Environmental Data Analysis with MatLab

William Menke, Joshua Menke

• Publisher: Academic Press
• Year: 2016
• ISBN:
  9780128045503
• Edition: 2
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

Environmental Data Analysis with MatLab is a new edition that expands fundamentally on the original with an expanded tutorial approach, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. Since publication of the bestselling Environmental Data Analysis with MATLAB®, many advances have been made in environmental data analysis. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often noisy data drawn from a broad range of sources. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios.

MATLAB®, a commercial data processing environment, is used in these scenarios. Significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. This new edition, though written in a self-contained way, is supplemented with data and MATLAB® scripts that can be used as a data analysis tutorial.

New features include boxed crib sheets to help identify major results and important formulas and give brief advice on how and when they should be used. Numerical derivatives and integrals are derived and illustrated. Includes log-log plots with further examples of their use. Discusses new datasets on precipitation and stream flow. Topical enhancement applies the chi-squared test to the results of the generalized least squares method. New coverage of cluster analysis and approximation techniques that are widely applied in data analysis, including Taylor Series and low-order polynomial approximations; non-linear least-squares with Newton's method; and pre-calculation and updating techniques applicable to real time data acquisition.

• Provides a clear learning path for researchers and students using data analysis techniques which build upon one another, choosing the right order of presentation to substantially aid the reader in learning material
• Includes crib sheets to summarize the most important data analysis techniques, results, procedures, and formulas, serving to organize the material in such a way that its sequence is more apparent
• Uses real-world environmental examples and case studies formulated using the readily-available software environment in MATLAB®
• Includes log-log plots with further examples of their use

1

Data analysis with MatLab

Abstract

Chapter 1, Data Analysis with MatLab, is a brief introduction to MatLab as a data analysis environment and scripting language. It is meant to teach the reader barely enough to understand the MatLab scripts in the book and to begin to start using and modifying them. While MatLab is a fully featured programming language, Environmental Data Analysis with MatLab is not a book on computer programming. It teaches scripting mainly by example and avoids long discussions on programming theory.

Keywords

MatLab; script; m-file; variable; matrix; command; function; plot; graph

1.1 Why MatLab? 1

1.2 Getting started with MatLab 3

1.3 Getting organized 3

1.4 Navigating folders 4

1.5 Simple arithmetic and algebra 5

1.6 Vectors and matrices 7

1.7 Multiplication of vectors of matrices 7

1.8 Element access 8

1.9 Representing functions 9

1.10 To loop or not to loop 11

1.11 The matrix inverse 13

1.12 Loading data from a file 13

1.13 Plotting data 14

1.14 Saving data to a file 16

1.15 Some advice on writing scripts 17

Problems 19

1.1 Why MatLab?

Data analysis requires computer-based computation. While a person can learn much of the theory of data analysis by working through short pencil-and-paper examples, he or she cannot become proficient in the practice of data analysis that way—for reasons both good and bad. Real datasets, which are almost always too large to handle manually, are inherently richer and more interesting than stripped-down examples. They have more to offer, but an expanded skill set is required to successfully tackle them. In particular, a new kind of judgment is required for selecting the analysis technique that is right for the problem at hand. These are good reasons. Unfortunately, the practice of data analysis is littered with bad reasons, too, most of which are related to the very steep learning curve associated with using computers. Many practitioners of data analysis find that they spend rather too many frustrating hours solving computer-related problems that have very little to do with data analysis, per se. That's bad, especially in a classroom setting where time is limited and where frustration gets in the way of learning.

One approach to dealing with this problem is to conduct all the data analysis within a single software environment—to limit the damage. Frustrating software problems will still arise, but fewer than if data were being shuffled between several different environments. Furthermore, in a group setting such as a classroom, the memory and experience of the group can help individuals solve commonly encountered problems. The trick is to select a single software environment that is capable of su...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Preface
• Advice on scripting for beginners
• 1: Data analysis with MatLab
• 2: A first look at data
• 3: Probability and what it has to do with data analysis
• 4: The power of linear models
• 5: Quantifying preconceptions
• 6: Detecting periodicities
• 7: The past influences the present
• 8: Patterns suggested by data
• 9: Detecting correlations among data
• 10: Filling in missing data
• 11: “Approximate” is not a pejorative word
• 12: Are my results significant?
• 13: Notes
• Index