Degree Days explained

“Degree days” is a measure of energy needed to cool or heat building spaces to a comfortable temperature.  It is useful for planning energy costs for different locations, and is far superior than using average temperatures in which highs and lows tend to cancel eachother.  The link below to the Numbers column of the Wall Street Journal is the best explanation I have seen on the subject.  The second link is to a U. S. government website which is also very helpful.

How Do Energy Companies Measure the Tem…re? Not in Fahrenheit or Celsius – WSJ

https://www.eia.gov/energyexplained/index.php?page=about_degree_days

 

VC Firms Use Big Data to Seek Out the Next Big Thing

Venture Capital firms, that is, firms that invest in funding start-ups, early stage  companies, and companies with good growth potential, are always on the hunt for the next great opportunity.  The article below talks about the trend away from people who are expert in spotting such opportunities, and towards computer based analytics which is believed to be faster and better at finding the “next big thing.”

Venture-Capital Firms Use Big Data to Seek Out the Next Big Thing – WSJ

 

Liberal Arts Become Mathematical

“Adding Math To Save Humanities” is the title of a sidebar article in the Wall Street Journal, April 25, 2017, about liberal arts colleges trying to add more mathematical contents to traditional liberal arts courses to better prepare their graduates for the work world.  Along with the Big Data revolution comes the need for employees in many diverse fields to be able to analyze data and to “rigorously and effectively” use data to answer questions.  “Emory University in Atlanta has created a degree that marries traditionally qualitative disciplines such as anthropology and English with math and statistics.”  This shift is in part to due students enrolling in liberal arts programs in smaller numbers.  Click below for the full article.

saving liberal arts

Statistically Counting Lost Luggage as Not Lost

“An innovation aiming to streamline how air passengers reconnect with their lost luggage comes with a major asterisk: airline would no longer count the luggage as lost.”  Using text messaging, airlines could advise you that your bags did not make your flight, and you can give them instructions via text where they should send the lost bags.

DOT statisticians will not count those bags as lost, and airlines can maintain good stats while perhaps not performing so well.  You say, “They’re not lost, because we know where they are.”  I say, “They’re lost because they are not where they belong.”

For the full article in the Wall Street Journal, click here:

Whoever said statistics was an exact science?

counting Lost Baggage

Economics Should be Required in High School

Economics is so basic and essential for good citizenship and good personal decisions, that it is a shame high school students can graduate without even being exposed to the subject.  An article by high schooler Benjamin Auslin from Montgomery Blair High School in Silver Spring, Maryland, makes a strong argument that understanding supply and demand, the time value of money, return on investment and the basics of personal finance are skills that would well serve the graduates and society.

economics-shouldnt-be-an-elective-wsj

When “Big Data” gets it wrong

Big Data could provide a big advantage to investors, but when the data is wrong or misinterpreted, it can be catastrophic.

“Credit card data sold to investors is making shares of retailers behave strangely, especially when the data gets things wrong.”  So begins an article in the Wall Street Journal ‘Big Data Adds Up to Trading Distortions.’  The first example is about Tailored Brands (owner of Men’s Warehouse and Joseph A. Bank) stock that shot up nearly 40% in one day when investors realized that the data they were basing their decisions on was inaccurate.

For the full article:  how-credit-card-data-might-be-distorting-retail-stocks-wsj

The Nobel Prize in Physics Awarded

The following two paragraphs are quotes from a fascinating article about how 3 physicists “playing” with math stumbled upon a remarkable and practical finding.  The link to the whole article follows the quotes.

“A trio of British-born researchers working in the U.S. won the Nobel Prize in physics for what one of them called a curious mathematical “toy” that to his surprise revolutionized the study of exotic matter suitable for quantum computers, new superconductors, and advanced designer materials.”

” “At the time, I thought it was of scientific interest and mathematical interest, but I didn’t think it would ever find a particular realization,” said Princeton University physicist F. Duncan M. Haldane, who shared the award for theoretical experiments he had conducted in the 1980s. “I basically stumbled on this playing with mathematics.” ”

For the whole article go to:

nobel-prize-in-physics-awarded-to-david-n-haldane-and-michael-kosterlitz-wsj

20% error rate found in study of published genetics research papers

In a recent study 20% of genetics research papers using Microsoft Excel have been found to have data errors due to improper data entry.  It turns out that gene names such as SEPT2 and MARCH1 (these are actual gene name abbreviations used by scientists) get converted to dates by Excel and then result in rejected data.  The problem is resolvable if the scientists would make sure the data cells were formatted as “Text,” prior to entering the data.

For the full article in the Washington Post click on the link below.

https://www.washingtonpost.com/news/wonk/wp/2016/08/26/an-alarming-number-of-scientific-papers-contain-excel-errors/

Early Bedtime May Fight Fat

Scientists studying sleep time of preschoolers and obesity in teenages have observed some interesting correlations, but they are not conclusively cause and effect.  As reported in the New York Times, July 19, 2016.

Early Bedtime May Fight Fat NYT 7_19_16

‘Residual Seasonality,’ Fixing the Data or Fixing the Results?

Adjusting the data? How much adjustment is good?  How do you know when the adjustments introduce distortions?   When are you fixing the data? And when are you fixing the results?  To know the answers to those question take a lot of experience and objectivity.

In an article in The Wall Street Journal ( July 1, 2016), by Jo Craven McGinty, titled, “Seasonal Fluctuations Vex Statisticians in Quest to Capture Economy’s Growth, Stripping out normal variation challenges government as it seeks to give accurate GDP figure,” Ms. McGinty addresses a current example of how data is handled.

” “We expect a certain amount of randomness in any economic data,” said Brent Moulton, who oversees GDP and other national economic statistics for the Bureau of Economic Analysis.”

“When adjusted numbers continue to exhibit the influences of seasonal effects, statisticians refer to it as residual seasonality.” That is something new.

” “Small patterns of seasonality at the individual granular level, which don’t appear to be that significant, can add up over time, over quarter and over various components to substantial residual seasonality,” said Mr. Rudebusch, who found that adjusting the GDP a second time seems to erase the effect.”

As I said, are we fixing the data, or are we fixing the results?