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Statistical Data Analysis for the Physical Sciences

Statistical Data Analysis for the Physical Sciences Author Adrian Bevan
ISBN-10 9781107067592
Release 2013-05-09
Pages
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Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.



Data Reduction and Error Analysis for the Physical Sciences

Data Reduction and Error Analysis for the Physical Sciences Author Philip R. Bevington
ISBN-10 0071199268
Release 2003
Pages 320
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The purpose of this book is to provide an introduction to the concepts of statistical analysis of data for students at the undergraduate and graduate level, and to provide tools for data reduction and error analysis commonly required in the physical sciences. The presentation is developed from a practical point of view, including enough derivation to justify the results, but emphasizing methods of handling data more than theory. The text provides a variety of numerical and graphical techniques. Computer programs that support these techniques will be available on an accompanying website in both Fortran and C++.



Bayesian Logical Data Analysis for the Physical Sciences

Bayesian Logical Data Analysis for the Physical Sciences Author Phil Gregory
ISBN-10 9781139444286
Release 2005-04-14
Pages
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Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.



Data Analysis Techniques for Physical Scientists

Data Analysis Techniques for Physical Scientists Author Claude A. Pruneau
ISBN-10 9781108267885
Release 2017-10-05
Pages
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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 Data Analysis

Statistical Data Analysis Author Glen Cowan
ISBN-10 9780198501565
Release 1998
Pages 197
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This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).



Statistics for Physical Sciences

Statistics for Physical Sciences Author Brian Martin
ISBN-10 9780123877659
Release 2012-01-25
Pages 320
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Statistics for Physical Sciences is an informal, relatively short, but systematic, guide to the more commonly used ideas and techniques in statistical analysis, as used in physical sciences, together with explanations of their origins. It steers a path between the extremes of a recipe of methods with a collection of useful formulas, and a full mathematical account of statistics, while at the same time developing the subject in a logical way. The book can be read in its entirety by anyone with a basic exposure to mathematics at the level of a first-year undergraduate student of physical science and should be useful for practising physical scientists, plus undergraduate and postgraduate students in these fields. Offers problems at the end of each chapter Features worked examples across all of the chapters Provides a collection of useful formulas in order to give a detailed account of mathematical statistics



Data Analysis with Excel

Data Analysis with Excel  Author Les Kirkup
ISBN-10 0521797373
Release 2002-03-07
Pages 446
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An essential introduction to data analysis techniques using spreadsheets, for undergraduate and graduate students.



Statistical Methods for Physical Science

Statistical Methods for Physical Science Author
ISBN-10 0080860168
Release 1994-12-13
Pages 542
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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



A Practical Guide to Data Analysis for Physical Science Students

A Practical Guide to Data Analysis for Physical Science Students Author Louis Lyons
ISBN-10 0521424631
Release 1991-11-29
Pages 95
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This textbook is intended for undergraduates who are carrying out laboratory experiments in the physical sciences for the first time. It is a practical guide on how to analyze data and estimate errors. The necessary formulas for performing calculations are given, and the ideas behind them are explained, although this is not a formal text on statistics. Specific examples are worked through step by step in the text. Emphasis is placed on the need to think about whether a calculated error is sensible. Students should take this book with them to the laboratory, and the format is intended to make this convenient. The book will provide the necessary understanding of what is involved, should inspire confidence in the method of estimating errors, and enable numerical calculations without too much effort.



Quantitative Methods of Data Analysis for the Physical Sciences and Engineering

Quantitative Methods of Data Analysis for the Physical Sciences and Engineering Author Douglas G. Martinson
ISBN-10 1107029767
Release 2018-05-31
Pages 598
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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 Methods in Physical Oceanography

Data Analysis Methods in Physical Oceanography Author Richard E. Thomson
ISBN-10 9780123877833
Release 2014-07-14
Pages 728
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Data Analysis Methods in Physical Oceanography, Third Edition is a practical reference to established and modern data analysis techniques in earth and ocean sciences. Its five major sections address data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. The revised Third Edition updates the instrumentation used to collect and analyze physical oceanic data and adds new techniques including Kalman Filtering. Additionally, the sections covering spectral, wavelet, and harmonic analysis techniques are completely revised since these techniques have attracted significant attention over the past decade as more accurate and efficient data gathering and analysis methods. Completely updated and revised to reflect new filtering techniques and major updating of the instrumentation used to collect and analyze data Co-authored by scientists from academe and industry, both of whom have more than 30 years of experience in oceanographic research and field work Significant revision of sections covering spectral, wavelet, and harmonic analysis techniques Examples address typical data analysis problems yet provide the reader with formulaic “recipes for working with their own data Significant expansion to 350 figures, illustrations, diagrams and photos



Data Analysis for Scientists and Engineers

Data Analysis for Scientists and Engineers Author Edward L. Robinson
ISBN-10 9781400883066
Release 2016-09-20
Pages 408
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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 for Physical Scientists

Data Analysis for Physical Scientists Author Les Kirkup
ISBN-10 9780521883726
Release 2012-02-16
Pages 510
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Introducing data analysis techniques to help undergraduate students develop the tools necessary for studying and working in the physical sciences.



Statistics

Statistics Author R. J. Barlow
ISBN-10 9781118723234
Release 2013-06-05
Pages 224
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The Manchester Physics Series General Editors: D. J. Sandiford; F. Mandl; A. C. Phillips Department of Physics and Astronomy, University of Manchester Properties of Matter B. H. Flowers and E. Mendoza Optics Second Edition F. G. Smith and J. H. Thomson Statistical Physics Second Edition F. Mandl Electromagnetism Second Edition I. S. Grant and W. R. Phillips Statistics R. J. Barlow Solid State Physics Second Edition J. R. Hook and H. E. Hall Quantum Mechanics F. Mandl Particle Physics Second Edition B. R. Martin and G. Shaw The Physics of Stars Second Edition A.C. Phillips Computing for Scientists R. J. Barlow and A. R. Barnett Written by a physicist, Statistics is tailored to the needs of physical scientists, containing and explaining all they need to know. It concentrates on parameter estimation, especially the methods of Least Squares and Maximum Likelihood, but other techniques, such as hypothesis testing, Bayesian statistics and non-parametric methods are also included. Intended for reasonably numerate scientists it contains all the basic formulae, their derivations and applications, together with some more advanced ones. Statistics features: * Comprehensive coverage of the essential techniques physical scientists are likely to need. * A wealth of examples, and problems with their answers. * Flexible structure and organisation allows it to be used as a course text and a reference. * A review of the basics, so that little prior knowledge is required.



Bayesian Methods for the Physical Sciences

Bayesian Methods for the Physical Sciences Author Stefano Andreon
ISBN-10 9783319152875
Release 2015-05-19
Pages 238
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Statistical literacy is critical for the modern researcher in Physics and Astronomy. This book empowers researchers in these disciplines by providing the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of real-world problems taken from modern astronomical research. The examples are intended to be starting points for readers as they learn to approach their own data and research questions. Acknowledging that scientific progress now hinges on the availability of data and the possibility to improve previous analyses, data and code are distributed throughout the book. The JAGS symbolic language used throughout the book makes it easy to perform Bayesian analysis and is particularly valuable as readers may use it in a myriad of scenarios through slight modifications. This book is comprehensive, well written, and will surely be regarded as a standard text in both astrostatistics and physical statistics. Joseph M. Hilbe, President, International Astrostatistics Association, Professor Emeritus, University of Hawaii, and Adjunct Professor of Statistics, Arizona State University



Bayesian Probability Theory

Bayesian Probability Theory Author Wolfgang von der Linden
ISBN-10 9781107035904
Release 2014-06-12
Pages 649
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Covering all aspects of probability theory, statistics and data analysis from a Bayesian perspective for graduate students and researchers.



Frontiers in Massive Data Analysis

Frontiers in Massive Data Analysis Author National Research Council
ISBN-10 9780309287814
Release 2013-09-03
Pages 190
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Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale--terabytes and petabytes--is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge--from computer science, statistics, machine learning, and application disciplines--that must be brought to bear to make useful inferences from massive data.