Archive for the ‘statistics in news’ Category

How Changing the Expectations Changes the Outcome

June 19, 2016

Self fulfilling prophesies can make someone his own worst enemy.  Expecting to fail can become the formula for failure.  The question is how to get students who have low expectations of themselves, and who doubt their ability to succeed, to reset their mind-set.  Some new research sheds some valuable light on this question.

A Small Fix in Mind-Set Can Keep Students in School – WSJ

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The Truth Behind the ‘Summer Rally’

June 6, 2016

Statistics students should read The Wall Street Journal article about the myth of the summer stock market rally (WSJ, June 6, 2016).  By examining the data the author shows that the summer historically had less rallies than other times of the year, and yet the name, the concept and the belief persists.  The author suggests that the name might go back to the Depression when in the summer of 1932 the Dow Jones Industrial Average gained 76.5% from the low close of June to the high close of August.

Sometimes we see what we expect to see, and no one is immune from the perception bias, but being aware of the tendency makes you better prepared to deal with it in an intelligent manner.

For the full article, click here: The Market’s Summer-Rally Myth – WSJ

MBAs Need Data Comprehension and Communication With Geeks

May 5, 2016

The Wharton School has come to realize that in a data filled world, understanding how data can drive good decision making is key to tomorrow’s (and today’s) executives.  Case studies, which have long dominated MBA education, is no match for a deep understanding of analytics.  Being able to communicate with the data handlers and being knowledgeable about what one can expect from them in now a key skill.  The Wharton School of the University of Pennsylvania, in now pushing analytics in their MBA programs.  See the attached article.

Wharton M.B.A

More on Correlation and Causation

March 19, 2016

If the graph of the per capita rate of  divorces in Maine and margarine sales track each other, are there grounds to say one causes the other?  Over a 10 year period there is a strong correlation between the two sets of data.  How do you deal with that? and what is the proper language to describe the various possible scenarios?

Explainer_ Correlation, causation, coincidence and more _ Science News for Students

Using Big Data to Predict Worker Illness and Pregnancies

February 29, 2016

Not a typographical error!  Companies who hire outside consultants are able to get data about their workforce that borders on a serious intrusion of privacy.  Click below for the whole WSJ article, but just a few quotes might give the sense of what I am talking about.

Bosses Tap Outside Firms to Predict Which Workers Might Get Sick – WSJ

“Trying to stem rising health-care costs, some companies, including retailer Wal-Mart Stores Inc., are paying firms like Castlight Healthcare Inc. to collect and crunch employee data to identify, for example, which workers are at risk for diabetes, and target them with personalized messages nudging them toward a doctor or services such as weight-loss programs.”

“To determine which employees might soon get pregnant, Castlight recently launched a new product that scans insurance claims to find women who have stopped filling birth-control prescriptions, as well as women who have made fertility-related searches on Castlight’s health app.”

“Privacy advocates have raised concerns about such practices. Employees generally have a choice in whether to participate in the programs. The services are new enough that relatively few workers are aware of them.”

“Federal health-privacy laws generally bar employers from viewing workers’ personal health information, though self-insured employers have more leeway, says Careen Martin, a health-care and cybersecurity lawyer at Nilan Johnson Lewis PA. Instead, employers contract with wellness firms who have access to workers’ health data.”

Zack Greinke: Baseball’s Big-Data Pitcher

October 10, 2015

Greinke might be the perfect pitcher for baseball’s era of big data. There are other pitchers who study hitters as extensively, combing through statistics and video clips for revelations of their vulnerabilities. But few of them can match that understanding with the precision Greinke uses to turn game theory into results.

For the full story from the Wall Street Journal, Oct. 10-11, 2015:

Zack Greinke_ Baseball’s Most Obsessively Prepared Pitcher – WSJ