Oyedele Opeoluwa F
Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia.
J Appl Stat. 2020 Jun 12;48(10):1816-1832. doi: 10.1080/02664763.2020.1779192. eCollection 2021.
At the core of multivariate statistics is the investigation of relationships between different sets of variables. More precisely, the inter-variable relationships and the causal relationships. The latter is a regression problem, where one set of variables is referred to as the response variables and the other set of variables as the predictor variables. In this situation, the effect of the predictors on the response variables is revealed through the regression coefficients. Results from the resulting regression analysis can be viewed graphically using the biplot. The consequential biplot provides a single graphical representation of the samples together with the predictor variables and response variables. In addition, their effect in terms of the regression coefficients can be visualized, although sub-optimally, in the said biplot.
多元统计的核心是研究不同变量集之间的关系。更确切地说，是变量间关系和因果关系。后者是一个回归问题，其中一组变量被称为响应变量，另一组变量被称为预测变量。在这种情况下，预测变量对响应变量的影响通过回归系数来揭示。所得回归分析的结果可以使用双标图以图形方式查看。由此产生的双标图提供了样本与预测变量和响应变量的单一图形表示。此外，尽管在上述双标图中效果不是最佳，但它们在回归系数方面的影响可以可视化。
