Multivariate AnalysisTextbook on the theoretics and methodology of multivariate analysis. |
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Introduction page | 1 |
Principal Components | 13 |
Classification and Clustering | 31 |
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a₁ ANOVA average c₁ canonical correlations Chapter cluster analysis coefficients column component analysis consider contingency table correlation matrix corresponding covariance matrix criterion defined degrees of freedom determinant deviances diagonal dimensions discrimination distance distribution eigenvalues eigenvectors equal equations error example fact factor analysis frequencies geometrical given gives H₁ hypothesis independent individual instrumental variables Kendall likelihood likelihood function linear function loadings means measure methods misclassification multivariate analysis multivariate normal n₁ n₂ Not-C observations orthogonal P₁ parameters percent polytomy populations principal component analysis principal components problem procedure random rank ratio regressand regression regressors relation require residual S₂ sample scores Sepal Sepal length significant situation statistics sum of squares Suppose transformation uncorrelated unit variance univariate Up+1 values vanish variables variation vector versicolor virginica x₁ and x2 zero