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典型相关分析(canonical correlation analysis)就是利用综合变量对之间的相关关系来反映两组指标之间的整体相关性的多元统计分析方法。其基本原理是:为了从总体上把握两组指标之间的相关关系,分别在两组变量中提取有代表性的两个综合变量U1和V1(分别为两个变量组中各变量的线性组合),利用这两个综合变量之间的相关关系来反映两组指标之间的整体相关性。其条件是:两组变量都是连续变量,资料都必须服从多元正态分布。其基本程序是:从两组变量各自的线性函数中各抽取一个组成一对,相关系数达到最大值的一对称为第1对典型变量,类似地
Canonical correlation analysis is a method of multivariate statistical analysis that uses the correlation between the pairs of variables to reflect the overall correlation between the two sets of indicators. The basic principle is: In order to grasp the relationship between the two sets of indicators as a whole, two representative variables, namely, U1 and V1, are extracted respectively from the two sets of variables (respectively, the linear combination of variables in the two variables ), Using the correlation between the two synthetic variables to reflect the overall correlation between the two sets of indicators. The conditions are: two groups of variables are continuous variables, the data must be subject to multivariate normal distribution. The basic procedure is: from the two linear functions of each variable to extract a pair, the correlation coefficient reaches a maximum of a pair of the first pair of typical variables, and similarly