Using a particular formula requires understanding the purpose, limitations, and assumptions involved. It also requires using the right formula.

Let's take a closer look at the concept of likelihood and it's role in an MCMC analysis.

Your science, engineering, and math formal training will serve you well as a reliability engineer, and that is not sufficient to be successful.

So how do you convert reliability data analysis into useful information for that decision?

Practical Use of Stress-Strength Models to Develop Specifications podcast episode with speaker Rob Schubert Warranty returns are a great place to start for setting targets for new products. But how do you translate that to specific numbers to design to? If you know the strength of a product and the return rate, you can develop […]

Let’s discuss the concept of failures along with a few other concepts universal to reliability engineering.

Some of you may have heard of ‘Bayesian analysis.’ You may think this is something fancy that only universities do.

This webinar will open your eyes to this important perspective. its’ so simple and has made many heroes in the data analysis world since Abraham Ward.

The selection hinges on knowing what is available, understanding the current situation, and available information, plus ...

The Reliability Value Map. It is a thing. podcast episode with speaker Chris Jackson If reliability engineering costs you money, then you are doing it wrong. But chances are that someone you work for looks at reliability engineering as a black hole of costs. This needs to stop! Reliability engineering is about creating value. Or […]