Principal+Components

Principal Components (Multivariate Methods) (GWU EMSE-271)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | Nomenclature | Concepts

"Principal components analyis is a method that can be used to reduce the dimensionality of mulitvariate data." -Lattin

"**Principal component analysis** (**PCA**) involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible." - [|Wikipedia]

Tables and Symbols ([|Wikipedia])

Properties and limitations of PCA ([|Wikipedia])

Rules of Thumb
 * See Principal components analyis check list for Scree Plot, Kaiser's Rule
 * TBD


 * Sources:**
 * Analyzing Multivariate Data, by James Lattin, Douglas Carroll and Green ([|Amazon]), page 9
 * Principal component analysis. (2009, November 28). In //Wikipedia, The Free Encyclopedia//. Retrieved 19:50, December 1, 2009, from []
 * EMSE 271, Fall 2009
 * [|Wikibooks: Statistics/Multivariate Data Analysis/Principal Component Analysis]