Quality Sampling and Reliability

New Uses for the Poisson Distribution

By: John J Heldt

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Hardcover | 21 October 1998 | Edition Number 1

At a Glance

Hardcover
166 Pages

Dimensions(cm)
22.9 x 15.2 x 1.12

Hardcover

RRP $326.00

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As a mathematical model for determining the probable number of outcomes, the new Poisson Distribution tables have long been an easier tool to use for reliability analyses. Longtime quality professional, inventor, and consultant John J. Heldt now makes the Poisson Table even more useful-creating two new tables (available only in this book) with the Poisson terms rearranged for further ease of estimation.

Quality Sampling and Reliability: New Uses for the Poisson Distribution simplifies the steps involved with reliability testing; Mean Time Between Failure (MTBF) assessment; advantages and risks involved in reliability life testing; and an example of methodology for tracking the MTBF for products in the field.

In addition to the tried-and-true Standard Poisson table, used to review conventional Poisson uses, Heldt's two variations yield these results:

Estimations of product Mean Time Between Failures (MTBFs), based on life tests-including the 90%, 80% or 60% envelop for any MTBFs that have been derived

Development of the Operating Characteristic Curves for Life testing-showing the risks and advantages of any test used to assure the product MTBF is not varying in a detrimental manner

Written for easy comprehension, with numerous illustrations, Quality Sampling and Reliability: New Uses for the Poisson Distribution will help quality professionals, engineers, instructors and students alike in their reliability testing tasks.

Industry Reviews

"illustrations of this book enlightening and capable of providing quick solutions to immediate practical engineering problems as well as an improved understanding of statistical quality control. makes reliability work much easier and can be used for quicker release of statistical information." -Hoang Pham, Department of Industrial Engineering, Rutgers University, Quality Sampling and Reliability Promo Copy

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