Study Find Millennials Willing to Pay for News to a Point
March 26, 2015
The article titled Millennials Say Keeping Up With the News Is Important To Them—But Good Luck Getting Them To Pay For It on NiemanLab explores the findings of a recent study by the Media Insight Project in partnership with the American Press Institute. A great deal of respondents get their news from Facebook, although the majority (88%) said it was only occasionally. Twitter and Reddit also made the list. Interestingly, millennials claimed multiple access methods to news categories across the board. The article states,
“The survey asked respondents how they accessed 24 different news topics, from national politics and government to style, beauty, and fashion. Facebook was either the number one or two source of information for 20 of the 24 topics, and in nine of those topics it was the only source cited by a majority of respondents. Search was the second most popular source of information, ranking first or second in 13 of the 24 news topics.”
In spite of the title of the article, most millennials in the study were willing to pay for at least one subscription, either digital or print. The article doesn’t mention the number of people involved in the study, but deeper interviews were held with 23 millennials, which is the basis for the assumptions about broader unwillingness to pay for the news, whether out of entitlement or a belief that access to free news is a fundamental pillar of democracy.
Chelsea Kerwin, March 26, 2015
Stephen E Arnold, Publisher of CyberOSINT at www.xenky.com
Free Statistics Text from Computer Science TA
February 11, 2014
The Probability and Statistics Cookbook from Matthias Vallentin is a free statistics text. The creator, Vallentin, is a doctoral student at UC Berkeley who works with Vern Paxson in his studies of computer science. While there Vallentin has worked as a teaching assistant in undergraduate computer security course. Vallentin also works for the International Computer Science Institute. His research in network intrusion and network forensics began in his undergraduate career in Germany. The “cookbook” is explained in the article,
“The cookbook aims to be language agnostic and factors out its textual elements into a separate dictionary. It is thus possible to translate the entire cookbook without needing to change the core LaTeX source and simply providing a new dictionary file. Please see the github repository for details. The current translation setup is heavily geared to Roman languages, as this was the easiest way to begin with. Feel free to make the necessary changes to the LaTeX source to relax this constraint.”
The overview provides screenshots that make it clear the cookbook is more interested in the mathematical crux rather than elaborate clarifications. The author is open to pull requests in order to lengthen the cookbook, but in the meanwhile the LaTeX source code can be found on github.
Chelsea Kerwin, February 11, 2014
Sponsored by ArnoldIT.com, developer of Augmentext
Release of 2nd Edition of the Elements of Statistical Learning
January 2, 2014
The release of the 2nd edition of The Elements of Statistical Learning is now available through the Stanford Statistics Department. The book was created in response to the massive leaps in computer and information technology in the last ten years by authors Trevor Hastie, Robert Tibshirani and Jerome Friedman. All are professors of statistics at Stanford, and the book does take a statistical approach but is concept-centered rather than focusing on mathematics.
The article summarizes the content:
“Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting–the first comprehensive treatment of this topic in any book.”
Sounds like another goody for the artificial intelligence fan. The book is aimed at data analysts or theory junkies and is absent of code. In a review, D.J. Hand calls it “a beautiful book” in both presentation and content. His only criticism that if the book were to be used for an undergrad or grad level course it should be supplemented with more practical approach utilizing S-PLUS or R language, if that can be called a criticism when paired with his praise of the authors and their work.
Chelsea Kerwin, January 02, 2014
Sponsored by ArnoldIT.com, developer of Augmentext
Arm Yourself with Statistics Knowledge
November 14, 2013
So many of the world’s big decisions are based on statistics, yet the discipline remains mysterious or misunderstood by many. Alex Reinhart, a statistics PhD student at Carnegie Mellon, aims to rectify that situation with “Statistics Done Wrong: the Woefully Complete Guide.” Hey, everyone needs more math. Well, except search engine optimization experts. They are all set.
The description reads:
“Statistics Done Wrong is a guide to the most popular statistical errors and slip-ups committed by scientists every day, in the lab and in peer-reviewed journals. Many of the errors are prevalent in vast swathes of the published literature, casting doubt on the findings of thousands of papers. Statistics Done Wrong assumes no prior knowledge of statistics, so you can read it before your first statistics course or after thirty years of scientific practice. Dive in: the whole guide is available online!”
Yep, go to the link above to access this helpful text—the clickable table of contents is right there on that page. Reinhart notes that this work is constantly being improved, and you can sign up for updates through a box on the right of the page. The guide is licensed under a Creative Commons Attribution 3.0 Unported License. Check it out, and be ahead of the crowd when statistics rears its unwieldy head.
Cynthia Murrell, November 14, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
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