Applied Multivariate Statistical AnalysisAspects of mulyivariate analysis; Matrix algebra and random vectors; Sampling geometry and random sampling; The multivariate normal distribution; Inferences about a mean vector; Comparisons of several multivariate means; Multivariate linear regression models; Analysis of covariance structure: principal components; Factor analysis and inference structured covarience matrices; Canonical correlation analysis; Classification and grouping techniques; Discrimination and classification; Clustering. |
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Page 180
... determined from the eigenvalues X , and eigenvectors e ; of S. As in ( 4-7 ) , the direction and lengths of the axes of n ( x − μ ) ' S ̄ ' ( x - μ ) ≤ c2 = are determined by going p ( n - 1 ) F. ( n − p ) - Fp.n - p ( a ) - √ác ...
... determined from the eigenvalues X , and eigenvectors e ; of S. As in ( 4-7 ) , the direction and lengths of the axes of n ( x − μ ) ' S ̄ ' ( x - μ ) ≤ c2 = are determined by going p ( n - 1 ) F. ( n − p ) - Fp.n - p ( a ) - √ác ...
Page 371
... determined by b . The second principal component minimizes the same quantity among all vectors perpendicular to the first choice . EXERCISES 8.1 . Determine the population principal components Y , and Y2 for the covariance matrix 5 2 Σ ...
... determined by b . The second principal component minimizes the same quantity among all vectors perpendicular to the first choice . EXERCISES 8.1 . Determine the population principal components Y , and Y2 for the covariance matrix 5 2 Σ ...
Page 481
... determination can be made only at the end of several years of training . 2. " Perfect " information requires destroying object . Example : The life length of a calculator battery is determined by using it until it fails and the strength ...
... determination can be made only at the end of several years of training . 2. " Perfect " information requires destroying object . Example : The life length of a calculator battery is determined by using it until it fails and the strength ...
Contents
Matrix Algebra and Random Vectors | 35 |
Sample Geometry and Random Sampling | 88 |
35335 | 95 |
Copyright | |
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Common terms and phrases
approximately axes calculate canonical correlations canonical variates chi-square chi-square distribution classification clusters confidence intervals confidence region correlation coefficient correlation matrix corresponding cross-products density determined discriminant eigenvalues eigenvectors ellipse ellipsoid Equation error Example Exercise F-distribution factor analysis factor loadings Figure function given H₁ large sample length likelihood ratio likelihood ratio test linear combinations MANOVA maximum likelihood estimates mean vector measurements multivariate normal n₁ n₂ normal distribution normal population observations obtained P₁ pairs parameters population mean Q-Q plots random sample random variables random vector regression model reject residual response Result rotated S₁ sample correlation sample covariance matrix sample mean sample variance scatterplot simultaneous confidence intervals Spooled squared distance statistical sum of squares Table treatment univariate V₁ values X₁ X₂ Y₁ Y₂ Z₁ zero μ₁ μ₂ μι Σ Σ