Multivariate Techniques for Water Quality Analysis

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Abstract
The objective of this study was to demonstrate the application of multivariate statistical techniques towards the understanding of variables affecting water quality. One of the major problems confronting an investigator in the application of statistical techniques to water quality data is missing observations. A simultaneous multiple regression method is presented in this paper for estimating missing observations in a data matrix. The major goals of using multivariate analysis are to facilitate interpretation and to prove hypotheses concerning the data. The techniques considered in this paper include principal components, canonical correlation, partial correlation, multivariate analysis of variance (MANOVA), and discriminant analysis. Examples are presented demonstrating the application of these methods.

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