An Introduction To Uncertainty In Measurement

Measurement shapes scientific theories, characterises improvements in manufacturing processes and promotes efficient commerce. In concert with measurement is uncertainty, and students in science and engineering need to identify and quantify uncertainties in the measurements they make. This book introduces measurement and uncertainty to second and third year students of science and engineering. Its approach relies on the internationally recognised and recommended guidelines for calculating and expressing uncertainty (known by the acronym GUM). The statistics underpinning the methods are considered and worked examples and exercises are spread throughout the text. Detailed case studies based on typical undergraduate experiments are included to reinforce the principles described in the book. This guide is also useful to professionals in industry who are expected to know the contemporary methods in this increasingly important area. Additional online resources are available to support the book at www.cambridge.org/9780521605793.

"This introduction to measurement uncertainty is intended for metrology professionals working in calibration laboratories and metrology institutes, as well as students in tertiary-level science and engineering programmes. The subject matter is presented with an emphasis on developing models of the physical measurement process. The level of mathematics and statistics used is basic and is typically covered by high school studies"--Distributor's website.

This book presents the need for clarity, consistancy, and accuracy when making measurements; some of the GUM guidelines are presented in this book. A background in first-year calculus and some basic statistics is necessary for understanding.

The scienti c method is based on the measurement of di erent physical qu- tities and the search for relations between their values. All measured values of physical quantities are, however, a ected by uncertainty. Understanding the origin of uncertainty, evaluating its extent, and suitably taking it into account in data analysis, are fundamental steps for assessing the global accuracy of physical laws and the degree of reliability of their technological applications. The introduction to uncertainty evaluation and data analysis procedures is generally made in laboratory courses for freshmen. During my long-lasting teaching experience, I had the feeling of some sort of gap between the ava- able tutorial textbooks, and the specialized monographs. The present work aims at lling this gap, and has been tested and modi ed through a feedback interaction with my students for several years. I have tried to maintain as much as possible a tutorial approach, that, starting from a phenomenolo- cal introduction, progressively leads to an accurate de nition of uncertainty and to some of the most common procedures of data analysis, facilitating the access to advanced monographs. This book is mainly addressed to - dergraduate students, but can be a useful reference for researchers and for secondary school teachers. The book is divided into three parts and a series of appendices. Part I is devoted to a phenomenological introduction to measurement and uncertainty. In Chap.

This volume presents measurement uncertainty and uncertainty budgets in a form accessible to practicing engineers and engineering students from across a wide range of disciplines. The book gives a detailed explanation of the methods presented by NIST in the “GUM” – Guide to Uncertainty of Measurement. Emphasis is placed on explaining the background and meaning of the topics, while keeping the level of mathematics at the minimum level necessary. Dr. Colin Ratcliffe, USNA, and Bridget Ratcliffe, Johns Hopkins, develop uncertainty budgets and explain their use. In some examples, the budget may show a process is already adequate and where costs can be saved. In other examples, the budget may show the process is inadequate and needs improvement. The book demonstrates how uncertainty budgets help identify the most cost effective place to make changes. In addition, an extensive fully-worked case study leads readers through all issues related to an uncertainty analysis, including a variety of different types of uncertainty budgets. The book is ideal for professional engineers and students concerned with a broad range of measurement assurance challenges in applied sciences. This book also: Facilitates practicing engineers’ understanding of uncertainty budgets, essential to calculating cost-effective savings to a wide variety of processes contingent on measurement Presents uncertainty budgets in an accessible style suitable for all undergraduate STEM courses that include a laboratory component Provides a highly adaptable supplement to graduate textbooks for courses where students’ work includes reporting on experimental results Includes an expanded case study developing uncertainty from transducers though measurands and propagated to the final measurement that can be used as a template for the analysis of many processes Stands as a useful pocket reference for all engineers and experimental scientists

This book presents the need for clarity, consistancy, and accuracy when making measurements; some of the GUM guidelines are presented in this book. A background in first-year calculus and some basic statistics is necessary for understanding.

Problems after each chapter

Literally an entire course between two covers, Measurement Uncertainty: Methods and Applications, Fourth Edition, presents engineering students with a comprehensive tutorial of measurement uncertainty methods in a logically categorized and readily utilized format. The new uncertainty technologies embodied in both U.S. and international standards have been incorporated into this text with a view toward understanding the strengths and weaknesses of both. The book is designed to also serve as a practical desk reference in situations that commonly confront an experimenter. The text presents the basics of the measurement uncertainty model, non-symmetrical systematic standard uncertainties, random standard uncertainties, the use of correlation, curve-fitting problems, and probability plotting, combining results from different test methods, calibration errors, and uncertainty propagation for both independent and dependent error sources. The author draws on years of experience in industry to direct special attention to the problem of developing confidence in uncertainty analysis results and using measurement uncertainty to select instrumentation systems.