An edition of Data analysis (1999)

Data Analysis

Statistical and Computational Methods for Scientists and Engineers

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Last edited by MARC Bot
October 9, 2024 | History
An edition of Data analysis (1999)

Data Analysis

Statistical and Computational Methods for Scientists and Engineers

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 programs (source code, Java classes, and documentation) and extensive appendices to the main text are available for free download from the book’s page at www.springer.com. Contents Probabilities. Random variables. Random numbers and the Monte Carlo Method. Statistical distributions (binomial, Gauss, Poisson). Samples. Statistical tests. Maximum Likelihood. Least Squares. Regression. Minimization. Analysis of Variance.

Time series analysis. Audience 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, working for bachelor or master degrees, in thesis work, and in research and professional work. “The book is concise, but gives a sufficiently rigorous mathematical treatment of practical statistical methods for data analysis; it can be of great use to all who are involved with data analysis.” Physicalia “This lively and erudite treatise covers the theory of the main statistical tools and their practical applications…a first rate university textbook, and good background material for the practicing physicist.” Physics Bulletin The Author Siegmund Brandt is Emeritus Professor of Physics at the University of Siegen.

With his group he worked on experiments in elementary-particle physics at the research centers DESY in Hamburg and CERN in Geneva in which the analysis of the experimental data plays an important role. He is author or coauthor of textbooks which have appeared in ten languages.

Publish Date
Publisher
Springer
Pages
523

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Previews available in: English

Edition Availability
Cover of: Data Analysis
Data Analysis: Statistical and Computational Methods for Scientists and Engineers
Apr 30, 2017, Springer
paperback
Cover of: Data Analysis
Data Analysis: Statistical and Computational Methods for Scientists and Engineers
Feb 15, 2014, Springer
hardcover
Cover of: Data Analysis
Data Analysis: Statistical and Computational Methods for Scientists and Engineers
2014, Springer International Publishing AG
in English
Cover of: Data Analysis
Data Analysis: Statistical and Computational Methods for Scientists and Engineers
Oct 06, 2011, Springer
paperback
Cover of: Data analysis
Data analysis: statistical and computational methods for scientists and engineers
1999, Springer
electronic resource : in English - 3rd ed.

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Book Details


Edition Notes

Source title: Data Analysis: Statistical and Computational Methods for Scientists and Engineers

Classifications

Library of Congress
QC5.53TA329-348TA640, QA273 .B86213 2014, QC5.53

The Physical Object

Format
hardcover
Number of pages
523

Edition Identifiers

Open Library
OL28118481M
ISBN 10
3319037617
ISBN 13
9783319037615
LCCN
2013957143

Work Identifiers

Work ID
OL19839965W

Work Description

This book bridges the gap between statistical theory and physcal experiment. It provides a thorough introduction to the statistical methods used in the experimental physical sciences and to the numerical methods used to implement them. The treatment emphasizes concise but rigorous mathematics but always retains its focus on applications. The reader is presumed to have a sound basic knowledge of differential and integral calulus and some knowledge of vectors and matrices (an appendix develops the vector and matrix methods used and provides a collection of related computer routines). After an introduction of probability, random variables, computer generation of random numbers (Monte Carlo methods) and impotrtant distributions (such as the biomial, Poisson, and normal distributions), the book turns to a discussion of statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The discussion concludes with the discussion of several important stistical methods: least squares, analysis of variance, polynomial regression, and analysis of tiem series. Appendices provide the necessary methods of matrix algebra, combinatorics, and many sets of useful algorithms and formulae. The book is intended for graduate students setting out on experimental research, but it should also provide a useful reference and programming guide for experienced experimenters. A large number of problems (many with hints or solutions) serve to help the reader test.

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