Studies in Quality Improvement II. An Analysis for Unreplicated Fractional Factorials
Abstract
In industrial experimentation it is frequently true that a large proportion of process variation is associated with a small proportion of the process variables. In such circumstances of effect sparsity unreplicated fractional designs have frequently been effective in isolating preponderant factors. A very useful graphical analysis for such experiments due to Cuthbert Daniel employs normal probability plotting. A more formal analysis is presented here which might be used to supplement such plots. Key Words: Fractional factorials, effect sparsity, normal plotting, Bayesian analysis, robust samples.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1985
- Accession Number
- ADA153539
Entities
People
- George E. Box
- R. D. Meyer
Organizations
- University of Wisconsin–Madison