Four points of consideration
Why is it important to keep the process stable?
Taking advantage of ‘natural experiments,’ researchers analyze data to look at what works
Part three: the ugly
Interoperability is key to avoiding the manual steps and hand-offs that Industry 4.0 hopes to eliminate
Be deliberate about the sample size you use
Avoid the unnecessary waste of being misled
As a tool, machine learning can accelerate insights in data for more efficient manufacturing and drive innovation
Passing the three FDA stage goals
Part two: the bad
We owe a debt of gratitude to Tippett and other pioneers who put ‘engineering’ into quality engineering.
There’s a Goldilocks balance with the number of predictors to include
What kind of review would you give the Bates Motel?
Part one: the good
All about data—and genomes
Procedure doesn’t allow nonconforming units in the sample, has superior detection capabilities
In the politically correct world of PPM defective, MIL-STD-105 is a misunderstood and misapplied specification
We keep trying to find the needle in a haystack
It's all about manufacturing.
What happens when we cannot write models for the data?
