensions, modifications to make them work better. Modeling is not our goal. Understanding is. Machines do not need to understand the work they are doing but humans do. For the curious minds, Statistics can help.