Presentation: A New DOE Paradigm - JMP

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Alternatives to Resolution III Designs The regular resolution III designs with from 9 to 15 factors in 16 runs are used frequently in practice These designs completely confound some interactions with main effects For example, in the minimum aberration nine factor case, 12 two-factor interactions are aliased with main effects and 24 two-factor interactions are confounded in groups with other two-factor interactions Follow-up experiments are often necessary, and the best augmentation approach may not be obvious. Nonregular designs with no pure confounding of main effects and twofactor interactions are useful alternatives. We provide a collection of these designs. 62
Recommended 9 Factor Design Run A B C D E F G H J 1 -1 -1 -1 -1 -1 -1 1 -1 1 2 -1 -1 -1 1 -1 1 -1 1 -1 3 -1 -1 1 -1 1 1 1 1 -1 4 -1 -1 1 1 1 -1 -1 -1 1 5 -1 1 -1 -1 1 1 -1 1 1 6 -1 1 -1 1 1 -1 1 -1 -1 7 -1 1 1 -1 -1 -1 -1 1 -1 8 -1 1 1 1 -1 1 1 -1 1 9 1 -1 -1 -1 1 -1 -1 -1 -1 10 1 -1 -1 1 1 1 1 1 1 11 1 -1 1 -1 -1 1 -1 -1 1 12 1 -1 1 1 -1 -1 1 1 -1 13 1 1 -1 -1 -1 1 1 -1 -1 14 1 1 -1 1 -1 -1 -1 1 1 15 1 1 1 -1 1 -1 1 1 1 16 1 1 1 1 1 1 -1 -1 -1 Correlation of Main Effects and Two-Factor Interactions
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A New DOE Paradigm • April 12, 20
Page 3:
Design Optimality • One of the tr
Page 6 and 7:
Which Criterion should I Use? • F
Page 8 and 9:
How would we design an experiment f
Page 10 and 11:
Design Comparison 2 reps D-optimal
Page 12 and 13: Let’s back up What we just showed
Page 14 and 15: The Regular One-Half Fraction of th
Page 16 and 17: Aliasing in the One-Half Fraction o
Page 18 and 19: • Resolution III Designs: - me =
Page 20 and 21: Projection of Fractional Factorials
Page 22 and 23: Example 8.1 Interpretation of resul
Page 24: Suppose we believe in the model: y
Page 27 and 28: The Alias Matrix: Columns represent
Page 29: The Regular One-Quarter Fraction of
Page 33 and 34: The Regular 2 k-p Design: Resolutio
Page 35 and 36: Number of Orthogonal Designs versus
Page 37: Could one of these other orthogonal
Page 40 and 41: The No-Confounding Design 40
Page 42 and 43: Color Plot for the No-Confounding D
Page 44 and 45: Stepwise regression selects the mai
Page 46 and 47: Hall I 15 Factor Design Run A B C D
Page 48 and 49: Hall III 15 Factor Design Run A B C
Page 50 and 51: Hall V 15 Factor Design Run A B C D
Page 52 and 53: Recommended Nonregular 6 Factor Des
Page 54 and 55: Color Plot for the Standard Minimum
Page 58 and 59: Color Plot for the Standard Minimum
Page 64 and 65: Recommended 10 Factor Design Run A
Page 70 and 71: Motivation: Problems with Standard
Page 72 and 73: Alias Matrix of Plackett-Burman des
Page 74 and 75: Screening Design - Wish List 1. Ort
Page 76 and 77: How did we find this design? - we u
Page 78 and 79: Orthogonal Main Effects Design Cons
Page 80 and 81: Conference Matrix of Order 6
Page 82 and 83: Example 1 6 factors and 13 runs. Ru
Page 84 and 85: Column Correlations 0.25 0.5 0.4655
Page 86 and 87: Column Correlations Note the zero c
Page 88 and 89: Where did the data come from? We si
Page 90 and 91: Recapitulation - Definitive Screeni
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