QC and QA
Quality is one of the buzz words of this era. It wasn't yet when I was active in it (early 90's). Production
considered us, QC and QA guys, as unnecessary cost to the company. They wanted to produce. Spit out products
and if a customer complained, just give them a box for free.
History has proven those guys wrong. Ams. paradoxically, many of these former critics are now selling themselves as quality gurus with a more than modest price tag. Lucky for me, they just learned a few tricks and as long as the other guys don't really understand the trickery, they're home free.
But not for long. I am going to spill some beans here. THEIR beans, to be precise. To educate the world and give tools to those who are knowledgeable. It's best to have a running Linux system around though.
Below are the topics covered in this section. The topics are mentioned in chronological order: newest edition is further down the page. I think that makes easier reading.
When taking samples, the gaussian (or 'normal') distribution is one of the most encountered. This program calculates the bell curve, if you supply average, standard deviation and stepsize.
The Poisson distribution is related to the Gauss distribution yet is more important for less discrete data sets. The gauss deals with continuous data sets. Poisson deals with stepped datasets (eg the sides of a die). This program produces a dataset that can be plotted with gnuplot, based on an average and a stepsize.
The binomial distribtion deals with non destructive sample taking. What are the odds when I perform n experiments with p variables. This program takes two arguments (average and width) and produces a dataset that can be plotted with gnuplot.
Random numbers are a great asset when simulating sampling experiments. This generator is easy to construct yet produces a good dataset.
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