Engineering Statistics


Book Description

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


Book Description

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.




Applied Engineering Statistics


Book Description

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.




Introduction to Engineering Statistics and Lean Sigma


Book Description

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.







Statistical Engineering


Book Description

Reducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product. The authors have structured this book around an algorithm for reducing process variation that they call "Statistical Engineering." The algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. The fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. A key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation. The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. A unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches. Also includes case studies, chapter exercises, chapter supplements, and six appendices. PRAISE FOR Statistical Engineering "I found this book uniquely refreshing. Don't let the title fool you. The methods described in this book are statistically sound but require very little statistics. If you have ever wanted to solve a problem with statistical certainty (without being a statistician) then this book is for you. - A reader in Dayton, OH "This is the most comprehensive treatment of variation reduction methods and insights I’ve ever seen."- Gary M. Hazard Tellabs "Throughout the text emphasis has been placed on teamwork, fixing the obvious before jumping to advanced studies, and cost of implementation. All this makes the manuscript !attractive for real-life application of complex techniques." - Guru Chadhabr Comcast IP Services COMMENTS FROM OTHER CUSTOMERS Average Customer Rating (5 of 5 based on 1 review) "This is NOT a typical book on statistical tools. It is a strategy book on how to search for cost-effective changes to reduce variation using empirical means (i.e. observation and experiment). The uniqueness of this book: Summarizes the seven ways to reduce variation so we know the goal of the data gathering and analysis, present analysis results using graphs instead of P-value, and integrates Taguchi, Shainin methods, and classical statistical approach. It is a must read for those who are in the business of reducing variation using data, in particular for the Six Sigma Black Belts and Master Black Belts. Don't forget to read the solutions to exercises and supplementary materials to each chapter on the enclosed CD-ROM." - A. Wong, Canada




Statistics and Data Analysis for Financial Engineering


Book Description

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.




Modern Statistics for Engineering and Quality Improvement


Book Description

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.




Introductory Statistics for Engineering Experimentation


Book Description

A concise treatment for undergraduate and graduate students who need a guide to statistics that focuses specifically on engineering.




Probability and Statistics for Engineering and the Sciences


Book Description

This market-leading text provides a comprehensive introduction to probability and statistics for engineering students in all specialties. This proven, accurate book and its excellent examples evidence Jay Devore’s reputation as an outstanding author and leader in the academic community. Devore emphasizes concepts, models, methodology, and applications as opposed to rigorous mathematical development and derivations. Through the use of lively and realistic examples, students go beyond simply learning about statistics-they actually put the methods to use. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.