Quantitative Methods of Data Analysis for the Physical Sciences and Engineering

Quantitative Methods of Data Analysis for the Physical Sciences and Engineering PDF

Author: Douglas G. Martinson

Publisher: Cambridge University Press

Published: 2018-09-20

Total Pages: 631

ISBN-13: 1108632629

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This book provides thorough and comprehensive coverage of most of the new and important quantitative methods of data analysis for graduate students and practitioners. In recent years, data analysis methods have exploded alongside advanced computing power, and it is critical to understand such methods to get the most out of data, and to extract signal from noise. The book excels in explaining difficult concepts through simple explanations and detailed explanatory illustrations. Most unique is the focus on confidence limits for power spectra and their proper interpretation, something rare or completely missing in other books. Likewise, there is a thorough discussion of how to assess uncertainty via use of Expectancy, and the easy to apply and understand Bootstrap method. The book is written so that descriptions of each method are as self-contained as possible. Many examples are presented to clarify interpretations, as are user tips in highlighted boxes.

Data Analysis for Scientists and Engineers

Data Analysis for Scientists and Engineers PDF

Author: Edward L. Robinson

Publisher: Princeton University Press

Published: 2016-09-20

Total Pages: 408

ISBN-13: 1400883067

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Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. In-depth discussion of data analysis for scientists and engineers Coverage of both frequentist and Bayesian approaches to data analysis Extensive look at analysis techniques for time-series data and images Detailed exploration of linear and nonlinear modeling of data Emphasis on error analysis Instructor's manual (available only to professors)

Data Analysis

Data Analysis PDF

Author: Siegmund Brandt

Publisher: Springer Science & Business Media

Published: 2014-02-14

Total Pages: 523

ISBN-13: 3319037625

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The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The concise mathematical treatment of the subject matter is illustrated by many examples and for the present edition a library of Java programs has been developed. It comprises methods of numerical data analysis and graphical representation as well as many example programs and solutions to programming problems. The book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, in working for bachelor or master degrees, in thesis work, and in research and professional work.

Data Analysis Techniques for Physical Scientists

Data Analysis Techniques for Physical Scientists PDF

Author: Claude A. Pruneau

Publisher: Cambridge University Press

Published: 2017-10-05

Total Pages: 719

ISBN-13: 1108267882

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A comprehensive guide to data analysis techniques for physical scientists, providing a valuable resource for advanced undergraduate and graduate students, as well as seasoned researchers. The book begins with an extensive discussion of the foundational concepts and methods of probability and statistics under both the frequentist and Bayesian interpretations of probability. It next presents basic concepts and techniques used for measurements of particle production cross-sections, correlation functions, and particle identification. Much attention is devoted to notions of statistical and systematic errors, beginning with intuitive discussions and progressively introducing the more formal concepts of confidence intervals, credible range, and hypothesis testing. The book also includes an in-depth discussion of the methods used to unfold or correct data for instrumental effects associated with measurement and process noise as well as particle and event losses, before ending with a presentation of elementary Monte Carlo techniques.

Statistical Methods for Physical Science

Statistical Methods for Physical Science PDF

Author:

Publisher: Academic Press

Published: 1994-12-13

Total Pages: 563

ISBN-13: 0080860168

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This volume of Methods of Experimental Physics provides an extensive introduction to probability and statistics in many areas of the physical sciences, with an emphasis on the emerging area of spatial statistics. The scope of topics covered is wide-ranging-the text discusses a variety of the most commonly used classical methods and addresses newer methods that are applicable or potentially important. The chapter authors motivate readers with their insightful discussions. Examines basic probability, including coverage of standard distributions, time series models, and Monte Carlo methods Describes statistical methods, including basic inference, goodness of fit, maximum likelihood, and least squares Addresses time series analysis, including filtering and spectral analysis Includes simulations of physical experiments Features applications of statistics to atmospheric physics and radio astronomy Covers the increasingly important area of modern statistical computing

Data-Driven Science and Engineering

Data-Driven Science and Engineering PDF

Author: Steven L. Brunton

Publisher: Cambridge University Press

Published: 2022-05-05

Total Pages: 615

ISBN-13: 1009098489

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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Methods and Applications of Statistics in Engineering, Quality Control, and the Physical Sciences

Methods and Applications of Statistics in Engineering, Quality Control, and the Physical Sciences PDF

Author: Narayanaswamy Balakrishnan

Publisher: Wiley

Published: 2011-04-12

Total Pages: 0

ISBN-13: 9780470405086

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Inspired by the Encyclopedia of Statistical Sciences, Second Edition (ESS2e), this volume presents a concise, well-rounded focus on the statistical concepts and applications that are essential for understanding gathered data in the fields of engineering, quality control, and the physical sciences. The book successfully upholds the goals of ESS2e by combining both previously-published and newly developed contributions written by over 100 leading academics, researchers, and practitioner in a comprehensive, approachable format. The result is a succinct reference that unveils modern, cutting-edge approaches to acquiring and analyzing data across diverse subject areas within these three disciplines, including operations research, chemistry, physics, the earth sciences, electrical engineering, and quality assurance. In addition, techniques related to survey methodology, computational statistics, and operations research are discussed, where applicable. Topics of coverage include: optimal and stochastic control, artificial intelligence, quantum mechanics, and fractals.

Supporting Research and Data Analysis in NASA's Science Programs

Supporting Research and Data Analysis in NASA's Science Programs PDF

Author: National Research Council

Publisher: National Academies Press

Published: 1998-10-16

Total Pages: 120

ISBN-13: 0309173590

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Effective science, clearly a mandate for the National Aeronautics and Space Administration (NASA), involves asking significant questions about the physical and biological world and seeking definitive answers. Its product is new knowledge that has value to the nation. NASA's flight projects are highly visible and usually the most costly elements of this process, but they are only a part of the science enterprise. Flight projects are founded on research that defines clear scientific goals and questions, designs missions to address those questions, and develops the required technologies to accomplish the missions. This research is funded primarily by NASA's research and analysis (R&A) programs. Data from flight projects are transformed into knowledge through analysis and synthesis-research that is funded both by R&A and by the data analysis (DA) portion of mission operations and data analysis (MO&DA) programs. R&A and DA programs are the subject of this report and are grouped for convenience under the heading of research and data analysis (R&DA).

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