Jorge Romeu
Jorge Luis Romeu is a Research Professor with the Department of Mechanical and Aerospace Engineering, MAE, Syracuse University (SU), and an Adjunct Professor for MAE and for SU Whitman School of Management where he regularly teaches statistics, quality and operations research courses. He is also a Senior Science Advisor with Quanterion Solutions Inc., which operates the RIAC (Reliability Info. Ana...read more
Engineers use samples to estimate or test performance measures (PM) such as reliability, MTTF, etc. Having an adequate sample size is important, for it determines the amount of time and dollars dedicated to the effort. The sample size used in an experiment depends, first, on the statistical distribution of the random variable (r.v.) in question (e.g., device life). Such life may be distributed Normally, Exponentially or Weibull, among other statistical distributions. Large variances can also induce large variability. This fact introduces higher levels of uncertainty in estimations, which have to be compensated by drawing larger sample sizes. Therefore, the variability, or variance of the r.v. under study, constitutes the second factor of importance in sample size determination. Finally, we have the issue of desired level of “confidence". To increase the confidence, we need to draw a larger sample. Summarizing, derivation of adequate sample sizes for testing, estimation or experimentation requires three elements: distribution of the r.v., its variability, and the risks of erring in the process of deriving such estimations or tests. In this Webinar we will develop numerical examples that illustrate how to obtain desired sample sizes for testing and CI derivation.
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