Quantitative Methods of Data Analysis for the Physical Sciences and Engineering

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.

Table of Contents

Part I: Fundamentals
Chapter 1 The Nature Of Data And Analysis
Chapter 2 Probability Theory
Chapter 3 Statistics

Part II: Fitting Curves to Data
Chapter 4 Interpolation
Chapter 5 Smoothed Curve Fitting
Chapter 6 Special Curve Fitting

Part III: Sequential Data Fundamentals
Chapter 7 Serial Products
Chapter 8 Fourier Series
Chapter 9 Fourier Transform
Chapter 10 Fourier Transform
Chapter 11 Spectral Analysis
Chapter 12 Cross-Spectral Analysis
Chapter 13 Filtering And Deconvolution
Chapter 14 Linear Parametric Modeling
Chapter 15 Empirical Orthogonal Function (Eof) Analysis

Appendix 1. Overview of Matrix Algebra
Appendix 2. Uncertainty Analysis