Exploring Best Practices in the Design of Experiments - JMP

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Optimal designs depend on a model! OH YEAH? 1. No more so than classical designs. 2. They should! 3. If you’re going to lose sleep, see model robustness, Bayesian, multi-objective design literature 4/29/2013
Lots of work in Model Robust and Multi-objective Design Randomly chosen examples 1. Li/Nachtsheim, 2000, Model Robust Factorial Designs 2. DuMouchel/Jones, 1994, Bayesian D-optimal Designs 3. Jones/Nachtsheim, 2011 Minimal Aliasing Designs 4/29/2013
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Exploring Best Practices in the Des
Page 3 and 4:
Overview • Where have we been?
Page 5 and 6:
Where have we been? • 10 th Centu
Page 7 and 8:
Where have we been? • James Lind,
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Where have we been? • Gergonne: 1
Page 11 and 12:
1920’s-1950’s: Orthogonality is
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1950s: Baby steps away from orthogo
Page 15 and 16:
Shift to problem-driven design (70s
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Paradigms for DOE Classical: • Fi
Page 19 and 20: Classical design paradigms pros, co
Page 21 and 22: What DOE Expertise (continued)? •
Page 23 and 24: New Paradigm: Problem-driven design
Page 25 and 26: Problem-driven paradigm pros, cons:
Page 27 and 28: Example 1: Extracting food solids f
Page 29 and 30: Knowledge Requirements: • Main ef
Page 31 and 32: Here’s how it’s done: 4/29/2013
Page 33 and 34: Example 2: Maximize recall of TV co
Page 35 and 36: Contrast: Problem-Driven Design App
Page 37 and 38: Example 3: Supersaturated Designs P
Page 39 and 40: You can’t do that! 1. Design matr
Page 41 and 42: FF designs are supersaturated desig
Page 43 and 44: Problem-Driven Design Approach Know
Page 45 and 46: All experimental Not all main facto
Page 47 and 48: Color-coded correlation matrix has
Page 49 and 50: Example 4: Sheet Aluminum Rolling M
Page 51 and 52: But there is a problem: We have con
Page 53 and 54: DOE to the rescue! • Result: When
Page 55 and 56: Confession of a Text Book Author
Page 57 and 58: Recent Paper---My Conversion 4/29/2
Page 59 and 60: Example 5: Corrosion resistance of
Page 61 and 62: Actual Corrosion Resistance DOE Who
Page 63 and 64: Contrast with ordinary DOE In usual
Page 65 and 66: Analysis depends on the randomizati
Page 67 and 68: Take-Aways 1. Always consider runni
Page 69: What’s driving the software? Opti
Page 73 and 74: Multi-objective designs: six-factor
Page 75 and 76: Alias Matrix of Plackett-Burman des
Page 77 and 78: Turns out there is: Six foldover pa
Page 79 and 80: Definitive Screening Design for 6 f
Page 81 and 82: Now generalize this structure for m
Page 83 and 84: Conference Matrices & Telephony Fro
Page 85 and 86: Here the amazing result: Form the a
Page 87 and 88: Impact? A break-through solution fo
Page 89 and 90: Impact? From the conclusions: “De
Page 91 and 92: Upshot - Definitive Screening Desig
Page 93 and 94: BUT!…a limitation DSDs can only b
Page 95 and 96: Example 6: Extracting food solids f
Page 97: So, what are the current best pract
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Exploring Best Practices in the Design of Experiments - JMP

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