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Predicting the Next Stock Market ‘Flash Crash’
Two engineers use fluid dynamics to study stock ‘flows’
Why Use Control Charts?
Can they help to do a better job?
Introducing the AIAG-VDA DFMEA
Understanding the changes
Who Invented the Pareto Chart?
Vilfredo Pareto, Joseph Juran, and Kaoru Ishikawa are all contenders
Control Charts and Capability Analysis
What you don’t know can confuse you
How to Convert ANSI/ASQ Z1.4 Sampling Plans Into Narrow-Limit Gauging Plans
Reduce the amount of attribute inspection that must be done
What Does MTBF Mean?
Four points of consideration
Process Capability: What It Is and How to Ensure It Helps, Part 5
Why is it important to keep the process stable?
Fighting Crime With Statistics
Taking advantage of ‘natural experiments,’ researchers analyze data to look at what works
Metrics: The Good, the Bad, and the Ugly
Part three: the ugly
Model-Based Definition: A Seven-Point Summary
Interoperability is key to avoiding the manual steps and hand-offs that Industry 4.0 hopes to eliminate
Reverse-Engineer Your Experiments
Be deliberate about the sample size you use
Why Did Shewhart Place a Premium on Time Order Sequence?
Avoid the unnecessary waste of being misled
Beyond the Hype: Machine Learning for Manufacturing Performance
As a tool, machine learning can accelerate insights in data for more efficient manufacturing and drive innovation
Getting Started with Process Validation Tools
Passing the three FDA stage goals
Metrics: The Good, the Bad, and the Ugly
Part two: the bad
d2: More Than Just a Control Chart Constant
We owe a debt of gratitude to Tippett and other pioneers who put ‘engineering’ into quality engineering.
How to Choose the Best Regression Model
There’s a Goldilocks balance with the number of predictors to include
Inside Quality Digest Live for Oct. 26, 2018
What kind of review would you give the Bates Motel?
Metrics: The Good, the Bad, and the Ugly
Part one: the good

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