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. |
From inside the book
Results 1-3 of 82
Page 88
Richard Arnold Johnson, Dean W. Wichern. Sample Geometry and Random Sampling 3.1 INTRODUCTION With the vector concepts introduced in the previous ... SAMPLE A single multivariate observation 3 Sample Geometry and Random Sampling Introduction.
Richard Arnold Johnson, Dean W. Wichern. Sample Geometry and Random Sampling 3.1 INTRODUCTION With the vector concepts introduced in the previous ... SAMPLE A single multivariate observation 3 Sample Geometry and Random Sampling Introduction.
Page 96
... random variables . In this context , let the ( i , j ) entry in the data matrix be the random variable X. Each set of measurements X ; on p variables is a random vector and we have the random matrix X ... Sample Geometry and Random Sampling.
... random variables . In this context , let the ( i , j ) entry in the data matrix be the random variable X. Each set of measurements X ; on p variables is a random vector and we have the random matrix X ... Sample Geometry and Random Sampling.
Page 231
... random sample of size n , from a population with mean μe , l = 1 , 2 , . . . , g . The random samples from different populations are independent . 2. All populations have a common covariance matrix Σ . 3. Each population is multivariate ...
... random sample of size n , from a population with mean μe , l = 1 , 2 , . . . , g . The random samples from different populations are independent . 2. All populations have a common covariance matrix Σ . 3. Each population is multivariate ...
Contents
Matrix Algebra and Random Vectors | 35 |
Sample Geometry and Random Sampling | 88 |
35335 | 95 |
Copyright | |
13 other sections not shown
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