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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.
A risk management plan has to meet your organizations needs as the organization identifies, manages, and mitigates potential and actual risks. The ISO 31000 framework does not detail how an organization should plan or what elements are required for an effective plan.
This short article outlines a few details that may comprise a starting point as you build a risk management plan within your organization. Or, the suggestions here may help you review and improve your existing plan. [Read more…]
The McNemar test is a nonparametric statistical test to compare dichotomous (unique) results of paired data.
If you are comparing survey results (favorable/unfavorable) for a group of potential customers given two ad campaigns, or evaluating the performance of two vendors in a set of prototype units, or determining if a maintenance procedure is effective for a set of equipment, this test permits the detection of changes.
The McNemar test is similar to the χ2 test. The McNemar only works with a two by two table, where the χ2 test works with larger tables. The χ2 test is checking for independence, while the McNemar test is looking for consistency in results.
Let’s examine an example where a group of people are surveyed about a prototype design, before and after a presentation. [Read more…]
This is part of a short series on the common distributions.
The Triangle distribution is univariate continuous distribution. This short article focuses on 4 formulas of the triangle distribution.
The distribution becomes a standard triangle distribution when a = 0, b = 1, thus it has a mean at the $- \sqrt{{c}/{2}\;} -$ and the median is at $- 1-\sqrt{{\left( 1-c \right)}/{2}\;}-$. The distribution becomes a symmetrical triangle distribution when $- c={\left( b-a \right)}/{2}\;-$.
The triangle distribution is used to approximate distributions when the actual distribution is unknown and bounded, often useful for Monte Carlo simulations. Other applications include subjective representation when there is evidence of bounds and a mode, or as a substitution to the beta distribution since it is bounded. [Read more…]
This is part of a short series on the common distributions.
The Uniform distribution is a univariate continuous distribution. This short article focuses on 7 formulas of the Uniform Distribution. A common application is as a non-informative prior. Another application is to model a bounded parameter. The uniform distribution also finds application in random number generation. [Read more…]
This is part of a short series on the common life data distributions.
The Poisson distribution is a discrete distribution. This short article focuses on 4 formulas of the Poisson Distribution. It is also known as the rare event distribution. It has application in a homogeneous Poisson princess and with renewal theory. [Read more…]
This is part of a short series on the common life data distributions.
The Pareto distribution is a univariate continuous distribution useful when modeling rare events as the survival function slowly decreases as compared to other life distributions. This short article focuses on 7 formulas of the Pareto Continuous Distribution also known as the Pareto distribution of the first kind (there are three kinds, apparently). [Read more…]
The quality triangle is an admission that every project has design constraints. Note that reliability is rarely listed directly within the triangle, yet reliability does impact each element of the triad.
In a world where the design team is beset with numerous design for X priorities, understanding how reliability related to the top three and most common set of constraints is imperative.
For any set of priorities, you should be able to convey how addressing reliability performance in the design and assembly process impacts those priorities. Addressing reliability can improve quality, reduce the risk of launch delays, and reduce the cost of a product. All this and improve customer satisfaction as well.
Let’s explore a few ways you can connect reliability priorities to the common quality triangle priorities. [Read more…]
This is part of a short series on the common life data distributions.
The Binomial distribution is discrete. This short article focuses on 4 formulas of the Binomial Distribution.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
This is part of a short series on the common life data distributions.
The Birnbaum-Saunders distribution is a univariate continuous distribution. This short article focuses on 7 formulas of the Birnbaum-Saunders Distribution. This distribution was designed to model the Miner’s rule, thus allowing for non-constant fatigue cycles through accumulated damage.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
This is part of a short series on the common life data distributions.
The Beta distribution is a univariate continuous distribution. This short article focuses on 7 formulas of the Beta Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
The Beta function is not used to describe life data very often yet is used to describe model parameters that are contained within an interval. For example given a probability parameter constrained from 0 ≤ p ≤ 1 the use of the Beta distribution is well suited to model such a parameter.
The Beta distribution is also known as a Pearson Type I distribution. [Read more…]
This is part of a short series on the common life data distributions.
The Logistic distribution is univariate continuous distribution. This short article focuses on 7 formulas of the Logistic Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
This is part of a short series on the common life data distributions.
The Normal distribution is a continuous distribution widely taught. It is commonly used to describe items, measurements, or time to failure data when there are many additive perturbations that comprise the results. This short article focuses on 7 formulas of the Normal Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
This is part of a short series on the common life data distributions.
The Lognormal distribution is a versatile and continuous distribution. It is similar to the Weibull in flexibility with just slightly fatter tails in most circumstances. It is commonly used to describe time to repair behavior. This short article focuses on 7 formulas of the Lognormal Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
This is part of a short series on the common life data distributions.
The Exponential distribution is popular and useful in isolated situations. It has some nice features and flexibility that support it’s popularity. This short article focuses on 7 formulas of the Exponential Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
This is part of a short series on the common life data distributions.
The Gamma distribution is routinely used to describe systems undergoing sequences of events or shocks which lead to eventual failure. Also used to describe renewal processes. This short article focuses on 7 formulas of the Gamma Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
