## Multivariate AnalysisMultivariate Analysis deals with observations on more than one variable where there is some inherent interdependence between the variables. With several texts already available in this area, one may very well enquire of the authors as to the need for yet another book. Most of the available books fall into two categories, either theoretical or data analytic. The present book not only combines the two approaches but it also has been guided by the need to give suitable matter for the beginner as well as illustrating some deeper aspects of the subject for the research worker. Practical examples are kept to the forefront and, wherever feasible, each technique is motivated by such an example. |

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### Contents

Chapter 1 Introduction | 1 |

Chapter 2Basic Properties of Random Vectors | 26 |

Chapter 3Normal Distribution Theory | 59 |

Copyright | |

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### Common terms and phrases

allocation analysis assume asymptotic called canonical correlation Chapter clusters coefficient columns combination conditional configuration Consider correlation corresponding covariance matrix data matrix defined Definition denote depends diag dimensions direction discriminant distance distribution eigenvalues eigenvectors elements equal equation estimate Example Exercise explained factor Figure function Further given gives groups Hence hypothesis important independent known leads linear maximized mean measure method multivariate normal Note observations obtained orthogonal orthogonal matrix parameters particular points population positive principal component probability problem Proof prove random rank ratio regression rejected represents respectively result rows rule sample sample mean satisfies scale scores similar solution squares standardized statistic Suppose symmetric symmetric matrix Table Theorem tion transformation univariate variables variance variation vector write written zero