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 379
... , in many investigations the ɛ , tend to be combinations of measurement error and factors that are uniquely associated with the individual variables . E ( ε ) = 0 4/2 0 0 , Sec . 9.2 The Orthogonal Factor Model 379 The Orthogonal Factor ...
... , in many investigations the ɛ , tend to be combinations of measurement error and factors that are uniquely associated with the individual variables . E ( ε ) = 0 4/2 0 0 , Sec . 9.2 The Orthogonal Factor Model 379 The Orthogonal Factor ...
Page 384
... factor model proceeds by imposing conditions that allow one to uniquely estimate L and ч . The loading matrix is then rotated ( multiplied by an orthogonal matrix ) , where the rotation is determined by some " ease - of- interpretation ...
... factor model proceeds by imposing conditions that allow one to uniquely estimate L and ч . The loading matrix is then rotated ( multiplied by an orthogonal matrix ) , where the rotation is determined by some " ease - of- interpretation ...
Page 431
... factor model , calculate the loading matrix L and matrix of specific variances using the principal component solution method . Compare the results with those in Exercise 9.1 . ( b ) What proportion of the total population variance is ...
... factor model , calculate the loading matrix L and matrix of specific variances using the principal component solution method . Compare the results with those in Exercise 9.1 . ( b ) What proportion of the total population variance is ...
Contents
Matrix Algebra and Random Vectors | 35 |
Sample Geometry and Random Sampling | 88 |
35335 | 95 |
Copyright | |
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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