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Compositional Data Analysis in Practice: A Comprehensive Guide to Statistical Techniques for Compositional Data

Lee Simmons

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Published in Compositional Data Analysis In Practice (Chapman Hall/CRC Interdisciplinary Statistics)

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Compositional data is a type of data that is composed of parts that sum to a constant. This type of data is often encountered in fields such as geology, environmental science, and economics. For example, in geology, compositional data might be used to represent the composition of a rock, which is composed of different minerals that sum to 100%. In environmental science, compositional data might be used to represent the composition of a water sample, which is composed of different chemicals that sum to 100%. In economics, compositional data might be used to represent the composition of a portfolio, which is composed of different stocks that sum to 100%.

Compositional Data Analysis in Practice (Chapman & Hall/CRC Interdisciplinary Statistics)

by Gail Buckland

4.3 out of 5

Language	:	English
File size	:	29506 KB
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Print length	:	136 pages
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Compositional data analysis is a statistical technique that is used to analyze compositional data. Compositional data analysis techniques are designed to take into account the unique properties of compositional data, such as the fact that the parts of a compositional data set always sum to a constant.

Statistical Techniques for Compositional Data Analysis

There are a number of different statistical techniques that can be used to analyze compositional data. Some of the most common techniques include:

* Simplex plots: Simplex plots are graphical representations of compositional data. They are used to visualize the relationships between the different parts of a compositional data set. * Aitchison distance: The Aitchison distance is a measure of the dissimilarity between two compositional data sets. It is used to compare the compositions of different samples. * Log-ratio analysis: Log-ratio analysis is a statistical technique that is used to transform compositional data into a form that is more amenable to statistical analysis. * Correspondence analysis: Correspondence analysis is a statistical technique that is used to analyze the relationships between different parts of a compositional data set.

Examples of Compositional Data Analysis in Practice

Compositional data analysis is used in a wide variety of fields, including geology, environmental science, and economics. Here are a few examples of how compositional data analysis is used in practice:

* In geology, compositional data analysis is used to: * Classify rocks based on their mineral composition * Determine the provenance of sediments * Model the evolution of the Earth's crust * In environmental science, compositional data analysis is used to: * Assess the quality of water and air * Monitor the impact of pollution on the environment * Develop remediation strategies for contaminated sites * In economics, compositional data analysis is used to: * Analyze the composition of portfolios * Assess the risk of investments * Develop economic models

Compositional data analysis is a powerful statistical technique that can be used to analyze a wide variety of data types. Compositional data analysis techniques are designed to take into account the unique properties of compositional data, such as the fact that the parts of a compositional data set always sum to a constant. Compositional data analysis is used in a wide variety of fields, including geology, environmental science, and economics.

Compositional Data Analysis in Practice (Chapman & Hall/CRC Interdisciplinary Statistics)

by Gail Buckland

4.3 out of 5