On 09/20/2019 08:47 AM, Cindy Hamilton wrote:
On Friday, September 20, 2019 at 10:22:06 AM UTC-4, rbowman wrote:
On 09/20/2019 04:32 AM, Cindy Hamilton wrote:
I recall my first job. I worked for a man studying renal adenocarcinoma
in frogs as a model for human cancer (unless you'd prefer to induce cancer
in humans for research). I typed endless statistical analyses of the
data.
When I was in college I worked summers for the state Department of
Education. They crunched a lot of numbers trying to formulate state wide
high school exams that would result in a pleasing bell curve. Sometimes
they succeeded. The crunching part was quite literal since they used
Friden square root calculators;

https://www.youtube.com/watch?v=fnmj9EzKgtg

That model isn't the square root variety but is very similar.

My memory of my statistics course was the chief use was determining how
many blivits you needed to test to insure only 5% of the blivits you
sold were junk. The 5% number varied depending on the cost of replacing
defective units and ****ing off customers.
That certainly was a very specific statistics course. Or your memory
of it was very specific.


My memory of it was that it was an 8 o'clock class taught by the aptly
named professor Dis Maly. I'm not making that up.

http://www.rpi.edu/magazine/winter20...lastthing.html

"I arrived from a small town but with vivid images of universities and
the academic life: musty buildings, clever professors, and studies of
wondrous things. My very first class filled the bill: Prof. Dis Maly was
slight of build, covered with chalk dust, and wreathed in pipe smoke.
Pleasant but somewhat distracted, he delivered lectures that were droll
but laced with wit. His eyes sparkled and danced when he spoke of
Calculus: The Mathematics of Change."

I don't remember his eyes ever sparking. Or the wit. Mrs. Maly also
taught mathematics and was very good; Dis was a cure for insomnia.

It was an engineering school hence the emphasis on engineering statistics:

https://en.wikipedia.org/wiki/Engineering_statistics

As such, most of the examples were drawn from QC applications. You do
know that the person behind the nom de plume Student was William Gossett
who worked for the Guinness Brewery. His interest was how many samples
of barley you have to pull to ensure the quality.

Most of my brushes with statistics in later life were just that, QC
applications. There is a certain degree of cynicism in mass production.
You know a percentage of the product will be defective. How much
sampling do you have to do to keep it in reasonable limits?

Another aspect is components like capacitors and resistors. Nobody sets
out to make resistors with a 20% tolerance. 5% would be nice but ****
happens. How much sampling do you do before deciding you just made a
batch of 20% components?

The 80386 chips had a similar quirk. The good ones could do 32 bit
multiplication. The bad ones were marked for 16 bit only and sold since
a lot of applications didn't require 32 bit operations at the time.
Again, how do you sort the sheep from the goats with a reasonable chance
of success?

Yes, I know there are many other uses for statistics but I also know
there have been many houses of cards built on statistics. I'm just
talking about honest procedural errors, not cooking the books to get the
answer you want.