DEPARTMENT OF STATIS TICS
COLLEGE OF ARTS & SCIENCES
330.972.6886 (TELEPHONE)
Course Description
3470:667
Prerequisite:
Factor Analysis
3 credits
3470:461/561 Applied Statistics or equivalent.
Course Description: Theory and techniques for identifying variables through use of
principal components and factor analysis. Identification of groups using cluster
analysis. Computer applications.
Course Topics:
Introduction to factor analysis and principal components analysis, terminology
Factor models and component models, notation, parsimony principles
Theorems on correlations, Spearman’s method of factor extraction, Decomposition
of the variance of an original variable
Two-step procedures
Centroid method
Introduction to rotation
Factor models in matrix form
Communality estimation
Principal-axes factor extraction
Number of common factors extracted
Other methods of factor extractions (maximum likelihood, minres, alpha factor
analysis, image analysis)
Principal components analysis
Component scores
Factor scores
Rotation and simple structure
Orthogonal rotations
Oblique rotations
Scores on rotated principal components
Scores on rotated factors
Introduction to cluster analysis
Cluster analysis of variables
Cluster Analysis of individuals
Fall 2014