Archive for the ‘Statistics Readings’ Category

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

April 25, 2017

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

 

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“At the current rate…” and other assumptions

March 30, 2017

In order to make any predictions in any area of study, one must make assumptions about the data, and what is likely to affect it going forward.  That is why statistical studies often make use of such statements as:

“At the current rate…”

“If things continue as in the past…”

“Based on what we know now…”

“It is reasonable to assume…”

“If history is a guide…”

“If things don’t change…”

But things do change, and that is what makes predicting the future so difficult, and so vulnerable to known and unknown biases.  While these caveats are unavoidable, and need to be stated, they require the consumer of the statistics to have a reasonable amount of skepticism and a large amount knowledge.

A quote in a The Great Race by Levi Tillemann states that “The available supply of gasoline, as is well know, is quite limited and it behooves the farseeing men of the motor car industry to look for likely substitutes.”  This quote is attributed to Thomas J. Fay, in 1905, in a magazine call Horseless Age.

Statistics on the Ti-84 and Ti-86 calculators

November 11, 2016

The following document shows how to enter and edit data on the Ti-84 and Ti-86 calculators and how to do some basic statistics with the data.  More documents on this topic to follow.  This is an ideal handout for teacher to stats students.

doing-statistics-on-the-ti

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

September 13, 2016

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/

Deficit v. Debt – knowing the diference

July 21, 2016
In simple terms, a budget deficit is the difference between what the federal government spends (called outlays) and what it takes in (called revenue or receipts). The national debt, also known as the public debt, is the result of the federal government borrowing money to cover years and years of budget deficits.

What’s the difference between the U.S. deficit and the national debt …

money.howstuffworks.com/difference-between-u-s-deficitnationaldebt-.htm

Early Bedtime May Fight Fat

July 19, 2016

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?

July 3, 2016

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?

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