Extension of biplot methodology to multivariate regression analysis

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.

Automated summary

Multivariate statistics focuses on exploring relationships between different sets of variables, with regression analysis revealing the impact of predictor variables on response variables. The biplot allows for a visual representation of samples, predictor variables, and response variables in a single graph.