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**Chapter 2** 

© 2012 Böhm et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Böhm et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Application of Multivariate** 

Additional information is available at the end of the chapter

K. Böhm, E. Smidt and J. Tintner

http://dx.doi.org/10.5772/53975

**1. Introduction** 

choose.

**Data Analyses in Waste Management** 

First of all, what is multivariate data analysis and why is it useful in waste management?

Methods dealing with only one variable are called univariate methods. Methods dealing with more than one variable at once are called multivariate methods. Using univariate methods natural systems cannot be described satisfactorily. Nature is multivariate. That means that any particular phenomenon studied in detail usually depends on several factors. For example, the weather depends on the variables: wind, air pressure, temperature, dew point and seasonal variations. If these factors are collected every day a multivariate data matrix is generated. For interpretation of such data sets multivariate data analysis is useful. Multivariate data analysis can be used to process information in a meaningful fashion. These methods can afford hidden data structures. On the one hand the elements of measurements often do not contribute to the relevant property and on the other hand hidden phenomena are unwittingly recorded. Multivariate data analysis allows us to handle huge data sets in order to discover such hidden data structures which contributes to a better understanding and easier interpretation. There are many multivariate data analysis techniques available. It depends on the question to be answered which method to

Due to the requirement of representative sampling number of samples and analyses in waste management lead to huge data sets to obtain reliable results. In many cases extensive data sets are generated by the analytical method itself. Spectroscopic or chromatographic methods for instance provide more than 1000 data points for one sample. Evaluation tools can be developed to support interpretation of such analytical methods for practical applications. For specific questions and problems different evaluation tools are necessary.

Calculation and interpretation are carried out by the provided evaluation tool.


**Chapter 2** 
