[continued from previous message]

And enhancing productivity is also related to my following powerful
product that i have designed and implemented(that can also be applied to
organizations):


https://sites.google.com/site/scalable68/universal-scalability-law-for-delphi-and-freepascal


Please read the following about Applying the Universal Scalability Law
to organisations:


https://blog.acolyer.org/2015/04/29/applying-the-universal-scalability-law-to-organisations/


Yet more political philosophy about quality control and quality..

So first you have to define quality(read below about it) and second
you have to construct quality and third you have to control quality.

So, I have just read the following about the Central Limit Theorem
(I understood it), i invite you to read it carefully:

https://www.probabilitycourse.com/chapter7/7_1_2_central_limit_theorem.php


So as you are noticing this Central Limit Theorem is so important
for quality control, read the following to notice it(I also understood Statistical Process Control (SPC)):

An Introduction to Statistical Process Control (SPC)

https://www.engineering.com/AdvancedManufacturing/ArticleID/19494/An-Introduction-to-Statistical-Process-Control-SPC.aspx

Also PERT networks are referred to by some researchers as "probabilistic activity networks" (PAN) because the duration of some or all of the arcs
are independent random variables with known probability distribution
functions, and have finite ranges. So PERT uses the central limit
theorem (CLT) to find the expected project duration.

So, i have designed and implemented my PERT++ that that is important for quality, please read about it and download it from my website here:

https://sites.google.com/site/scalable68/pert-an-enhanced-edition-of-the-program-or-project-evaluation-and-review-technique-that-includes-statistical-pert-in-delphi-and-freepascal

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So I have provided you in my PERT++ with the following functions:


function NormalDistA (const Mean, StdDev, AVal, BVal: Extended): Single;

function NormalDistP (const Mean, StdDev, AVal: Extended): Single;

function InvNormalDist(const Mean, StdDev, PVal: Extended; const Less: Boolean): Extended;

For NormalDistA() or NormalDistP(), you pass the best estimate of
completion time to Mean, and you pass the critical path standard
deviation to StdDev, and you will get the probability of the value Aval
or the probability between the values of Aval and Bval.

For InvNormalDist(), you pass the best estimate of completion time to
Mean, and you pass the critical path standard deviation to StdDev, and
you will get the length of the critical path of the probability PVal,
and when Less is TRUE, you will obtain a cumulative distribution.


So as you are noticing from my above thoughts that since PERT networks
are referred to by some researchers as "probabilistic activity networks"
(PAN) b