Let's explore where the data comes from and how to prepare for analysis. Plus, let's discuss some ways to look at your data initially.

Most humans can be quite technically minded. And sometimes we aren't. Sometimes, we expect every drug or medical procedure to be checked, approved, validated, and tested on other people (et cetera) before subjecting ourselves to whatever our doctor suggests. At other times, we spend lots of money at casinos (note that I said spend' and not invest' or win'). When it comes to reliability, we can sometimes be too technically minded. A reliability number' might not exist until our product has undergone exhaustive testing. Which can often be two years too late to do anything about if it turns out it doesn't meet all our reliability dreams. So what can we do? Well why spend money trying to generate information (through lengthy tests) when you can use the information stored in everyone's brain? The most common answer (even if we don't want to admit it) is that this sounds like guesswork. And guesswork can sound unprofessional. Or it’s just wrong. But there are ways you can suck out information from a group of experts in a quantifiable and remarkably accurate way. Want to learn more? This webinar introduces you to some of the concepts that might interest you.
This Accendo Reliability webinar was originally broadcast on 24 May 2022.
What is Your Approach episode
What Could Go Wrong episode
Strength-Stress with Limited Information episode
Incomplete Data episode
Is Testing The Only Way to Confirm Reliability episode
Let's explore where the data comes from and how to prepare for analysis. Plus, let's discuss some ways to look at your data initially.
For repairable items, the mean cumulative function and associated plots provide you with an estimate of the effectiveness of your repairs.
We will discuss the pros and cons of various sources. Plus, let's examine a few ways to use simulations or models.
The Weibull distribution is a versatile tool to analyze time to failure data. Like any tool, it could be wielded well or not so well.
The design is done, the assembly process is working, now we can focus on answering the question: is the product hitting reliability targets?
Data is only as useful as the information you derive. So would you like to take your Weibull probability plotting skills to the next level?
Minitab itself has many reliability functions available; this presentation covers the basics, including distributions, censoring, and fitting.
This webinar examines an important perspective. Its' so simple and has made many heroes in the data analysis world since Abraham Ward.
Some of you may have heard of Bayesian analysis.' You may think this is something fancy that only universities do.
Let's take a closer look at the concept of likelihood and it's role in an MCMC analysis. A powerful tool for data analysis.
This webinar is about how we use this thing called Markov Chain Monte Carlo Simulation (MCMC) to create this posse.'
We show you how to get your computer to help you give useful reliability information to your boss, manager, director, or whoever.
To create test results that are meaningful, we need to both design and execute the test well, then, interpret the results accurately.
there are ways you can suck out information from a group of experts in a quantifiable and remarkably accurate way.
A Weibull plot is a really useful way of quickly looking' at data and being able to see' really useful things.
WeiBayes is useful, and there are quite a few catches. Interested in learning about Weibayes analysis? Join us for this webinar.
Sometimes the equations we need to model reliability are just so complicated that we simply avoid them. Let's use Monte Carlo instead.
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