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Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process. All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control. Developed with sponsorship from the National Science Foundation, this revision incorporates many insights from the authors teaching experience along with feedback from numerous adopters of previous editions.
PRACTICAL ENGINEERING STATISTICS This lucidly written book offers engineers and advanced studentsall the essential statistical methods and techniques used inday-to-day engineering work. Without unnecessary digressions intoformal proofs or derivations, Practical Engineering Statisticsshows how to select the appropriate statistical method for aspecific task and then how to apply it correctly and confidently.Clear explanations supported by real-world examples lead the readerstep-by-step through each procedure. Topics covered include productdesign and development; estimations of the mean value andvariability of measured data; comparison of processes or products;the relationships between variables; and more. With its emphasis on practical use and its full range ofengineering applications, Practical Engineering Statistics servesas an indispensable, time-saving reference for all engineersworking in design, reliability, assurance, scheduling, andmanufacturing. PRACTICAL ENGINEERING STATISTICS While engineers are frequently involved in projects that requirethe application of statistical methods to analysis, prediction, andplanning, their background in statistics is often insufficient tothe task. In many cases the engineer has had little training instatistics beyond the concepts of the mean, the standard deviation,the median, and the quartile. Even those who have had one or morecourses in statistics will, at times, encounter problems which arebeyond their capacity to solve or understand. Practical Engineering Statistics is designed to give engineers theknowledge to select the statistical approach that is mostappropriate to the problem at hand and the skills to confidentlyapply this approach to specific cases. It provides the engineerwith the statistical tools needed to perform the job effectively,whether it is pro-duct design and development, estimation of themean value and variability of measured data, comparison ofprocesses or products, or the relationship between variables. Its authors bring two different areas of expertise to this uniquebook: statistics and engineering physics. In Practical EngineeringStatistics their collaboration has produced a book that clearlyleads engineers step-by-step through each procedure, withouttime-consuming and unnecessary discussions of proofs andderivations. Statistical procedures are discussed and explained indetail and demonstrated through real-world sample problems, withcorrect answers always provided. Readers learn how to determinewhich data represent true observations and which, through humanerror or flawed data, are false observations. Complex problems are presented with computer printouts of thedatabase, intermediate steps, and results. Numerous illustrationsand tables of all commonly used distributions enhance theusefulness of this invaluable book. Virtually all engineers and advanced students, especially those inmechanical, civil, electrical, aerospace, and chemical engineering,Practical Engineering Statistics is an indispensable reference thatwill give them the tools to do the statistical part of their workquickly and accurately.
Originally published in 1991. Textbook on the understanding and application of statistical procedures to engineering problems, for practicing engineers who once had an introductory course in statistics, but haven't used the techniques in a long time.
In today’s global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. This book gathers together the full range of statistical techniques required by engineers from all fields. It will assist them to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.
Through years of teaching experience, John S. Lawson and John Erjavec have learned that it doesn't take much theoretical background before engineers can learn practical methods of data collections, analysis, and interpretation that will be useful in real life and on the job. With this premise in mind, the authors wrote ENGINEERING AND INDUSTRIAL STATISTICS, which includes the basic topics of engineering statistics but puts less emphasis on the theoretical concepts and elementary topics usually found in an introductory statistics book. Instead, the authors put more emphasis on techniques that will be useful for engineers. With fewer details of traditional probability and inference and more emphasis on the topics useful to engineers, the book is flexible for instructors and interesting for students.
Lean production, has long been regarded as critical to business success in many industries. Over the last ten years, instruction in six sigma has been increasingly linked with learning about the elements of lean production. Introduction to Engineering Statistics and Lean Sigma builds on the success of its first edition (Introduction to Engineering Statistics and Six Sigma) to reflect the growing importance of the "lean sigma" hybrid. As well as providing detailed definitions and case studies of all six sigma methods, Introduction to Engineering Statistics and Lean Sigma forms one of few sources on the relationship between operations research techniques and lean sigma. Readers will be given the information necessary to determine which sigma methods to apply in which situation, and to predict why and when a particular method may not be effective. Methods covered include: • control charts and advanced control charts, • failure mode and effects analysis, • Taguchi methods, • gauge R&R, and • genetic algorithms. The second edition also greatly expands the discussion of Design For Six Sigma (DFSS), which is critical for many organizations that seek to deliver desirable products that work first time. It incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on the design of experiments, and on two level and full factorial experiments, to help improve student intuition-building and retention. The emphasis on lean production, combined with recent methods relating to Design for Six Sigma (DFSS), makes Introduction to Engineering Statistics and Lean Sigma a practical, up-to-date resource for advanced students, educators, and practitioners.