Modern Engineering Statistics

An introductory perspective on statistical applications in the field of engineering Modern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering. With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features: Examples demonstrating the use of statistical thinking and methodology for practicing engineers A large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data sets Clear illustrations of the relationship between hypothesis tests and confidence intervals Extensive use of Minitab and JMP to illustrate statistical analyses The book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics.

An introductory perspective on statistical applications in the field of engineering

Modern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering.

With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features:

• Examples demonstrating the use of statistical thinking and methodology for practicing engineers
  
  

• A large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data sets
  
  

• Clear illustrations of the relationship between hypothesis tests and confidence intervals
  
  

• Extensive use of Minitab and JMP to illustrate statistical analyses
  
  

The book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics.

The objective of this book is to motivate an appreciation of contemporary statistical techniques within the context of engineering. The author presents an optimum blend between statistical thinking and statistical methodology through emphasis of a broad sweep of "tools" rather than endless streams of seemingly unrelated methods and formulae. The book has many novel features, including (among others) the connection that is made (but rarely illustrated) between hypothesis testing and confidence intervals and the discussions of topics such as mechanistic models and prediction intervals. Though computing equations are kept to a minimum, MINITAB and JMP from SAS are discretely employed as the statistical software packages of choice due to their simplicity and popularity among Six Sigma enthusiasts. There is a generous selection of homework problems. - Publisher