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Prep notes for ASQ Certified Reliability Engineer exam ISSN 2165-8633
The CRE Preparation Notes series provides you with short practical tutorials on all the elements that make up the ASQ CRE body of knowledge. The articles provide introductory material, basics, how-tos, examples, and practical use guidance for the full range of reliability engineering concepts, terms, tools, and practices.
Keep your knowledge fresh by regularly reviewing topics and tools that make up reliability engineering.
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You will find the most recent tutorials in reverse chronological order below.
When using a sample to calculate a statistic we are estimating a population parameter. It is just an estimate and the sample due to the nature of drawing a sample may not create a value (statistic) that is close to the actual value (parameter). [Read more…]
Control charts help us monitor and stabilize a process. A little graphics along with statistics provides a tool to identify when something has changed. Some changes are abrupt and obvious, other a little more subtle, yet the out of control signals each have approximately the same chance of alerting us to a change.
A little graphics along with statistics provides a tool to identify when something has changed. Some changes are abrupt and obvious, other a little more subtle, yet the out of control signals each have approximately the same chance of alerting us to a change. [Read more…]
As you may recall the probability density function describes the behavior of a random variable.
Like a histogram, the PDF when plotted reveals the shape of the distribution. The PDF also has the property that the area under the curve for is one. Another property is the PDF is defined across the entire sample space. [Read more…]
It seems that anytime we draw a sample, it should be taken randomly. Statistics books and papers regularly advise using a random sample. The adverse effect on results drawn from the experiment may hinge on the randomness of the selection of samples. [Read more…]
This nonparametric test evaluates if two continuous cumulative distributions are significantly different or not.
For example, if the assumption is two production lines producing the same product create the same resulting dimensions, comparing a set of samples from each line may reveal if that hypothesis is true or not.
There is a lot of probability, statistics and data analysis involved with reliability engineering. Why is that? Have you considered why our field of endeavor includes the use of these tools?
Let’s say there were no statistical tools. We would not be able to accurately infer a conclusion based on an observation of a few samples. We might react to everything that we observe – constantly spinning our wheels on minor issues. We might make decisions based on factors that did not include the random variation of the items. We might track failure rates, yet not really know how to determine if the few failures we observe were an indication of a major issue or not. [Read more…]
You use your calculator or spreadsheet, or even a statistics software package to calculate standard deviation, which is an estimate of the population standard deviation. Yet, understanding how one could calculate standard deviation without such advanced tools may prove useful. The knowledge of basic sum of squares methods provides a foundation for ANOVA and DOE analysis techniques. [Read more…]
Before computers and statistical software, we relied on tables to determine values for common integration problems – the normal distribution in particular. There is no closed form solution for the integral of the normal distribution probability density function, it requires advanced numerical methods to estimate the area under the curve. [Read more…]
There are two basic ways to consider the central limit theorem. First consider a random variable, X, which has a mean, μ, and variance σ2. If we take a random sample from f(X) of size n and calculate the sample mean, X̄, then as n increases the distribution of the sample means, X̄’s approaches a normal distribution with mean, μ, and variance σ2/√n̄. The original data, X, may have any distribution and when n is suitably large the distribution of the averages will approach a normal distribution. [Read more…]
A continuous distribution is useful in many statistical applications such as process capability, control charts, and confidence intervals about point estimates. On occasion time to failure, data may exhibit behavior that a normal distribution models well. [Read more…]
Learn more and register for the CRE Prep course here. (registration is closed at this time)
The course is 16 two hour sessions – a mix of lecture, discussion and Q&A that focused on what you need to know to pass the ASQ CRE exam. Sure, beyond the course you will need to study. This involved practicing sample exam questions, using your references and calculator, and practicing finding answers quickly.
We discuss ways to study along with any issues or ideas you have as you prepare for the exam.
*Note: These online classes will be Archived and you will be able to access them later even if you cannot make it to the Live class session! This means you may watch them again in case you forget something, miss a class, or can’t make it one day. Saturdays may be scheduled with the instructor for extra sessions or make-up classes if needed.
Mondays, July 13 – August 17, 2015 (3:00PM-5:00PM PDT)
Last Session for questions, September 21 and 28, 2015 (3:00PM-5:00PM PDT)
This class ends prior to the semi-annual ASQ Section testing, where everyone may sign-up through their local Section to take their test. Be sure to check out the ASQ website to get the early-bird rate before the end of this course!
Looking forward to seeing you in the course. And, of course, at any time, feel free to ask questions here, or in the CRE Preparation LinkedIn group.
Learn more and register for the CRE Prep course here.
Similar to the Weibull distribution yet with slightly heavier tails. While not as easy to interpret if the data shows early life or wear out features, the lognormal distribution often fits time to repair data accurately.
Transform the data by taking the natural log of each data point. The resulting values tend to be normally distributed if the original data fits a lognormal distribution.
You can use base 10 or base 2 or any base and the results will still tend to be normally distributed. It is common to use natural log, ln(). [Read more…]
A discreet distribution useful when counting events within a time period.
It models rates, such as the number of gophers in your garden, paint scratches on your car, or the number of shopping carts that arrive in the 5 minutes before you in line. Essentially it’s the count of something over a time period or defined area. [Read more…]
The exponential distribution is a model for items with a constant failure rate (which very rarely occurs).
If the chance of failure is the same each hour (or cycle, etc.), including the first hour, 100th hour, and 1 millionth hour or use, then the exponential distribution is suitable.
Many practitioners assume the failure rate either is constant or changes so little as to be essential constant in order to make reliability calculations simpler. [Read more…]