## Applied multivariate statistical analysis |

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### Contents

Matrix Algebra and Random Vectors | 35 |

Sample Geometry and Random Sampling | 88 |

Sample Mean and Covariance Matrix | 95 |

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approximately axes bivariate normal calculate canonical correlations canonical variates clusters columns confidence intervals Consider Construct coordinates correlation coefficient corresponding determined dimensions eigenvalues eigenvectors ellipse ellipsoid equal Equation Example Exercise F-distribution factor analysis factor loadings factor model factor scores Figure function given independent interpretation least squares length likelihood ratio linear combinations linear regression MANOVA maximizes maximum likelihood estimates measurements methods misclassification multivariate normal multivariate normal distribution normal distribution normal population observations obtained orthogonal pairs parameters positive definite predictor variables prior probabilities procedure Q-Q plot r-i r-i random sample random variables random vector regression model reject H0 residual response Result rM rM rM rotated sample canonical sample covariance matrix sample mean sample principal components sample variance scatterplot simultaneous confidence intervals squared distance statistical sum of squares Table treatment univariate values Xu X2 zero