CQAS Seminars

 

Analysis of Multidimensional Data

 

This course will focus on the discovery and explanation of latent (or hidden) structures underlying observed multivariate data. The first part of the course concentrates on methods such as cluster analysis, principal components analysis and factor analysis, all intended for analyzing data measured on a continuous scale. The second part of the course is devoted to the analysis of categorical data using methods such as dual scaling and correspondence analysis. Examples are taken from the physical and engineering sciences, as well as the marketing and business applications. 2.1 CEUs.

 

Topics include:

• The structure of multivariate data
• Principal components analysis
• Factor analysis
• Cluster analysis
• Correspondence analysis
• Dual scaling

 

Who should attend

 

Quality professionals, engineers and statisticians with a basic understanding of univariate statistics who want to gain an appreciation for the complex, interrelated nature of data with many variables.

 

Cost

 

$895. Cost includes a copy of the softcover book Dual Scaling in a Nutshell by Nishisato & Nishisato. Also included is continental breakfast and lunch each day.

 

Meet your instructors

 

Steven M. LaLonde recently joined CQAS as an assistant professor after twelve years at Eastman Kodak Company, where he applied statistical methods in such varied areas as the chemical optimization of KodaColor film for manufacturability; the measurement of worldwide customer satisfaction as applied to management compensation systems; and application of market research methods to evaluate marketing decisions involving product design, pricing and promotion strategy. Steve is a member of ASA, ASQ and AMA.

 

Daniel R. Lawrence is an associate professor in the Graduate Statistics Department at The John D. Hromi Center for Quality and Applied Statistics. His areas of interest include multivariate statistics and, specifically, the applications and underlying theory of methods for quantifying qualitative measures. Dan holds a Ph.D. in Measurement Statistics from the University of Toronto, a MS in Applied Statistics from RIT, and an undergraduate degree in Mathematics from the University of Akron (Ohio).