Presentation: A New DOE Paradigm - JMP

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How did we find this design? – we used an optimization algorithm
Problem: Finding orthogonal main effects plans. Algorithmic approach gave orthogonal main effects plans for: 6 factors 8 factors 10 factors but not 12 factors.
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A New DOE Paradigm • April 12, 20
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Design Optimality • One of the tr
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Which Criterion should I Use? • F
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How would we design an experiment f
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Design Comparison 2 reps D-optimal
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Let’s back up What we just showed
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The Regular One-Half Fraction of th
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Aliasing in the One-Half Fraction o
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• Resolution III Designs: - me =
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Projection of Fractional Factorials
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Example 8.1 Interpretation of resul
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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 62 and 63: Alternatives to Resolution III Desi
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 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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Presentation: A New DOE Paradigm - JMP

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