Design of Experiments - US Army Conference on Applied Statistics

More documents

Recommendations

Info

OLS vs GLS Data Analysis OLS Analysis GLS Analysis
Module 5 – Summary 1. Split-plot designs are common in industry. 2. They are not commonly recognized as being split-plot designs. 3. As a result, these designs are mistakenly analyzed using OLS. 4. Explicitly, taking randomization restrictions into account makes the design process more economical, <strong>of</strong>ten more statistically efficient and more likely to produce valid analytical results.
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 and 28:
Simple experiment for three factors
Page 29 and 30:
Standard designs using an optimal d
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
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Note that both main effects and two
Page 85 and 86: Main effects are not aliased. One t
Page 87 and 88: Plackett-Burman Design</str
Page 89: A 12-Factor Example 1 1 1 1 1 1 1 1
Page 92 and 93: The data for the example came from
Page 94 and 95: Embarrassing Problem Case Suppose w
Page 96 and 97: Reactor Case Study Box, Hunter and
Page 98 and 99: Robust Screening Design</st
Page 100 and 101: Robust Screening Design</st
Page 102 and 103: The Case for Non-orthogonal <strong
Page 104 and 105: References Hall, M. Jr. (1961). Had
Page 106 and 107: Module 4 - Blocking • Many experi
Page 108 and 109: Blocking • Blocking is a techniqu
Page 110 and 111: The randomized complete block desig
Page 112 and 113: Cars The Latin Square Desig
Page 114 and 115: JMP Demo
Page 116 and 117: Another Example of
Page 118 and 119: In general, if there are p treatmen
Page 120 and 121: Back to tire testing • Suppose th
Page 122 and 123: Module 4 - Summary • Most design
Page 124 and 125: Split-plot Graphic Definition
Page 126 and 127: Model for Split-plot Experi
Page 128 and 129: General procedure Split-plot <stron
Page 130 and 131: Scenario 1. Four factors Split-Plot
Page 132 and 133: Ad hoc Design #1
Page 134 and 135: I-optimal Split-Plot Design
Page 138 and 139: Module 6 - Introduction to RSM •
Page 140 and 141: E(y) = f( ) The response surface Th
Page 142 and 143: Response Surface Methodology • Wh
Page 144 and 145: CI on the mean response at a point
Page 147 and 148: Three Typical Applications
Page 149 and 150: Useful References on RSM • Box, G
Page 151 and 152: Categorical and Continuous Variable
Page 153 and 154: Goals Module 7 - RSM with Factor Co
Page 155 and 156: A problem with a constrained design
Page 157 and 158: JMP Default RSM Design</str
Page 159 and 160: Design Comparison
Page 161 and 162: Module 7 - Summary • Constraints
Page 163 and 164: Robust Parameter Design</st
Page 165 and 166: The Response Surface Approach Secti
Page 167 and 168: Filtration Robust Processing Exampl
Page 169 and 170: JMP Demo
Page 171 and 172: • Goals Module 9 Mixture
Page 173 and 174: Mixtures occur in lots of</
Page 175 and 176: Simplex Designs Si
Page 177 and 178: An experiment involving shampoo for
Page 179 and 180: Makeup of an Aircr
Page 181 and 182: Mixtures in JMP Ternary Plot .1 X1
Page 183 and 184: • Goals Module 10 - Covering Arra
Page 185 and 186: Covering Array Definition A coverin
Page 187 and 188:
Covering arrays 1 1 1 1 1 2 2 2 2 2
Page 189 and 190:
Covering arrays and sof</st
Page 191 and 192:
Example - Air to ground missile sys
Page 193 and 194:
CA(N:2,k,2) Results k 2-3 4 5-10 11
Page 195 and 196:
References • R. Brownlie, J. Prow
Page 197 and 198:
Module 10 - Summary • Covering ar
Page 199 and 200:
199 What is a supersaturated design
Page 201 and 202:
201 A more general definition… Su
Page 203 and 204:
203 Classical “supersaturated”
Page 205 and 206:
205 Case 2 - Fractional Factorial <
Page 207 and 208:
207 Problem D-Optimal designs depen
Page 209 and 210:
209 Example 2 6-2 Fractional Factor
Page 211 and 212:
211 Bayesian D-Optimal designs Find
Page 213 and 214:
213 Question Why should only higher
Page 215 and 216:
Card Trick in JMP
Page 217:
DOX Course - Final Thoughts 1. Opti
show all

Design of Experiments - US Army Conference on Applied Statistics

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?