Multivariate Descriptive Statistical Analysis: Correspondence Analysis and Related Techniques for Large Matrices |
Contents
Descriptive Principal Components Analysis and Singular | 1 |
AnalysisFinding the Best | 43 |
Canonical Analysis and Discriminant | 63 |
Copyright | |
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Common terms and phrases
absolute contribution active advantages aggregated algorithm analyzed approximation axis calculated CALL canonical analysis center of gravity Chapter chi-squared chi-squared distribution classification clusters coding computational configuration contingency table CONTINUE coordinates correlation coefficient correspondence analysis covariance cross-tabulating data matrix defined diagonal DIMENSION discriminant analysis distribution eigenvalues eigenvector elements equation example factor Figure FORMAT graph groups H₁ hierarchy ICARD individuals interpretation iterated power ITOT JBASE KFIN largest eigenvalue Lemma linear combinations method Minimum Spanning Tree multiple correspondence analysis Multiple discriminant analysis Multivariate NACT NBAND NFAC NGUS NLEG nodes NPAGE NQEXA NSAV number of variables NVAR NVIDI observations obtained parameters partition percentages of variance PJ(J principal axes principal components analysis projection rank reading relationship rows and columns sample Section set of points space Statistical Stochastic stochastic approximation SUBROUTINE subspace supplementary variables symmetric symmetric matrix techniques tion tree ultrametric distance vector WRITE IMP λα Φα