Название: Applied Engineering Statistics, 2nd Edition
Автор: R. Russell Rhinehart, Robert M. Bethea
Издательство: CRC Press
Год: 2022
Страниц: 501
Язык: английский
Формат: pdf (true)
Размер: 21.8 MB

Thoroughly updated throughout, this second edition will continue to be about the practicable methods of statistical applications for engineers, and as well for scientists and those in business. It remains a what-I-wish-I-had-known-when-starting-my-career compilation of techniques. Contrasting a mathematical and abstract orientation of many statistics texts, which expresses the science/math values of researchers, this book has its focus on the application to concrete examples and the interpretation of outcomes. Supporting application propriety, this book also presents the fundamental concepts, provides supporting derivation, and has frequent do and not-do notes.

The authors each came out of significant engineering practice experience, where we used statistical methods to support legitimacy in decision making. After changing to academic careers, we found ourselves co-teaching the unit operations laboratory course, which required students to use similar statistical techniques as part of their career preparation. We co-authored the first edition of Applied Engineering Statistics, to develop a reference text that would be of utility for the students – in both the course and their engineering careers. We continue to sense the need for the explanation of statistical concepts and application methods for the practitioner, and like the original organization of the book – explain basic concepts, explain commonly applied statistical methods, use examples from industrial practice, and provide case study chapters on advanced applications.

A few things have changed since the original publication: 1) The custom has shifted from critical values and the accept/reject dichotomy to the use of p-values to indicate a degree of confidence. 2) The Big Data and Machine Learning era has introduced a few new techniques on finding associations within data. 3) Computational accessibility has improved the utility of non-linear regression and stochastic approaches for propagating uncertainty. 4) New examples and exercises will take the learner out of the “You are finished after doing a simple word problem” mindset to a more complete analysis of issues and auxiliary aspects of a problem. 5) Statistical tools are widely available and convenient, and this book will reveal those in Excel. 6) Rhinehart has a website that offers Excel/VBA programs to apply some of the procedures

Key Features:

Contains details of the computation for the examples.

Includes new examples and exercises.

Includes expanded topics supporting data analysis.

The book is for upper-level undergraduate or graduate students in engineering, the hard sciences, or business programs. The intent is that the text would continue to be useful in professional life, and appropriate as a self-learning tool after graduation – whether in graduate school or in professional practice.