|
QICID: 29812
Title: Identify Outliers, Understand the Process
Copyright: ASQ
Author: Paulk, Mark C.; LaScola Needy, Kim; Rajgopal, Jayant
Organization: Carnegie Mellon University; University of Pittsburgh
Subject: Control charts; X-bar and R control charts; Cause and effect analysis; Outliers; Software quality assurance (SQA); Statistical process control (SPC); Statistical methods;
Series: Software Quality Professional, Vol. 11, No. 2, March 2009, pp. 28-37
This ARTICLE is available for FREE
to ASQ members
with the appropriate membership type and/or magazine subscriptions. If you are signed-in you should be able to download articles you are entitled to receive. If you have questions about your membership please contact Customer Care at 800-248-1946 or help@asq.org
Abstract: [This abstract is based on the authors' abstract.]
The ability to spot atypical performance in software processes or atypical entities in software data is important for knowing when to take action and which action is most appropriate. This study uses two techniques for identifying atypical observations in data from the Personal Software Process. Results show that simple techniques, such as interquartile limits, are almost as effective as XmR control charts for identifying outliers in the absence of casual analysis. Following recommended practice, however, is an important precursor to statistical process control.
Number of pages: 10
Price for ASQ Members: $5.00
Price for List/Forum/Division: $10.00
All electronic articles are sent as PDFs via e-mail. To view the documents, you will need Adobe
Reader (free download).
Orders placed during business hours are usually filled within one business day.
If you have questions please e-mail our Customer Care center at help@asq.org.
Browse QIC articles chronologically
previous next
|
