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Design of Experiments - US Army Conference on Applied Statistics

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DOX Course – Final Thoughts<br />

1. Optimal design framework is general and powerful for handling<br />

all kinds <str<strong>on</strong>g>of</str<strong>on</strong>g> DOX problems.<br />

2. Modern s<str<strong>on</strong>g>of</str<strong>on</strong>g>tware makes it easy to generate optimal designs<br />

for virtually any problem incorporating c<strong>on</strong>straints <strong>on</strong><br />

1. Factor combinati<strong>on</strong>s<br />

2. Model requirements<br />

3. Restricti<strong>on</strong>s <strong>on</strong> sample size<br />

4. Restricti<strong>on</strong>s <strong>on</strong> randomizati<strong>on</strong><br />

3. It is time to break away from traditi<strong>on</strong>al methods<br />

4. Make the design fit the problem d<strong>on</strong>’t force your problem into<br />

the c<strong>on</strong>straints <str<strong>on</strong>g>of</str<strong>on</strong>g> a classical design.

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