Douglas G. Martinson

Quantitative Methods of Data Analysis for the Physical Sciences and Engineering

• Gebundenes Buch

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 632
• Erscheinungstermin: 12. Februar 2019
• Englisch
• Abmessung: 260mm x 183mm x 38mm
• Gewicht: 1375g
• ISBN-13: 9781107029767
• ISBN-10: 1107029767
• Artikelnr.: 49305216

Autorenporträt

Douglas G. Martinson is a Lamont Research Professor in the Division of Ocean and Climate Physics at Columbia University's Lamont-Doherty Earth Observatory. A physical oceanographer who researches the role of polar oceans in global climate, his research involves the collection of a large amount of data and considerable quantitative analysis. He developed the course on Quantitative Methods of Data Analysis as an Adjunct Professor for the Department of Earth and Environmental Sciences at Columbia University, New York, and received an Outstanding Teacher Award in 2004.

Inhaltsangabe

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.

Rezensionen

'This text is suitable for undergraduates and graduates, as well as seasoned scientists and engineers seeking to broaden their statistical skills. It will have lasting value as it is comprehensive, containing detailed explanations of a wide range of statistical methods. The book is clearly written by a meticulous scientist who is an expert in the field and an award winning teacher.' James Hays, Columbia University, New York