Multivariate statistics in the analytical laboratory (1): an introduction

Modern analytical techniques can harvest large amounts of multi-analyte data from multiple sample materials in extremely short periods. Such methods offer much more than major gains in efficiency, cost and time. They can yield information not otherwise available - classification, discrimination, cluster analysis and pattern recognition. Multivariate regression methods are also widely used. All these applications are available in software packages and are readily implemented. The calculations use matrix algebra, but here we outline the basic principles that underpin some of the methods, and show the types of information available. Here we provide an introduction to multivariate statistics.