Cluster Analysis Cluster analysis attempts to address a very different problem from a different point of view when compared with discriminant analysis. Given a set of observations we want to form smal
Principal Component Analysis: Linear reduction technique.
Example (Johnson and Wichern):
Weekly return of five stocks (Allied Chemical, du Pont, Union Carbide, Exxon, Texaco). Let x1 , x 2 ,.x5 denote
Vector Space A vector consisting of m elements may be regarded geometrically as a point in mdimensional space.
A vector consisting of p elements can also be regarded geometrically as a line from the o
Factor Analysis
The factor analysis model assumes that there is a smaller set of uncorrelated variables (underlying factors or underlying characteristics) that will give a better understanding of the
Discriminant Analysis Discrimination and classification are multivariate techniques concerned with separating distinct sets of observations (objects) and with allocating new observations (objects) to
Inferences about a mean vector Readings from Johnson 10.2 The Central Limit Theorem: Let ( x1 , x2 ,.xN ) be independent observations for a population with mean and variance covariance Then:
N ( ) is
Canonical Correlation Analysis The objective of CCA is to identify and measure the association between two sets of random variables using a specific matrix function of the variance-covariance matrix o