000			01369nam a2200193Ia 4500
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008			160802s9999 xx 000 0 und d
020			_a9780470689189
041			_aeng
082			_a519.535 _bCOX-I
100			_aCox, Trevor _932091
245			_aIntroduction to multivariate data / _cby Trevor Cox
260			_bWiley-Blackwell Publishing, _c2014.
300			_avii, 232 p.
505			_a1.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.
520			_aMultivariate 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.
650			_aRES _933629
942			_cBK