Statistics and Probability for Engineering Applications by - pdf epub fb2

Title: Statistics and Probability for Engineering Applications
ISBN: 0750676183
ISBN13: 978-0750676182
Other Formats: doc lit rtf txt
Pages: 396 pages
Publisher: Newnes; 1 edition (April 28, 2003)
Language: English
Category: Science & Math
Size PDF version: 1705 kb
Size EPUB version: 1325 kb
Subcategory: Mathematics
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Statistics and Probability for Engineering Applications provides a complete discussion of all the major topics typically covered in a college engineering statistics course. This textbook minimizes the derivations and mathematical theory, focusing instead on the information and techniques most needed and used in engineering applications. It is filled with practical techniques directly applicable on the job. Written by an experienced industry engineer and statistics professor, this book makes learning statistical methods easier for today's student.
This book can be read sequentially like a normal textbook, but it is designed to be used as a handbook, pointing the reader to the topics and sections pertinent to a particular type of statistical problem. Each new concept is clearly and briefly described, whenever possible by relating it to previous topics. Then the student is given carefully chosen examples to deepen understanding of the basic ideas and how they are applied in engineering. The examples and case studies are taken from real-world engineering problems and use real data. A number of practice problems are provided for each section, with answers in the back for selected problems.
This book will appeal to engineers in the entire engineering spectrum (electronics/electrical, mechanical, chemical, and civil engineering); engineering students and students taking computer science/computer engineering graduate courses; scientists needing to use applied statistical methods; and engineering technicians and technologists.
* Filled with practical techniques directly applicable on the job* Contains hundreds of solved problems and case studies, using real data sets* Avoids unnecessary theory