Engineering Statistics, 4th Edition

基本信息·出版社：John Wiley & Sons

·页码：512 页

·出版日期：2006年

·ISBN：0471735574

·条形码：9780471735571

·装帧：平装

·正文语种：英语

产品信息有问题吗？请帮我们更新产品信息。

内容简介The statistics every engineer should know

Statistics is essential for solving many types of engineering problems. Focusing on the statistical techniques most often used in engineering practice, Montgomery, Runger, and Hubele′s Engineering Statistics, Fourth Edition presents a wide range of techniques and methods that you′ll be able to call upon in your professional capacities.

Through its three previous editions, Engineering Statistics set the standard for statistical texts serving engineers. This Fourth Edition follows the classic′s winning approach, and provides a host of new features and improvements that you′ll appreciate. Developed initially with sponsorship from the National Science Foundation, this revision covers all the major aspects of engineering statistics including:

∗ Descriptive statistics

∗ Probability and probability distributions

∗ Statistical tests and confidence intervals for one and two samples

∗ Building regression models

∗ Designing and analyzing engineering experiments

∗ Statistical quality control

The new edition also features numerous enhancements that help you grasp and absorb the material. Emphasizing data analysis and statistical inference, the text makes use of new, cross–sections of real engineering situations and real data sets and takes a step–by–step approach so that you gain insight into the underlying structure of the problems as you master the problem–solving techniques. Notes which help you interpret results are a particularly useful feature of the revised text.

With comprehensive integration of the PC–based statistical software Minitab and online support through WileyPLUS, this newest edition of Engineering Statistics will serve as a practical introduction for students and a reliable reference for every stage of your engineering career.

作者简介Douglas C. Montgomery, Regents’ Professor of Industrial Engineering and Statistics at Arizona State University, received his B.S., M.S., and Ph.D. degrees in engineering from Virginia Polytechnic Institute, the editor ofQuality and Reliability Engineering Internationaland a former editor of theJournal of Quality Technology.George C. Runger, Ph.D.,is a Professor in the department of Industrial Engineering at Arizona State University. His research is on data mining, real–time monitoring and control, and other data–analysis methods with a focus on large, complex, multivariate data streams. His work is funded by grants from the National Science Foundation and corporations. In addition to academic work, he was a senior engineer at IBM. He holds degrees in industrial engineering and statistics.Norma Faris Hubele,Professor of Engineering and Statistics at Arizona State University, and Director of Strategic Initiatives for the Ira A. Fulton School of Engineering, has taught and done research at the University level and with industry for over 20 years. Her cutting edge educational research has been supported by the National Science Foundation and is reflected in this textbook.