Abstract
Two-level screening designs are appropriate for situations where a large number of factors (q) is examined but relatively few (k) of these are expected to be important. It is not knownwhich of theq factors will be the important ones, that is, it is not known whichk dimensions of the experimental space will be of further interest. After the results of the design have received a first analysis, the design will be projected into thek dimensions of interest. These projections are investigated for Plackett and Burman type-screening designs withq≤23 factors, andk=3, 4, and 5.
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References
Box GEP, Bisgaard S (1993) What can you find out from 12 experimental runs? Quality Engineering 5(4):663–668
Box GEP, Hunter JS (1961) The 2k−p fractional factorial designs. Parts I and II. Technometrics 3:311–351 and 449–458
Box GEP, Hunter WG, Hunter JS (1978) Statistics for Experimenters. Wiley, New York
Draper NR (1985) Small composite designs. Technometrics 27:173–180
Draper NR, Lin DKJ (1990) Using Plackett and Burman designs with fewer thanN−1 factors. Working Paper No 253, College of Business Administration, The University of Tennessee
Hall MJ (1961) Hadamard matrix of order 16. Jet propulsion laboratory. Summary 1:21–26
Hall MJ (1965) Hadamard matrix of order 20. Jet propulsion laboratory. Technical RReport 1:32–76
Lin DKJ, Draper NR (1993) Generating alias relationships for two-level plackett and burman design. Computational Statistics and Data Analysis 15:147–157
Lucas JM (1991) Achieving a robust process using response surface methodology. Du Pont Technical Report
Plackett RL, Burman JP (1946) The design of optimum multifactorial experiments. Biometrika 33:305–325
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Lin, D.K.J., Draper, N.R. Screening properties of certain two-level designs. Metrika 42, 99–118 (1995). https://doi.org/10.1007/BF01894291
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DOI: https://doi.org/10.1007/BF01894291