Multivariate Analysis focuses on the most essential tools for analyzing compositional and/or multivariate data sets that often emerge when performing geochemical analysis. The chapter starts by introducing groundwater contamination in one of the world’s largest agricultural areas: the Central Valley of California. The goal is to use data science to discover the processes that caused contaminations, whether geogenic or anthropogenic. Knowing these causes aids deciding on mitigation actions. The reader will take a path of discovery through several protocols of applying data-scientific tools to unmask the processes, including principal component analysis, multivariate outlier detection and factor analysis. The key to using these tools is to understand the compositional nature of geochemical datasets, and how compositions need to be treated appropriately to draw meaningful conclusions, a field termed compositional data analysis. This chapter emphasizes the need for data scientists to work with domain experts.
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