Let's find the motivation to use reliability statistics and find the resources to learn the statistical tools necessary to succeed.

How do you use the Weibull Distribution?
It's just one of many useful statistical distributions we must master as reliability engineers.
Let's explore an array of distributions and the problems they can help solve in our day-to-day work.
Detailed Information: When confronted with a set of time-to-failure data, what is your go-to analysis approach?
For me, it's a Weibull plot. It's quick, often provides some insight to ask better questions, and is easy to explain to others. A histogram is another great starting point.
If we know a little about the data source, we may favor the normal or lognormal distributions. If discreet data, binomial is the first choice, yet Poisson or hypergeometric have uses too.
A basic understanding of statistical distributions provides a way to summarize data, providing insights to identify or solve problems.
In this webinar, we'll explore a few distributions useful for reliability engineering work and talk about how to select a distribution, the basics of interpreting distributions, and judging if you have selected the right one.
This Accendo Reliability webinar originally broadcast on 14 April 2015.
To view the recorded webinar and slides, visit the webinar page.
When it's Not Normal: How to Choose from a Library of Distributions episode
Density Curves (With a Reliability Engineering Example) article
Where does the Bell Curve come from? episode
Reliability and Statistics episode
Let's find the motivation to use reliability statistics and find the resources to learn the statistical tools necessary to succeed.
Let's explore R software's many capabilities concerning reliability statistics from field data analysis, to statistical process control.
Let's explore an array of distributions and the problems they can help solve in our day-to-day relaibility engineering work.
Perry discusses the basics of DOE (design of experiments) and fundamentals so you can get started with they useful product development tool.
Let's discuss the 6 basic considerations to estimate the necessary sample size to support decision making.
When we make a measurement, we inform a decision. It's important to have data that is true to the actual value.
One of the first things I learned about data analysis was to create a plot, another, and another. Let the data show you what needs attention.
If you want a really easy introduction or review of these functions that help inform a decision then check out this webinar.
Sometimes we have to work out how many of them we need (if they make up a fleet) or how many spare parts we need to keep them running.
Let's explore the ways we use, or should use, statistics as engineers. From gathering data to presenting, from analyzing to comparing.
Let's explore what residuals are, where they come from, and how to evaluate them to detect if the fitted line (model) is adequate or not.
This webinar is a light (re)introduction into common mathematical symbols used in many engineering scenarios including reliability.
Reliability is a measure of your product or system. Confidence is a measure of you. But we often forget this.
How to calculate Gage discrimination - the more useful result for a design situation, and even how to use it for destructive tests.
For those who conduct reliability data analysis or turning a jumble of dots (data points) into meaningful information
It is not just a pretty shape' that seems to work, It comes from a really cool physical phenomena that we find everywhere.
Let's examine a handful of parametric and non-parametric comparison tools, including various hypothesis tests.
You need to have a good idea of the probability distribution of the TTF of your product when it comes to reliability engineering.
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