Search results
Results from the WOW.Com Content Network
For many devices, the wear-out failure point is measured by the number of cycles performed before the device fails, and can be discovered by cycle testing. In cycle testing, a device is cycled as rapidly as practical until it fails. When a collection of these devices are tested, the test will run until 10% of the units fail dangerously. FMEDA
where B 10 is the number of operations that a device will operate prior to 10% of a sample of those devices would fail and n op is number of operations. B 10d is the same calculation, but where 10% of the sample would fail to danger. n op is the number of operations/cycle in one year. [11]
Given a sample set, one can compute the studentized residuals and compare these to the expected frequency: points that fall more than 3 standard deviations from the norm are likely outliers (unless the sample size is significantly large, by which point one expects a sample this extreme), and if there are many points more than 3 standard ...
In this case, 70/100 = 0.70 or 70% yield. The same example using first pass yield (FPY) would take into account rework: (# units leaving process A as good parts with no rework) / (# units put into the process) 100 units enter process A, 5 were reworked, and 90 leave as good parts. The FPY for process A is (90-5)/100 = 85/100 = 0.8500
Given a sample from a normal distribution, whose parameters are unknown, it is possible to give prediction intervals in the frequentist sense, i.e., an interval [a, b] based on statistics of the sample such that on repeated experiments, X n+1 falls in the interval the desired percentage of the time; one may call these "predictive confidence intervals".
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
Thus the sampling distribution of the quantile of the sample maximum is the graph x 1/k from 0 to 1: the p-th to q-th quantile of the sample maximum m are the interval [p 1/k N, q 1/k N]. Inverting this yields the corresponding confidence interval for the population maximum of [m/q 1/k, m/p 1/k].
In practice, testing measures are never perfectly consistent. Theories of test reliability have been developed to estimate the effects of inconsistency on the accuracy of measurement. The basic starting point for almost all theories of test reliability is the idea that test scores reflect the influence of two sorts of factors: [7] 1.