Throughout the book, the computer is used as an adjunct to the presentation of a multivariate statistical method in an empirically oriented approach.
Basically, the model adopted in this book is to first present the theory of a multivariate statistical method along with the basic mathematical computations necessary for the analysis of data. Subsequently, a real world problem is discussed and an example data set is provided for analysis.
Throughout the presentation and discussion of a method, many references are made to the computer, output are explained, and exercises and examples with real data are included. The Multivariate Normal Distribution. Factorial Multivariate Analysis of Variance.
Discriminant Analysis. Principal Components andFactorAnalysis.
The Multivariate Normal Distribution. Parameter Estimation.
Discrimination and Classification Round 1. Statistical Inference for Means.
Linear Principal Component Analysis. Selected Results From Matrix Algebra.
follow A1 Partitioned Matrices. A2 Positive Definite Matrices.
A3 The Cholesky Decomposition. A4 Vector and Matrix Differentiation.
A5 Eigenvectors and Eigenvalues. A6 Spectral Decomposition of Symmetric Matrices.
Why Multivariate Statistics? Normal Mixtures. Applied Multivariate Analysis Neil H.