多變數分析在農業科技之應用

Abstract

農業科技上常會針對某研究主題同時測量一大堆不同變數（調查性狀）的資料，但我們並非針對個別變數進行統計分析，而是將所有變數合起來共同討論，針對這樣資料的統計分析，就需要用到多變數分析技術。本文試以不涉及統計理論從實用角度出發，針對農業科技研究上常用的多變數分析法，以實例分析說明其意義、應用場合、分析流程及結果解釋，期能有助於農業科技研究之參考。這些多變數分析方法包括主成份分析、對應分析因素分析、集群分析、判別分析、路徑分析及典型相關分析。 Data containing many variables (measured characters) are often collected in agricultural science research. An interpretative analysis of multivariate data considers all the variables simultaneously is required, and entails the use of multivariate statistical analysis. This paper provides a non-statistical, practical overview of the commonly used multivariate methods in agricultural science research, including principal component analysis, correspondence analysis, factor analysis, cluster analysis, discriminant analysis, path analysis, and canonical correlation analysis. The meaning, applicability, analytical procedures, and results interpretation of these methods are presented with cited examples. Our objective is to provide the researcher an intuitive understanding of multivariate analysis and their applications in agriculture.