Archive for the ‘Statistics’ Category

Making Box Plots on Ti-84

August 15, 2017

Quick graphs on the Ti-84 is a good way to  see how data is distributed.  The attached document gives step by step instructions along with an example to practice.

Making a Box Plot on the TI 84

Investment Decisions – Brains Vs. Computers; Which is Better?

June 13, 2017

The two points of view represented below are both well reasoned, conflicting, and cogent, but can they both be right?  The experts currently disagree, but maybe one day the question will be answered definitively.  For now, it makes for an interesting debate, and only time will tell who is right.  Today’s answer may not be tomorrow’s answer.

A key question is how much data does one need in order to make smart investment decisions.  Those who believe best decisions come from analyzing tons of data lean towards computer algorithms, while those who think smart evaluations don’t require looking at every piece of data lean toward human understanding and interpretation.

The term “quants,” refers to those quantitative analysts who crunch the number and are more reliant on computers and algorithms.

Why Brains Are More Reliable Than Machines – WSJ

Quants_ Best Strategy Is From the 17th Century – WSJ

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


Liberal Arts Become Mathematical

April 25, 2017

“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

“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.


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.

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 …

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?