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    <title>topic Multiple Orthogonal Regression in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Multiple-Orthogonal-Regression/m-p/14724#M13712</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I have a multiple linear regression fit that has an actual by predicted plot that looks like this:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Actual by Predicted Plot&lt;/P&gt;&lt;DIV&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="10184_pastedImage_0.png" style="width: 357px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/2303i7F71B4624D85D10B/image-size/medium?v=v2&amp;amp;px=400" role="button" title="10184_pastedImage_0.png" alt="10184_pastedImage_0.png" /&gt;&lt;/span&gt;&lt;/DIV&gt;&lt;DIV&gt; &lt;/DIV&gt;&lt;DIV&gt;What can I do with it to get a better fit as there appears to be a bias from standard regression. Is there away to due multiple orthogonal regression in JMP or SAS and if so how? Is there another technique in JMP I can use to address this? The model is only main effects.&lt;/DIV&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Wed, 19 Oct 2016 00:55:04 GMT</pubDate>
    <dc:creator>bjbreitling</dc:creator>
    <dc:date>2016-10-19T00:55:04Z</dc:date>
    <item>
      <title>Multiple Orthogonal Regression</title>
      <link>https://community.jmp.com/t5/Discussions/Multiple-Orthogonal-Regression/m-p/14724#M13712</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I have a multiple linear regression fit that has an actual by predicted plot that looks like this:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Actual by Predicted Plot&lt;/P&gt;&lt;DIV&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="10184_pastedImage_0.png" style="width: 357px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/2303i7F71B4624D85D10B/image-size/medium?v=v2&amp;amp;px=400" role="button" title="10184_pastedImage_0.png" alt="10184_pastedImage_0.png" /&gt;&lt;/span&gt;&lt;/DIV&gt;&lt;DIV&gt; &lt;/DIV&gt;&lt;DIV&gt;What can I do with it to get a better fit as there appears to be a bias from standard regression. Is there away to due multiple orthogonal regression in JMP or SAS and if so how? Is there another technique in JMP I can use to address this? The model is only main effects.&lt;/DIV&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 19 Oct 2016 00:55:04 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Multiple-Orthogonal-Regression/m-p/14724#M13712</guid>
      <dc:creator>bjbreitling</dc:creator>
      <dc:date>2016-10-19T00:55:04Z</dc:date>
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    <item>
      <title>Re: Multiple Orthogonal Regression</title>
      <link>https://community.jmp.com/t5/Discussions/Multiple-Orthogonal-Regression/m-p/14725#M13713</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hello bjbreitling,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The first thing I would recommend is doing a Partition analysis under Analyze &amp;gt; Modeling.&amp;nbsp; Use the same response and factors as you have for the original analysis.&amp;nbsp; Make sure to use a validation set of at least 20% by selecting a validation portion in the model dialog.&amp;nbsp; You can also make a validation column that you can then add to the analysis.&amp;nbsp; First things first though.&amp;nbsp; Once you get things set up select OK and then you will see the analysis page.&amp;nbsp; The model building portion has not been run yet.&amp;nbsp; You will need to select Go.&amp;nbsp; If you do not see Go then you have not selected a validation set.&amp;nbsp; If you do not see Go then select Split and keep selecting Split until you can go no further - no more splits will take place when you click split.&amp;nbsp; Go to the red hot spot at the top and select column contributions to see which variables are the most important to your model.&amp;nbsp; If you have JMP Pro do the same analysis but this time do a Bootstrap Forest.&amp;nbsp; You may see a difference in variables that are important.&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;From this point go back to Fit Model and build a Main Effects only model with the variables that are most important and then build a model with Interactions included.&amp;nbsp; If there are important interactions you will see them here.&amp;nbsp; Again, if you have JMP Pro, use the Generalized Regression platform under the Standard Least Squares drop down in the upper right. This is a good variable shrinkage technique to try with all of your main effects and interactions included.&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;There is a univariate orthogonal fit under Analyze &amp;gt; Fit Y by X in JMP.&amp;nbsp; Not sure what SAS has to offer there.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Hope this helps,&lt;/P&gt;&lt;P&gt;Bill&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 13 Oct 2015 11:01:12 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Multiple-Orthogonal-Regression/m-p/14725#M13713</guid>
      <dc:creator>Bill_Worley</dc:creator>
      <dc:date>2015-10-13T11:01:12Z</dc:date>
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