Statistical Distributions in Engineering

Description

Engineers face numerous uncertainties in the design and development of products and processes. To deal with the uncertainties inherent in measured information, they make use of a variety of statistical techniques. This outstanding text presents single-variable statistical distributions that are useful in engineering design and analysis. It lists significant properties of these distributions and describes methods for estimating parameters and their standard errors, constructing confidence intervals, testing hypotheses, and plotting data. Each distribution is worked through typical applications. Figures are used extensively to clarify concepts. Methods are illustrated by numerous fully worked examples in the form of Mathcad documents that readers can use as templates for their own data, eliminating the need for programming. Intended as both a text and reference, the book assumes an elementary knowledge of calculus and probability. Graduate and advanced undergraduate students, as well as practicing engineers and scientists, will be able to use this book to solve practical problems connected with the uncertainty assessment in a wide range of engineering contexts.

' ... the book is well structured and well written, particularly in the stand-alone chapters on the individual distributions ... In summary, if your work or graduate study involves the statistical analysis of engineering data, Statistical Distributions in Engineering provides a good value reference.' J. E. Cooper, Journal of Mechanical Engineering Science 'The book is well written, quite comprehensive and easy to follow. It is highly recommended for graduate students and researchers in engineering as well as practicing engineers and scientists who deal with uncertainty assessment in engineering contexts.' The American Statistician 'As a reference, the book could serve a great variety of readers, not limited to engineers only, but to all kinds of readers involved in the application of statistics and statistical modelling.' D. Christozov, Measuring Science Technology