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    <title>topic Re: Analysis Option in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974688#M110632</link>
    <description>&lt;P&gt;Thank you for the suggestion. I did consider the FIT Model. I tried your suggestion for Predictive model and try Neural and Partition.&amp;nbsp; I am confuse what does those suggest?&lt;/P&gt;</description>
    <pubDate>Fri, 02 Oct 2026 16:17:33 GMT</pubDate>
    <dc:creator>OneFunViet</dc:creator>
    <dc:date>2026-10-02T16:17:33Z</dc:date>
    <item>
      <title>Analysis Option</title>
      <link>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974314#M110621</link>
      <description>&lt;P&gt;I had JMP data files that I need to determine what is best analysis plot or model to use. I have X-Y axis that variable that response to Z value. I want to use that best analysis model/plot to determine the behavior and response.&amp;nbsp; I include the JMP with changed of labels avoid giving away the detail of the work. Z axis that I am interesting in the thickness of materials in well.&amp;nbsp; Thank you.&lt;/P&gt;</description>
      <pubDate>Wed, 30 Sep 2026 17:21:03 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974314#M110621</guid>
      <dc:creator>OneFunViet</dc:creator>
      <dc:date>2026-09-30T17:21:03Z</dc:date>
    </item>
    <item>
      <title>Re: Analysis Option</title>
      <link>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974553#M110629</link>
      <description>&lt;P&gt;It sounds like you are trying to see the relationships between your inputs/controls and Lay Thickness. If that is the case, you could think about fitting a linear regression model in Analyze &amp;gt; Fit Model. You could also consider using one of the options in Analyze &amp;gt; Predictive Modeling.&lt;/P&gt;</description>
      <pubDate>Thu, 01 Oct 2026 20:43:39 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974553#M110629</guid>
      <dc:creator>MarilynWheatley</dc:creator>
      <dc:date>2026-10-01T20:43:39Z</dc:date>
    </item>
    <item>
      <title>Re: Analysis Option</title>
      <link>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974688#M110632</link>
      <description>&lt;P&gt;Thank you for the suggestion. I did consider the FIT Model. I tried your suggestion for Predictive model and try Neural and Partition.&amp;nbsp; I am confuse what does those suggest?&lt;/P&gt;</description>
      <pubDate>Fri, 02 Oct 2026 16:17:33 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974688#M110632</guid>
      <dc:creator>OneFunViet</dc:creator>
      <dc:date>2026-10-02T16:17:33Z</dc:date>
    </item>
    <item>
      <title>Re: Analysis Option</title>
      <link>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974791#M110634</link>
      <description>&lt;P&gt;Can you provide more detail about the nature of your data - how derived and the meaning of the variables?&amp;nbsp; You have 2 Thickness variables (you said that you wanted to explain the thickness), but those two variables look like a designed experiment to span a two-dimensional range of thickness measurements. I don't see any randomness is either of those. Instead, you have 4 continuous factors that appear to exhibit randomness. So, it looks to me like the data is from a designed experiment with combinations of these 4 factors that result in those fixed combinations of 2 dimensional thickness. I'm not a DOE person, but there are many in this community and I'm pretty sure they can help you with the analysis - but it doesn't look like a standard prediction model to me. I think some more details about the nature of the data and how it was collected would be most useful to the community.&lt;/P&gt;</description>
      <pubDate>Sat, 03 Oct 2026 14:57:58 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Analysis-Option/m-p/974791#M110634</guid>
      <dc:creator>dlehman1</dc:creator>
      <dc:date>2026-10-03T14:57:58Z</dc:date>
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