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    <title>topic Re: DOE custom design factor combinations in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/DOE-custom-design-factor-combinations/m-p/241396#M47683</link>
    <description>You can replicate the runs over the three incubators, from a DOE point of view it would be less efficient (you use more runs than strictly necessary), compared to considering 8 independent factors, incubator entity being one of those.&lt;BR /&gt;I can imagine you may have practical reasons to do the same thing on each incubator. In these situations, I like to construct a DOE table and use the "Evaluate Design" feature. The platform already provides comparison to full factorial design, I always look at "fractional increase of confidence interval". You can also compare to other scenarios, e.g., a D-optimal design with the same number of runs.</description>
    <pubDate>Wed, 15 Jan 2020 17:35:22 GMT</pubDate>
    <dc:creator>Wibo</dc:creator>
    <dc:date>2020-01-15T17:35:22Z</dc:date>
    <item>
      <title>DOE custom design factor combinations</title>
      <link>https://community.jmp.com/t5/Discussions/DOE-custom-design-factor-combinations/m-p/191812#M41095</link>
      <description>I am designing an experiment (2 level DOE) where my factors come from two sources, an incubator that has 4 factors to be controlled and shake flasks with 3 factors. I have 3 incubators and each one can hold 12 shake flasks. I want to implement a design where the shake flask settings (12 shake flasks each with different settings) are the same for each incubator setting. Is this possible in jmp?</description>
      <pubDate>Mon, 08 Apr 2019 09:55:31 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/DOE-custom-design-factor-combinations/m-p/191812#M41095</guid>
      <dc:creator>lara90</dc:creator>
      <dc:date>2019-04-08T09:55:31Z</dc:date>
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    <item>
      <title>Re: DOE custom design factor combinations</title>
      <link>https://community.jmp.com/t5/Discussions/DOE-custom-design-factor-combinations/m-p/191813#M41096</link>
      <description>&lt;P&gt;When you define the factors, change the Changes setting from Easy to Hard for the factors around the incubator.&lt;/P&gt;</description>
      <pubDate>Mon, 08 Apr 2019 10:30:53 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/DOE-custom-design-factor-combinations/m-p/191813#M41096</guid>
      <dc:creator>Mark_Bailey</dc:creator>
      <dc:date>2019-04-08T10:30:53Z</dc:date>
    </item>
    <item>
      <title>Re: DOE custom design factor combinations</title>
      <link>https://community.jmp.com/t5/Discussions/DOE-custom-design-factor-combinations/m-p/241396#M47683</link>
      <description>You can replicate the runs over the three incubators, from a DOE point of view it would be less efficient (you use more runs than strictly necessary), compared to considering 8 independent factors, incubator entity being one of those.&lt;BR /&gt;I can imagine you may have practical reasons to do the same thing on each incubator. In these situations, I like to construct a DOE table and use the "Evaluate Design" feature. The platform already provides comparison to full factorial design, I always look at "fractional increase of confidence interval". You can also compare to other scenarios, e.g., a D-optimal design with the same number of runs.</description>
      <pubDate>Wed, 15 Jan 2020 17:35:22 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/DOE-custom-design-factor-combinations/m-p/241396#M47683</guid>
      <dc:creator>Wibo</dc:creator>
      <dc:date>2020-01-15T17:35:22Z</dc:date>
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