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 121
... univariate normal density to p≥ 2 dimensions . Recall the univariate normal distribution , with mean μ and variance o2 , has the probability density function 1 f ( x ) = e - [ ( x - μ ) / σ ] 2 / 2 - < x < ∞ ( 4-1 ) A plot of this ...
... univariate normal density to p≥ 2 dimensions . Recall the univariate normal distribution , with mean μ and variance o2 , has the probability density function 1 f ( x ) = e - [ ( x - μ ) / σ ] 2 / 2 - < x < ∞ ( 4-1 ) A plot of this ...
Page 146
... univariate and bivariate ex- aminations of normality . We can never be sure that we have not missed some feature ... Univariate Marginal Distributions Dot diagrams for smaller n and histograms for n > 25 , or so , help reveal situations ...
... univariate and bivariate ex- aminations of normality . We can never be sure that we have not missed some feature ... Univariate Marginal Distributions Dot diagrams for smaller n and histograms for n > 25 , or so , help reveal situations ...
Page 237
... univariate result , the hypothesis of no treatment effects = Ho T1 = T2 = · = Tg = 0 is tested by considering the relative sizes of the treatment and residual sums of squares and cross - products . Equivalently , we may consider the ...
... univariate result , the hypothesis of no treatment effects = Ho T1 = T2 = · = Tg = 0 is tested by considering the relative sizes of the treatment and residual sums of squares and cross - products . Equivalently , we may consider the ...
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