Download Reliability: Probabilistic Models and Statistical Methods, by Lawrence M. Leemis
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Reliability: Probabilistic Models and Statistical Methods, by Lawrence M. Leemis
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Covers both the Probabalistic models and Statistical methods used by reliability engineers. It contains explanations of how the mathematical models and results apply to engineering design and the analysis of lifetime data sets. Most applications are drawn from mechanical, civil or electrical engineering systems.
- Sales Rank: #3064864 in Books
- Published on: 1994-12-30
- Original language: English
- Number of items: 1
- Dimensions: 9.12" h x .69" w x 7.00" l, 1.26 pounds
- Binding: Paperback
- 288 pages
From the Publisher
An elementary introduction to the probabilistic models and statistical methods used by reliability engineers as applied to, for example, electrical or mechanical systems. Leemis offers explanations of how the mathematical models and results apply to engineering design and the analysis of lifetime data sets, with simple, supplementary proofs and derivations provided when necessary.
From the Back Cover
Covers both the Probabalistic models and Statistical methods used by reliability engineers. It contains explanations of how the mathematical models and results apply to engineering design and the analysis of lifetime data sets. Most applications are drawn from mechanical, civil or electrical engineering systems.
Most helpful customer reviews
12 of 13 people found the following review helpful.
Outstanding!
By A Customer
This is definitely the best text on Reliability Engineering that I've seen. Leemis really brings the material to life in a way that I have not seen replicated in the other texts that I have perused. For a first introduction to Reliability, I cannot think of a better text. The reader should have a solid foundation in mathematical statistics, however, before starting on this volume. An adequate resource for building this foundation is Larson and Marx's "Introduction to Mathematical Statistics". Especially make sure you understand the basics of maximum-likelihood, as Leemis emphasizes it in his derivations -- the more advanced stuff you'll learn about in his book, however.
As an aside, I have actually taken Leemis' class, and I can honestly say that I learned more about probability from his lectures and the text than I ever previously thought possible. Again, I highly recommend the text.
3 of 4 people found the following review helpful.
The definitive introduction to reliability analysis
By Edward Williams
This is THE seminal text on reliability analysis. The author writes with the same crystal clarity he uses to present material at technical conferences. The exercises are carefully graded to lead the diligent reader toward steadily deepening understanding of the material. All diagrams are clear, cogent, carefully annotated, and well keyed to accompanying text. The definition and explanations of cut sets are especially good, allowing the engineer or analyst to economically reduce a complex problem to a set of smaller, more mathematically tractable problems. Also, this work does an excellent job of ramping the reader's knowledge upward from the justifiably assumes prerequisite of basic statistics learned in one introductory class presumably having a calculus prerequisite.
4 of 7 people found the following review helpful.
Excellent presentation of Reliability Math
By E. Taylor
I have to give it to this author, he has a very high assumption of math. If you are not a math wiz but can comprehend Calculus, this book is beyond outstanding. I will give the reader the necessary math methods for achieving you reliability analysis correctly. I spent alot of time going through these calcs and doing the proofs they are with out a dought right on the money. The boo is packed full of wonderful examples and methods. Kudos to the author.
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