Introduction to multivariate data /
by Trevor Cox
- Wiley-Blackwell Publishing, 2014.
- vii, 232 p.
1.Introduction-- 2. Matrix algebra-- 3.Basic multivariate statistics-- 4.Graphical representation of multivariate data-- 5. Principal components analysis-- 6. Biplots-- 7.Correspondence analysis-- 8.Cluster analysis-- 9. Multidimensional scaling-- 10. Linear regression analysis-- 11.Multivariate analysis of variance-- 12.Canonical correlation analysis-- 13.Discriminant analysis and canonical variates analysis-- 14. Loglinear modelling-- 15.Factor analysis-- 16.Other latent variable models. Graphical modelling. Data mining.
Multivariate data appear in all scientific fields of investigation and the study of multivariate analysis has become central to the discipline of data science. This title explains how to successfully reduce a set of data with many variables to a manageable formulation where information, structure and underlying patterns are more clearly revealed.