<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Split DoE in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Split-DoE/m-p/433111#M68274</link>
    <description>Hi,&lt;BR /&gt;I designed DoE by RSM. I wonder if I can split the design and first step to try the main effects and then 2nd step to try the rest experiments for the 2nd order effects.&lt;BR /&gt;Please advice&lt;BR /&gt;</description>
    <pubDate>Thu, 08 Jun 2023 21:06:51 GMT</pubDate>
    <dc:creator>YanivD</dc:creator>
    <dc:date>2023-06-08T21:06:51Z</dc:date>
    <item>
      <title>Split DoE</title>
      <link>https://community.jmp.com/t5/Discussions/Split-DoE/m-p/433111#M68274</link>
      <description>Hi,&lt;BR /&gt;I designed DoE by RSM. I wonder if I can split the design and first step to try the main effects and then 2nd step to try the rest experiments for the 2nd order effects.&lt;BR /&gt;Please advice&lt;BR /&gt;</description>
      <pubDate>Thu, 08 Jun 2023 21:06:51 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Split-DoE/m-p/433111#M68274</guid>
      <dc:creator>YanivD</dc:creator>
      <dc:date>2023-06-08T21:06:51Z</dc:date>
    </item>
    <item>
      <title>Re: Split DoE</title>
      <link>https://community.jmp.com/t5/Discussions/Split-DoE/m-p/433132#M68280</link>
      <description>&lt;P&gt;If you do this, you are running parts of the design in 2 blocks and therefore might (likely) need to account for the block effect.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I don't know anything about your situation, like the number of factors you are looking at, but typically if you are doing RSM, you already have data to support a first order model AND you have a good amount of information about noise. &amp;nbsp;If not, perhaps you can do sequential experimentation and start with fractional factorials and block designs.&lt;/P&gt;</description>
      <pubDate>Thu, 04 Nov 2021 16:57:28 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Split-DoE/m-p/433132#M68280</guid>
      <dc:creator>statman</dc:creator>
      <dc:date>2021-11-04T16:57:28Z</dc:date>
    </item>
    <item>
      <title>Re: Split DoE</title>
      <link>https://community.jmp.com/t5/Discussions/Split-DoE/m-p/433193#M68290</link>
      <description>&lt;P&gt;RSM is an old method and still valid. The Central Composite Design method, in particular, had an advantage of running the two-level treatments and some of the center points first, and then deciding if continuing with the axial points was warranted.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;On the other hand, modern methods offer another way. You can create a small custom screening design and then &lt;A href="https://www.jmp.com/support/help/en/16.1/#page/jmp/augment-designs.shtml" target="_self"&gt;augment the design&lt;/A&gt; to address deficiencies such as low power or model bias. You could also use the economical &lt;A href="https://www.jmp.com/support/help/en/16.1/#page/jmp/definitive-screening-designs.shtml#" target="_self"&gt;Definitive Screening Design&lt;/A&gt; method if it is a screening scenario (e.g., determine a small number of important factors out of a large number of candidate factors.) These designs support estimating linear, non-linear, and interaction effects when the screening principles hold.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;You have choices.&lt;/P&gt;</description>
      <pubDate>Thu, 04 Nov 2021 18:26:02 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Split-DoE/m-p/433193#M68290</guid>
      <dc:creator>Mark_Bailey</dc:creator>
      <dc:date>2021-11-04T18:26:02Z</dc:date>
    </item>
  </channel>
</rss>

