Design of Experiments - US Army Conference on Applied Statistics

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Module 1 – Conclusions 1. Remember Fisher’s Four Principles 1. Factorial Principle 2. Randomization 3. Blocking 4. Replication 2. ANOVA models can be converted to regression models. 3. Factor coding for continuous and categorical factors is a technical detail important for this conversion. 4. Variance and bias are fundamental criteria for evaluating designs.
Standard designs using an optimal design tool. Goals DOE Course – Module 2 1. Give many examples <strong>of</strong> familiar designs created using an optimal design algorithm
Page 1 and 2: Design of<
Page 3 and 4: What Is a Designed
Page 5 and 6: What Is a Response? A response is a
Page 7 and 8: What is a model? a simplified mathe
Page 9 and 10: Important Points from the Fathers <
Page 11 and 12: ANOVA Model for Mileage Study Note
Page 13 and 14: Orthogonal Coding and Orthogonal <s
Page 15 and 16: ANOVA/Regression Model - Matrix Not
Page 17 and 18: Categorical Factor Coding - 2 level
Page 19 and 20: Continuous Factor Coding MR - midra
Page 21 and 22: The Model/Design R
Page 23 and 24: The Model/Design R
Page 25 and 26: Design Optimality
Page 27: Simple experiment for three factors
Page 31 and 32: INPUTS (Factors) Airspeed Turn Rate
Page 33 and 34: Fractional Factorial designs are D-
Page 35: Consider the 2 5-1 - Again Created
Page 38 and 39: JMP Demo Relative Variance
Page 40 and 41: INPUTS (Factors) 40 SPEAR AGM Tests
Page 42 and 43: Suppose that we want to focus on ma
Page 44 and 45: Alias Matrix Effect Intercept A B C
Page 46 and 47: Module 2 - Summary 1. Main message
Page 48 and 49: Regular Designs ma
Page 50 and 51: Define Nonisomorphic Two designs ar
Page 53 and 54: Screening for Shrinkage Contrasts T
Page 55 and 56: Where Did the Data in this Experime
Page 57 and 58: Stepwise Fit Response: Shrinkage St
Page 59 and 60: No-Confounding Design</stro
Page 61 and 62: Hall II 15 Factor Design</s
Page 63 and 64: Hall IV 15 Factor Design</s
Page 65 and 66: Constructing the Recommended 6 Fact
Page 67 and 68: Color Plot for the Standard Minimum
Page 69 and 70: Recommended Nonregular 7 Factor <st
Page 71 and 72: Color Plot for the Standard Minimum
Page 73 and 74: Recommended Nonregular 8 Factor <st
Page 75 and 76: Alternatives to Resolution III <str
Page 77 and 78: Recommended 10 Factor Desig
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Recommended 12 Factor Desig
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Recommended 14 Factor Desig
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Note that both main effects and two
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Main effects are not aliased. One t
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Plackett-Burman Design</str
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A 12-Factor Example 1 1 1 1 1 1 1 1
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The data for the example came from
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Embarrassing Problem Case Suppose w
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Reactor Case Study Box, Hunter and
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Robust Screening Design</st
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Robust Screening Design</st
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The Case for Non-orthogonal <strong
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References Hall, M. Jr. (1961). Had
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Module 4 - Blocking • Many experi
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Blocking • Blocking is a techniqu
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The randomized complete block desig
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Cars The Latin Square Desig
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JMP Demo
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Another Example of
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In general, if there are p treatmen
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Back to tire testing • Suppose th
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Module 4 - Summary • Most design
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Split-plot Graphic Definition
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Model for Split-plot Experi
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General procedure Split-plot <stron
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Scenario 1. Four factors Split-Plot
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Ad hoc Design #1
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I-optimal Split-Plot Design
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OLS vs GLS Data Analysis OLS Analys
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Module 6 - Introduction to RSM •
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E(y) = f( ) The response surface Th
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Response Surface Methodology • Wh
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CI on the mean response at a point
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Three Typical Applications
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Useful References on RSM • Box, G
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Categorical and Continuous Variable
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Goals Module 7 - RSM with Factor Co
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A problem with a constrained design
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JMP Default RSM Design</str
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Design Comparison
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Module 7 - Summary • Constraints
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Robust Parameter Design</st
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The Response Surface Approach Secti
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Filtration Robust Processing Exampl
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JMP Demo
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• Goals Module 9 Mixture
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Mixtures occur in lots of</
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Simplex Designs Si
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An experiment involving shampoo for
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Makeup of an Aircr
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Mixtures in JMP Ternary Plot .1 X1
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• Goals Module 10 - Covering Arra
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Covering Array Definition A coverin
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Covering arrays 1 1 1 1 1 2 2 2 2 2
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Covering arrays and sof</st
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Example - Air to ground missile sys
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CA(N:2,k,2) Results k 2-3 4 5-10 11
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References • R. Brownlie, J. Prow
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Module 10 - Summary • Covering ar
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199 What is a supersaturated design
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201 A more general definition… Su
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203 Classical “supersaturated”
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205 Case 2 - Fractional Factorial <
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207 Problem D-Optimal designs depen
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209 Example 2 6-2 Fractional Factor
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211 Bayesian D-Optimal designs Find
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213 Question Why should only higher
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Card Trick in JMP
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DOX Course - Final Thoughts 1. Opti
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Design of Experiments - US Army Conference on Applied Statistics

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