Statistical Models in Engineering (Wiley Classics Library) (Paperback)

by Gerald J. Hahn (Author), Samuel S. Shapiro (Author) "An important development in modern science and engineering is the study of systems in a probabilistic rather than a deterministic framework..." (more)
Key Phrases: exponentially distributed variate, gamma variate, normal distribution approximation, Monte Carlo, New York, Cambridge University Press (more...)

Editorial Reviews

Product Description
A detailed treatment on the use of statistical models representing physical phenomena. Considers the relevance of the popular normal distribution models and the applicability of exponential distribution in reliability problems. Introduces and discusses the use of alternate models such as gamma, beta and Weibull distributions. Features expansive coverage of system performance and describes an exact method known as the transformation of variables. Deals with techniques on assessing the adequacy of a chosen model including both graphical and analytical procedures. Contains scores of illustrative examples, most of which have been adapted from actual problems.

Product Details

• Paperback: 376 pages
• Publisher: Wiley-Interscience (April 1, 1994)
• Language: English
• ISBN-10: 0471040657
• ISBN-13: 978-0471040651
• Product Dimensions: 9 x 6 x 0.7 inches
• Shipping Weight: 1.1 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars See all reviews (2 customer reviews)
• Amazon.com Sales Rank: #1,299,882 in Books (See Bestsellers in Books)

  Popular in this category: (What's this?)

  #67 in

  Books > Science > Mathematics > Applied > Engineering > Statistics

Customer Reviews

Most Helpful Customer Reviews

7 of 7 people found the following review helpful:

5.0 out of 5 stars An incredibly helpful resource for statistical modeling., June 5, 1999

By A Customer

This book has proven incredibly helpful on so many occasions that I highly recommend it. Here are some examples:

Years ago, when I had a problem to deal with to improve probe yields on some major products, I presumed that a certain technique existed. I searched through several libraries, and finally found this book - and it had exactly the technique I needed.

Months later, an associate expressed frustration with his efforts to model a complex reliability problem - I showed him this book, and his eyes lit up as he saw the technique he needed.

Years later, when I needed to develop a statistical model for on time delivery, I again referred to this book to derive relationships between the standard deviation and average yields - but it also provided the information I needed on using Poisson statistics for process and assembly yield, and gave a helpful model for obtaining the cycle time distributions for a series of processes, such as from order through manufacturing and shipping to delivery of final product.

5.0 out of 5 stars Excellent Statistical Reference, May 9, 2008

By	mhnstr (Christchurch, New Zealand) - See all my reviews

This book has been an incredible resource when I realized that I needed to use statistical models for my analysis, but didn't know where to begin. My background in statistics was basic and this book proved to be written so well that I could follow along. I needed details about how to estimate the parameters for the Gamma distribution and this book had what I needed. I wanted to run a Monte Carlo simulation and this book explained the fundamentals. The book also proved incredibly useful when learning about Chi-Squared goodness of fit tests and many other statistical methods. I highly recommend this book as a resource for statistical methods.