Your Reliability Engineering Professional Development Site
Why is confidence level so important in engineering test data analysis?
From the name itself it gives us a very good hint; Confidence level is giving the confidence in data analysis. In the next graph, you can find 10 samples and fitted Weibull 2p distribution with 95% of Confidence level:
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In this video, Hemant Urdhwareshe explains how to estimate tolerance interval at given confidence level and specified percentage of distribution. The purpose of tolerance intervals is to assure that the tolerance limits specified during design are achievable during production! Often, the designers specify tolerance limits that are not demonstrated and create lot of rework and/or rejection during production phase. This can increase cost of poor quality (COPQ) and result in redesign of the product and/or revising specifications after the product is released for production. Changes made after product launch can be expensive
I am adding a small correction: at 7:01 minute, please read the tolerance interval as 152 to 308, (and not 358 as shown in the footer).
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In this video, Hemant Urdhwareshe explains concept of Confidence Intervals for Mean Time Between Failures (MTBF). The concept is explained for time terminated and failure terminated tests. The calculation is illustrated with Chi-square distribution tables and also Excel Template created by IoQR. The video would be useful to those who want to learn these concepts and learn how to calculate MTBF confidence intervals using tables. It will be beneficial to those who wish to take ASQ CRE, CQE, Six Sigma Black Belt and CMBB certification exams.
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Confidence intervals (CIs)provide a means to judge point estimates based on a sample from the population.
If that statement excites you, you may well have the makings of a fine statistician.
CIs are a form of internal estimate and specify a range within which a parameter may reside. CIs helps us evaluate the trustworthiness of point estimates. [Read more…]
The planning of environmental or reliability testing becomes a question of sample size at some point.
It’s probably the most common question I hear as a reliability engineer – how many samples do we need. [Read more…]
There are a number of different methods to calculate confidence intervals for a proportion. The normal approximation method is easy to use and is appropriate in most cases.
Clopper and Pearson describe the Clopper-Pearson method also called the exact confidence interval and we’ll describe it in a separate article.
There are other methods, which again will find a description in separate articles. [Read more…]
As with other point estimates, we often want to calculate the confidence interval about the estimate. The intent is to determine the range of reasonable values for the true and unknown population parameter. For MTBF, this no different.
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…]
A point estimate of the mean of a population is determined by calculating the mean of a sample drawn from the population. The calculation of the mean is the sum of all sample values divided by the number of values. [Read more…]
An error that I see on occasion is the use of statistical confidence with a goal or target value. For example, we want 95% reliability at five years with 90% confidence. What does that mean? [Read more…]
Last week during our CRE Test Prep class, we were covering the Basic Statistics section in the CRE Primer and had several questions regarding Confidence Limits for Reliability.
All of them were fair questions, and when students are asking these types of questions, the class gets better… [Read more…]