Exploring Best Practices in the Design of Experiments - JMP

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From “A Treatise…” “On the 20th May, 1747, I took twelve patients in the scurvy on board the Salisbury at sea. Their cases were as similar as I could have them. They all in general had putrid gums, the spots and lassitude, with weakness of their knees. … •Two of these were ordered each a quart of cyder a day… •Two others took twenty five gutts of elixir vitriol three times a day upon an empty stomach… •Two others took two spoonfuls of vinegar three times a day upon an empty stomach… •Two of the worst patients, with the tendons in the ham rigid (a symptom none the rest had) were put under a course of sea water… •Two others had each two oranges and one lemon given them every day… •The two remaining patients took the bigness of a nutmeg three times a day… The consequence was that the most sudden and visible good effects were perceived from the use of the oranges and lemons” 4/29/2013
Where have we been? • Gergonne: 1815 • Designs for polynomial regression, response surface design • S. C. Peirce: 1870s • Randomization • K. Smith, 1918: Biometrika, Optimal design for polynomial regression 4/29/2013
Page 1 and 2: 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: Where have we been? • James Lind,
Page 11 and 12: 1920’s-1950’s: Orthogonality is
Page 13 and 14: 1950s: Baby steps away from orthogo
Page 15 and 16: Shift to problem-driven design (70s
Page 17 and 18: 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 and 70:
What’s driving the software? Opti
Page 71 and 72:
Lots of work in Model Robust and Mu
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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