Data Analysis: Statistical and Computational Methods for Scientists and Engineers (Ohlin Lectures; 7) - Hardcover
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Book by Brandt Siegmund
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"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
"... Serves as a nice reference guide for any scientist interested in the fundamentals of data analysis on the computer." The American Statistician
Bridging the gap between statistical theory and physical experiment, this is a thorough introduction to the statistical methods used in the experimental physical sciences and to the numerical methods used to implement them. The treatment emphasises concise but rigorous mathematics but always retains its focus on applications. Readers are assumed to have a sound basic knowledge of differential and integral calculus and some knowledge of vectors and matrices. After an introduction to probability, random variables, computer generation of random numbers and important distributions, the book turns to statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The discussion concludes with several important statistical methods: least squares, analysis of variance, polynomial regression, and analysis of time series. Appendices provide the necessary methods of matrix algebra, combinatorics, and many sets of useful algorithms and formulae.
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- VerlagSpringer
- Erscheinungsdatum1998
- ISBN 10 0387984984
- ISBN 13 9780387984988
- EinbandTapa dura
- Anzahl der Seiten692
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Data Analysis : Statistical and Computational Methods for Scientists and Engineers
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Buchbeschreibung Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. An accompanying CD-ROM provides detailed code (in Fortran77 and in C) for implementing many of these algorithms (for IBM PS/2 or PC machines). 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. Artikel-Nr. 9780387984988
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