Authors:
- Introduces the statistical techniques most commonly employed in physical sciences and engineering
- Makes clear distinction between material that is strictly mathematical and theoretical, and practical applications of statistical methods
- Expanded to cover selected core statistical methods used in business science
- Request lecturer material: sn.pub/lecturer-material
Part of the book series: Graduate Texts in Physics (GTP)
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Table of contents (16 chapters)
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Front Matter
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Back Matter
About this book
The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains.
Features new to this edition include:
• a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets.
• a new chapter on the various measures of the mean including logarithmic averages.
• new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors.
• a new case study and additional worked examples.
• mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text.
• end-of-chapter summary boxes, for easy reference.
As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.
Keywords
- Fitting Data with Bivariate Errors
- Goodness of Fit and Parameter Uncertainty
- Maximum Likelihood Fit
- Median, Weighted Mean and Linear Average
- Monte Carlo Methods and Markov Chains
- Probability Theory for Physicists
- Statistical Methods for Science and Engineering
- Statistics for Business Science
- Systematic Errors and Intrinsic Scatter
- Textbook Statistical Methods
Authors and Affiliations
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University of Alabama, Huntsville, USA
Massimiliano Bonamente
About the author
Bibliographic Information
Book Title: Statistics and Analysis of Scientific Data
Authors: Massimiliano Bonamente
Series Title: Graduate Texts in Physics
DOI: https://doi.org/10.1007/978-1-4939-6572-4
Publisher: Springer New York, NY
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2017
Softcover ISBN: 978-1-4939-8239-4Published: 23 June 2018
eBook ISBN: 978-1-4939-6572-4Published: 08 November 2016
Series ISSN: 1868-4513
Series E-ISSN: 1868-4521
Edition Number: 2
Number of Pages: XVII, 318
Number of Illustrations: 36 b/w illustrations, 4 illustrations in colour
Topics: Mathematical Methods in Physics, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics for Business, Management, Economics, Finance, Insurance, Mathematical and Computational Engineering, Complex Systems, Statistical Physics and Dynamical Systems