Multivariate Descriptive Statistical Analysis: Correspondence Analysis and Related Techniques for Large Matrices |
Contents
Descriptive Principal Components Analysis and Singular | 1 |
Correspondence Analysis | 30 |
Canonical Analysis and Discriminant | 63 |
Copyright | |
7 other sections not shown
Other editions - View all
Common terms and phrases
advantages aggregated algorithm analyzed axis Benzecri calculated canonical analysis center of gravity Chapter chi-squared chi-squared distribution classification coding computation configuration contingency table CONTINUE coordinates correlation coefficient correspondence analysis covariance matrix criterion cross-tabulating data matrix defined diagonal DIMENSION distribution edges eigenvalues eigenvector elements equation example explained variance F₁ factor Figure FORMAT graph H₁ H₂ hierarchy hypothesis of independence ICARD individuals inequality interpretation iterated power ITOT JBASE k₁ KLAC largest eigenvalue Lemma linear method Minimum Spanning Tree moving centers Multivariate NACT NBAND NFAC NGUS NLEG nodes NQEXA NSAV number of groups number of variables NVAR NVIDI observations obtained parameters partition percentages of variance principal axes principal axes analysis principal components analysis projection rank reading rows and columns sample Section single linkage space standard deviations statistical step stochastic approximation SUBROUTINE subspace supplementary variables symmetric techniques tion ultrametric distance vector WRITE IMP