JSM 2005 Online Program

Abstract #303733

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	330
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Quality and Productivity
Abstract - #303733
Title:	Large Common-cause Variation in Multivariate SPC
Author(s):	John Young*+ and Robert L. Mason and Youn-Min Chou
Companies:	McNeese State University and Southwest Research Institute and The University of Texas at San Antonio
Address:	1750 Bilbo Street, Lake Charles, LA, 70601, United States
Keywords:	Multivariate Statistical Process Control ; Special-Cause Variation
Abstract:	Process variation usually is categorized as either common-cause or special-cause variation. Common-cause variation is considered to be variation inherent to a process as opposed to special-cause variation, which consists of variation induced by special causes acting on the process. Although common-cause variation usually is considered to be small in size relative to special-cause variation, there exist situations where the inherent process variation contains large components. In these latter situations, many statistical process control procedures often do not perform well because they have been designed for monitoring processes containing only small common-cause variation. The purpose of this paper is to identify the characteristics of large common-cause variation and discuss what can be done when it is present in a process. We give attention to the usefulness of the T-Square statistic in monitoring processes containing such variation.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program