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| Author | : John Robert Taylor |
| Publisher | : Univ Science Books |
| Total Pages | : 327 |
| Release | : 1997-01-01 |
| Genre | : Mathematics |
| ISBN | : 9780935702422 |
Problems after each chapter
| Author | : Ifan Hughes |
| Publisher | : OUP Oxford |
| Total Pages | : 152 |
| Release | : 2010-07-02 |
| Genre | : Science |
| ISBN | : 0191576565 |
This hands-on guide is primarily intended to be used in undergraduate laboratories in the physical sciences and engineering. It assumes no prior knowledge of statistics. It introduces the necessary concepts where needed, with key points illustrated with worked examples and graphic illustrations. In contrast to traditional mathematical treatments it uses a combination of spreadsheet and calculus-based approaches, suitable as a quick and easy on-the-spot reference. The emphasis throughout is on practical strategies to be adopted in the laboratory. Error analysis is introduced at a level accessible to school leavers, and carried through to research level. Error calculation and propagation is presented though a series of rules-of-thumb, look-up tables and approaches amenable to computer analysis. The general approach uses the chi-square statistic extensively. Particular attention is given to hypothesis testing and extraction of parameters and their uncertainties by fitting mathematical models to experimental data. Routines implemented by most contemporary data analysis packages are analysed and explained. The book finishes with a discussion of advanced fitting strategies and an introduction to Bayesian analysis.
| Author | : John Robert Taylor |
| Publisher | : Grove Press |
| Total Pages | : 296 |
| Release | : 1982 |
| Genre | : Mathematics |
| ISBN | : |
| Author | : Jack Merrin |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 112 |
| Release | : 2017-08-29 |
| Genre | : |
| ISBN | : 9781975906658 |
Great scientists master the math behind the science. Do you still delay mastering data analysis, keeping you from more accurate, rigorous, and higher certainty conclusions? Jack Merrin, Ph.D. Princeton University, is a physicist who has helped hundreds of students with math and physics, taught physics labs, and used error analysis through 25 years of research. You can surely learn the right statistical methods from Jack. Introduction to Error Analysis is more than a collection of ad-hoc statistical theory. It is an easy-to-read blueprint used by scientists for presenting correct results. Transform your experimental perspective to confidence. Learn reusable principles for each new scientific project. This book covers reporting measurements and uncertainties, propagation of error, combining results, curve fitting, essential statistical concepts, and much, much, more. You might love this book if: You are doing lab reports or actual research, and it's time to get serious about data analysis. You want to focus on the essential calculations, not on time-wasting theory. You want adaptable MATLAB code for each different calculation. Hey, no need to reinvent the wheel. You want to reach correct and unique results using the established convention. You want to know what is correct to spot bad scientific literature. Introduction to Error Analysis is the concise book you need to start building your successful scientific career. If you like easy-to-follow lessons, practical examples, insightful tips, and an author who actually cares about you getting it right, then you'll love Jack's book. Buy Introduction to Error Analysis to start refining your data analysis skills today!
| Author | : Robert M. Corless |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 896 |
| Release | : 2013-12-12 |
| Genre | : Mathematics |
| ISBN | : 1461484537 |
This book provides an extensive introduction to numerical computing from the viewpoint of backward error analysis. The intended audience includes students and researchers in science, engineering and mathematics. The approach taken is somewhat informal owing to the wide variety of backgrounds of the readers, but the central ideas of backward error and sensitivity (conditioning) are systematically emphasized. The book is divided into four parts: Part I provides the background preliminaries including floating-point arithmetic, polynomials and computer evaluation of functions; Part II covers numerical linear algebra; Part III covers interpolation, the FFT and quadrature; and Part IV covers numerical solutions of differential equations including initial-value problems, boundary-value problems, delay differential equations and a brief chapter on partial differential equations. The book contains detailed illustrations, chapter summaries and a variety of exercises as well some Matlab codes provided online as supplementary material. “I really like the focus on backward error analysis and condition. This is novel in a textbook and a practical approach that will bring welcome attention." Lawrence F. Shampine A Graduate Introduction to Numerical Methods and Backward Error Analysis” has been selected by Computing Reviews as a notable book in computing in 2013. Computing Reviews Best of 2013 list consists of book and article nominations from reviewers, CR category editors, the editors-in-chief of journals, and others in the computing community.
| Author | : Philip R. Bevington |
| Publisher | : McGraw-Hill Science, Engineering & Mathematics |
| Total Pages | : 362 |
| Release | : 1992 |
| Genre | : Mathematics |
| ISBN | : |
This book is designed as a laboratory companion, student textbook or reference book for professional scientists. The text is for use in one-term numerical analysis, data and error analysis, or computer methods courses, or for laboratory use. It is for the sophomore-junior level, and calculus is a prerequisite. The new edition includes applications for PC use.
| Author | : Herman J. C. Berendsen |
| Publisher | : Cambridge University Press |
| Total Pages | : 239 |
| Release | : 2011-04-07 |
| Genre | : Technology & Engineering |
| ISBN | : 1139497855 |
All students taking laboratory courses within the physical sciences and engineering will benefit from this book, whilst researchers will find it an invaluable reference. This concise, practical guide brings the reader up-to-speed on the proper handling and presentation of scientific data and its inaccuracies. It covers all the vital topics with practical guidelines, computer programs (in Python), and recipes for handling experimental errors and reporting experimental data. In addition to the essentials, it also provides further background material for advanced readers who want to understand how the methods work. Plenty of examples, exercises and solutions are provided to aid and test understanding, whilst useful data, tables and formulas are compiled in a handy section for easy reference.
| Author | : J. E. Akin |
| Publisher | : Elsevier |
| Total Pages | : 465 |
| Release | : 2005-06-22 |
| Genre | : Technology & Engineering |
| ISBN | : 0080472753 |
This key text is written for senior undergraduate and graduate engineering students. It delivers a complete introduction to finite element methods and to automatic adaptation (error estimation) that will enable students to understand and use FEA as a true engineering tool. It has been specifically developed to be accessible to non-mathematics students and provides the only complete text for FEA with error estimators for non-mathematicians. Error estimation is taught on nearly half of all FEM courses for engineers at senior undergraduate and postgraduate level; no other existing textbook for this market covers this topic. The only introductory FEA text with error estimation for students of engineering, scientific computing and applied mathematics Includes source code for creating and proving FEA error estimators
| Author | : JOHN R. TAYLOR |
| Publisher | : |
| Total Pages | : 380 |
| Release | : 2022-03-15 |
| Genre | : |
| ISBN | : 9781940380087 |
This remarkable text by John R. Taylor has been a non-stop best-selling international hit since it was first published forty years ago. However, the two-plus decades since the second edition was released have seen two dramatic developments; the huge rise in popularity of Bayesian statistics, and the continued increase in the power and availability of computers and calculators. In response to the former, Taylor has added a full chapter dedicated to Bayesian thinking, introducing conditional probabilities and Bayes' theorem. The several examples presented in the new third edition are intentionally very simple, designed to give readers a clear understanding of what Bayesian statistics is all about as their first step on a journey to become practicing Bayesians. In response to the second development, Taylor has added a number of chapter-ending problems that will encourage readers to learn how to solve problems using computers. While many of these can be solved using programs such as Matlab or Mathematica, almost all of them are stated to apply to commonly available spreadsheet programs like Microsoft Excel. These programs provide a convenient way to record and process data and to calculate quantities like standard deviations, correlation coefficients, and normal distributions; they also have the wonderful ability - if students construct their own spreadsheets and avoid the temptation to use built-in functions - to teach the meaning of these concepts.
| Author | : Luis Tenorio |
| Publisher | : SIAM |
| Total Pages | : 275 |
| Release | : 2017-07-06 |
| Genre | : Mathematics |
| ISBN | : 1611974917 |
Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an inverse problem is to recover an object from noisy, usually indirect observations. Solutions to inverse problems are subject to many potential sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy data, and limitations in the number of observations; thus it is important to include an assessment of the uncertainties as part of the solution. Such assessment is interdisciplinary by nature, as it requires, in addition to knowledge of the particular application, methods from applied mathematics, probability, and statistics. This book bridges applied mathematics and statistics by providing a basic introduction to probability and statistics for uncertainty quantification in the context of inverse problems, as well as an introduction to statistical regularization of inverse problems. The author covers basic statistical inference, introduces the framework of ill-posed inverse problems, and explains statistical questions that arise in their applications. An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems?includes many examples that explain techniques which are useful to address general problems arising in uncertainty quantification, Bayesian and non-Bayesian statistical methods and discussions of their complementary roles, and analysis of a real data set to illustrate the methodology covered throughout the book.
