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A combination of two texts authored by Patrick Dunn, this set covers sensor technology as well as basic measurement and data analysis subjects, a combination not covered together in other references. Written for junior-level mechanical and aerospace engineering students, the topic coverage allows for flexible approaches to using the combination book in courses. MATLAB® applications are included in all sections of the combination, and concise, applied coverage of sensor technology is offered. Numerous chapter examples and problems are included, with complete solutions available.
The third edition of Measurement and Data Analysis for Engineering and Science provides an up-to-date approach to presenting the methods of experimentation in science and engineering. Widely adopted by colleges and universities within the U.S. and abroad, this edition has been developed as a modular work to make it more adaptable to different approaches from various schools. This text details current methods and highlights the six fundamental tools required for implementation: planning an experiment, identifying measurement system components, assessing measurement system component performance, setting signal sampling conditions, analyzing experimental results, and reporting experimental results. What’s New in the Third Edition: This latest edition includes a new chapter order that presents a logical sequence of topics in experimentation, from the planning of an experiment to the reporting of the experimental results. It adds a new chapter on sensors and transducers that describes approximately 50 different sensors commonly used in engineering, presents uncertainty analysis in two separate chapters, and provides a problem topic summary in each chapter. New topics include smart measurement systems, focusing on the Arduino® microcontroller and its use in the wireless transmission of data, and MATLAB® and Simulink® programming for microcontrollers. Further topic additions are on the rejection of data outliers, light radiation, calibrations of sensors, comparison of first-order sensor responses, the voltage divider, determining an appropriate sample period, and planning a successful experiment. Measurement and Data Analysis for Engineering and Science also contains more than 100 solved example problems, over 400 homework problems, and provides over 75 MATLAB® Sidebars with accompanying MATLAB M-files, Arduino codes, and data files available for download.
This book provide a comprehensive set of modeling methods for data and uncertainty analysis, taking readers beyond mainstream methods and focusing on techniques with a broad range of real-world applications. The book will be useful as a textbook for graduate students, or as a training manual in the fields of calibration and testing. The work may also serve as a reference for metrologists, mathematicians, statisticians, software engineers, chemists, and other practitioners with a general interest in measurement science.
Fundamentals of Sensors for Engineering and Science is a practical analysis of sensors and measurement, designed to help readers make informed decisions when selecting an appropriate sensor for a given application. Spurred by a growing demand for information on the evolution of modern sensors, this book evaluates current applications to illustrate
Discover the fundamental principles of biomedical measurement design and performance evaluation with this hands-on guide. Whether you develop measurement instruments or use them in novel ways, this practical text will prepare you to be an effective generator and consumer of biomedical data. Designed for both classroom instruction and self-study, it explains how information is encoded into recorded data and can be extracted and displayed in an accessible manner. Describes and integrates experimental design, performance assessment, classification, and system modelling. Combines mathematical concepts with computational models, providing the tools needed to answer advanced biomedical questions. Includes MATLAB® scripts throughout to help readers model all types of biomedical systems, and contains numerous homework problems, with a solutions manual available online. This is an essential text for advanced undergraduate and graduate students in bioengineering, electrical and computer engineering, computer science, medical physics, and anyone preparing for a career in biomedical sciences and engineering.
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.
Presenting the fundamental tools of experimentation that are currently used by engineers and scientists, Measurement and Data Analysis for Engineering and Science, Second Edition covers the basics of experimentation, hardware of experiments, and methods of data analysis. It also offers historical perspectives throughout. Updating and reorganizing its popular predecessor, this second edition makes the text much easier to follow and enhances the presentation with electronic material. New to the Second Edition Order of chapters now reflects the sequence of topics usually included in an undergraduate course Asterisked sections denote material not typically covered formally during lecture in an introductory undergraduate course More than 150 new problems, bringing the total to over 420 problems Supplementary website that provides unit conversions, learning objectives, review crossword puzzles and solutions, differential equation derivations, laboratory exercise descriptions, MATLAB® sidebars with M-files, and homework data files Thorough and up to date, this edition continues to help students gain a fundamental understanding of the tools of experimentation. It discusses basic concepts related to experiments, measurement system components and responses, data analysis, and effective communication of experimental findings. Ancillary materials for instructors are available on a CD-ROM and a solutions manual is available for qualifying instructors. More data available on www.nd.edu/~pdunn/www.text/measurements.html
"Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors"--
Presenting the fundamental tools of experimentation that are currently used by engineers and scientists, Measurement and Data Analysis for Engineering and Science, Second Edition covers the basics of experimentation, hardware of experiments, and methods of data analysis. It also offers historical perspectives throughout. Updating and reorganizing its popular predecessor, this second edition makes the text much easier to follow and enhances the presentation with electronic material. New to the Second Edition Order of chapters now reflects the sequence of topics usually included in an undergraduate course Asterisked sections denote material not typically covered formally during lecture in an introductory undergraduate course More than 150 new problems, bringing the total to over 420 problems Supplementary website that provides unit conversions, learning objectives, review crossword puzzles and solutions, differential equation derivations, laboratory exercise descriptions, MATLAB® sidebars with M-files, and homework data files Thorough and up to date, this edition continues to help students gain a fundamental understanding of the tools of experimentation. It discusses basic concepts related to experiments, measurement system components and responses, data analysis, and effective communication of experimental findings. Ancillary materials for instructors are available on a CD-ROM and a solutions manual is available for qualifying instructors. More data available on www.nd.edu/~pdunn/www.text/measurements.html