## Multivariate Descriptive Statistical Analysis: Correspondence Analysis and Related Techniques for Large Matrices |

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### Contents

Canonical Analysis and Discriminant | 63 |

Multiple Correspondence Analysis | 81 |

Automatic ClassificationClustering Techniques Used | 109 |

Copyright | |

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### Common terms and phrases

active advantages aggregated algorithm allows Applications approximation axes axis calculated CALL centers Chapter classification clusters coding coefficient columns computational consider contains contingency table CONTINUE coordinates correlation correspondence analysis defined described descriptive designates diagonal DIMENSION distance distribution eigenvalues eigenvector elements equal equation example factor Figure FORMAT give given graph groups ICARD individuals interpretation iteration JBASE linear matrix maximum mean measurements method multiple NACT NBAND normal Note objects observations obtained origin partition percentages performed points position possible presented principal axes principal components analysis problem procedure projection quantity questions rank reading relationship relative represent respect response rows sample space square Statistical step subspace supplementary symmetric matrix Table techniques term tion TRACE tree variables variance vector weight WRITE IMP written