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.

中文翻译：

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分析实验室中的多元统计（1）：简介

现代分析技术可以在极短的时间内从多种样品材料中收集大量的多分析物数据。这样的方法在效率，成本和时间上提供的好处远远超过主要的收获。它们可以产生其他方式无法获得的信息-分类，区分，聚类分析和模式识别。多元回归方法也被广泛使用。所有这些应用程序均以软件包提供，并且易于实现。计算使用矩阵代数，但在这里我们概述了支持某些方法的基本原理，并显示了可用信息的类型。