Reliability Analysis … now what? Part 4

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.

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.