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Quantitative Methods of Data Analysis for the Physical Sciences and Engineering Vital Source e-bog
Douglas G. Martinson
(2018)
Cambridge University Press
751,00 kr.
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Quantitative Methods of Data Analysis for the Physical Sciences and Engineering
Douglas G. Martinson
(2018)
Sprog: Engelsk
Cambridge University Press
765,00 kr.
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Detaljer om varen
- Vital Source searchable e-book (Fixed pages)
- Udgiver: Cambridge University Press (September 2018)
- ISBN: 9781108671453
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.
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Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Hardback: 626 sider
- Udgiver: Cambridge University Press (September 2018)
- ISBN: 9781107029767
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.
Part I. Fundamentals:
1. The nature of data and analysis;
2. Probability theory;
3. Statistics;
Part II. Fitting Curves to Data;
4. Interpolation;
5. Smoothed curve fitting;
6. Special curve fitting;
Part III. Sequential Data Fundamentals:
7. Serial products;
8. Fourier series;
9. Fourier transform;
10. Fourier sampling theory;
11. Spectral analysis;
12. Cross spectral analysis;
13. Filtering and deconvolution;
14. Linear parametric models;
15. Empirical orthogonal function (EOF) analysis; A1. Overview of matrix algebra; A2. Uncertainty analysis; References; Index.
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