Environmental Data Analysis with MatLab is for students and researchers working to analyze real data sets in the environmental sciences. 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. This book 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. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial.

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Key Features

  • 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


Environmental scientists, specialists, researchers, analysts, undergraduate and graduate students in Environmental Engineering, Environmental Biology and Earth Science, who are working to analyze data and communicate results

Table of Contents



Advice on scripting for beginners

1. Data analysis with MatLab

1.1. Why MatLab?

1.2. Getting started with MatLab

1.3. Getting organized

1.4. Navigating folders

1.5. Simple arithmetic and algebra

1.6. Vectors and matrices

1.7. Multiplication of vectors of matrices

1.8. Element access

1.9. To loop or not to loop

1.10. The matrix inverse

1.11. Loading data from a file

1.12. Plotting data

1.13. Saving data to a file

1.14. Some advice on writing scripts

2. A first look at data

2.1. Look at your data!

2.2. More on MatLab graphics

2.3. Rate information

2.4. Scatter plots and their limitations

3. Probability and what it has to do with data analysis

3.1. Random variables

3.2. Mean, median, and mode

3.3. Variance

3.4. Two important probability density functions

3.5. Functions of a random variable

3.6. Joint probabilities

3.7. Bayesian inference

3.8. Joint probability density functions

3.9. Covariance

3.10. Multivariate distributions

3.11. The multivariate Normal distributions

3.12. Linear functions of multivariate data

4. The power of linear models

4.1. Quantitative models, data, and model parameters

4.2. The simplest of quantitative models

4.3. Curve fitting

4.4. Mixtures

4.5. Weighted averages

4.6. Examining error

4.7. Least squares

4.8. Examples

4.9. Covariance and the behavior of error

5. Quantifying preconceptions

5.1. When least square fails

5.2. Prior information

5.3. Bayesian inference

5.4. The product of Normal probability density distributions

5.5. Generalized least squares

5.6. The role of the covariance of the dat


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© 2012
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About the authors

William Menke

William Menke is a Professor of Earth and Environmental Sciences at Columbia University, USA. His research focuses on the development of data analysis algorithms for time series analysis and imaging in the earth and environmental sciences and the application of these methods to volcanoes, earthquakes and other natural hazards.

Joshua Menke

Joshua Menke is a software engineer and principal of JOM Associates. His specialty is in the design and implementation of parallel processing systems for matching and correlation of large volumes of data in order to identify and quantify trends and patterns that can assist manufacturers and retailer better serve their clientele.