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Probability And Statistics For Engineers And Scientists

The new edition of Anthony Hayter's book addresses the many challenges professors face in Engineering Statistics. Debate over teaching this course has centered on which topics to emphasize and how to do so (e.g., balancing data analysis with probability and use of the computer with the understanding of formulas). While Hayter's book assumes students have some quantitative ability, it provides a pr...

Hardcover: 916 pages
Publisher: Duxbury Press; 2 edition (December 5, 2001)
Language: English
ISBN-10: 0534386695
ISBN-13: 978-0534386696
Product Dimensions: 1.5 x 8 x 9.2 inches
Amazon Rank: 2809970
Format: PDF Text TXT book

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“Absolutely the worst text book I've ever used. The reading is nowhere near intuitive and every example problem uses information, sample numbers, and table data scattered across the book that you have to go back to in order to follow what's happening....”

marily applied rather than theoretical approach. Hayter has carefully constructed his book to allow for maximum customization. The material has been divided into four sections based on probability (Chapters 1 - 5), basic statistics (Chapters 6 - 10), advanced statistical methodologies (Chapters 11 - 14), and additional topics (Chapters 15 - 17). The Preface offers suggested paths that can be taken, based on topic preference. This is ideal for departments where different methods of teaching coexist. This flexible approach is also employed with regard to the use of computer tools. Because the book is not tied to a particular software package, instructors may choose to use the program that best suits their needs. However, the book does provide substantial computer output (using MINITAB and other programs) to give students the necessary practice in interpreting computer output. "Computer Note" sections offer tips for using various software packages to perform analysis of the data sets, which can be downloaded from the web site. Through the use of extensive examples and data sets, the book illustrates the importance of statistical data collection and analysis for students in the fields of aerospace, biochemical, civil, electrical, environmental, industrial, mechanical, and textile engineering, as well as students in physics, chemistry, computing, biology, management, and mathematics.