Full metadata
Title
Measurement systems analysis studies: a look at the partition of variation (POV) method
Description
The Partition of Variance (POV) method is a simplistic way to identify large sources of variation in manufacturing systems. This method identifies the variance by estimating the variance of the means (between variance) and the means of the variance (within variance). The project shows that the method correctly identifies the variance source when compared to the ANOVA method. Although the variance estimators deteriorate when varying degrees of non-normality is introduced through simulation; however, the POV method is shown to be a more stable measure of variance in the aggregate. The POV method also provides non-negative, stable estimates for interaction when compared to the ANOVA method. The POV method is shown to be more stable, particularly in low sample size situations. Based on these findings, it is suggested that the POV is not a replacement for more complex analysis methods, but rather, a supplement to them. POV is ideal for preliminary analysis due to the ease of implementation, the simplicity of interpretation, and the lack of dependency on statistical analysis packages or statistical knowledge.
Date Created
2015
Contributors
- Little, David John (Author)
- Borror, Connie (Thesis advisor)
- Montgomery, Douglas C. (Committee member)
- Broatch, Jennifer (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
xiv, 45 pages : illustrations (some color)
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.36478
Statement of Responsibility
by David John Little
Description Source
Viewed on March 9, 2016
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2015
Note type
thesis
Includes bibliographical references (page 43)
Note type
bibliography
Field of study: Industrial engineering
System Created
- 2016-02-01 07:06:57
System Modified
- 2021-08-30 01:25:32
- 2 years 8 months ago
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