Summary and Generalizations of Multivariate Quantitative Procedures

Research problems lend themselves to analysis by a number of multivariate statistical applications. This text has attempted to address some of the applications and underlying basis for choosing a procedure or technique. Scientific parsimony is the essence of scientific reasoning and should always be adhered to. The explained variance in a dataset is a significant aspect of any structural analysis whether it be the explained variance of a row, explained variance of a group of measurements, or the explained variance of a factor or column. In any situation, if we can reduce the number of “explainer” variables or reduce the number of measurements to attain a true value or estimate, we have fulfilled the task of multivariate statistics. The examples chosen for review represent a broad spectrum of methodologies and techniques, by which scientific data can be parsimoniously analysed using model building and model fitting strategies.