Environmental Data Analysis with MatLab

William Menke, Joshua Menke

• Publisher: Elsevier
• Year: 2009
• Print ISBN: 9780123918864
• eBook ISBN: 9780123918871
• Topic: Mathematics
• Subtopic: Applied Mathematics
• Index: Mathematics

Environmental Data Analysis with MatLab is a reference work designed to teach students and researchers the basics of data analysis in the environmental sciences using MatLab, and more specifically how to analyze data sets in carefully chosen, realistic scenarios. Although written in a self-contained way, the text is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial, available at the author's website: http://www.ldeo.columbia.edu/users/menke/edawm/index.htm.This book is organized into 12 chapters. After introducing the reader to the basics of data analysis with MatLab, the discussion turns to the power of linear models; quantifying preconceptions; detecting periodicities; patterns suggested by data; detecting correlations among the data; filling in missing data; and determining whether your results are significant. Homework problems help users follow up upon case studies.This text will appeal to environmental scientists, specialists, researchers, analysts, and undergraduate and graduate students in Environmental Engineering, Environmental Biology and Earth Science courses, who are working to analyze data and communicate results.- Well written and outlines a clear learning path for researchers and students- Uses real world environmental examples and case studies- MatLab software for application in a readily-available software environment- Homework problems help user follow up upon case studies with homework that expands them

1. Data analysis with MatLab

1.1 Why MatLab?1

1.2 Getting started with MatLab3

1.3 Getting organized3

1.4 Navigating folders4

1.5 Simple arithmetic and algebra5

1.6 Vectors and matrices7

1.7 Multiplication of vectors of matrices7

1.8 Element access8

1.9 To loop or not to loop9

1.10 The matrix inverse11

1.11 Loading data from a file11

1.12 Plotting data12

1.13 Saving data to a file13

1.14 Some advice on writing scripts13

Problems15

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?

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 supporting real data analysis.

The key decision is whether to go with a spreadsheet or a scripting language-type software environment. Both are viable environments for computer-based data analysis. Stable implementations of both are available for most types of computers from commercial software developers at relatively modest prices (and especially for those eligible for student discounts). Both provide support for the data analysis itself, as well as associated tasks such as loading and writing data to and from files and plotting them on graphs. Spreadsheets and scripting languages are radically different in approach, and each has advantages and disadvantages.

In a spreadsheet-type environment, typified by Microsoft Excel, data are presented as one or more tables. Data are manipulated by selecting the rows and columns of a table and operating on them with functions selected from a menu and with formulas entered into the cells of the table itself. The immediacy of a spreadsheet is both its greatest advantage and its weakness. You see the data and all the intermediate results as you manipulate the table. You are, in a sense, touching the data, which gives you a great sense of what the data are like. More of a problem, however, is keeping track of what you did in a spreadsheet-type environment, as is transferring useful procedures from one spreadsheet-based dataset to another.

In a scripting language, typified by The MathWorks MatLab,...

Table of contents

• Cover image
• Table of Contents
• Front matter
• 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. Are my results significant?
• 12. Notes
• Index