THE USE OF CANONICAL PRINCIPLE AND FACTORS ANALYSIS

Abstract

In this research the question of dimension reducing procedures are of interest, comprises canonical correlation, which investigates the correlation between linear combinations of variables, and principal component analysis that reduces the set of measurement characteristics to fewer components. Factor analysis performs the same reduction but assuming that the observations have an underlying structure. The auxiliary of principal component is taken for a more parsimonious data representation. The deficiencies of principal component analysis are overcome. Rotation of factors application is discussed.