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    <title>topic Re: Non-parametric coefficient of determination in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Non-parametric-coefficient-of-determination/m-p/222530#M44405</link>
    <description>Hi,&lt;BR /&gt;&lt;BR /&gt;Many thanks for your response. With this information I can continue my analysis :)&lt;/img&gt;&lt;BR /&gt;&lt;BR /&gt;Greetings,</description>
    <pubDate>Wed, 21 Aug 2019 17:52:37 GMT</pubDate>
    <dc:creator>tomasVH</dc:creator>
    <dc:date>2019-08-21T17:52:37Z</dc:date>
    <item>
      <title>Non-parametric coefficient of determination</title>
      <link>https://community.jmp.com/t5/Discussions/Non-parametric-coefficient-of-determination/m-p/222515#M44400</link>
      <description>&lt;P&gt;Dear,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I have a question about the use of the coefficient of determination (which is part of the output from a regression model).&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;For the study I'm ready to analyze the data, I want to use the coefficient of determination (COD) to examine the relationship between my dependent variables which represent each column (see annex). But, as I found out each dependent variables (column) has a non consistent distribution of the variance of the residuals.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;For this reason I suppose I cannot perform a regression model. But, I wonder what kind of non-parametric test I can use to become &lt;SPAN class="ver"&gt;some kind of&lt;/SPAN&gt; non-parametric coefficient of determination (COD) if this is even possible?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-right" image-alt="data exemple.JPG" style="width: 631px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/18931i198D531B2205FA9F/image-size/large?v=v2&amp;amp;px=999" role="button" title="data exemple.JPG" alt="data exemple.JPG" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Many thanks,&lt;/P&gt;&lt;P&gt;Tomas&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 21 Aug 2019 16:30:38 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Non-parametric-coefficient-of-determination/m-p/222515#M44400</guid>
      <dc:creator>tomasVH</dc:creator>
      <dc:date>2019-08-21T16:30:38Z</dc:date>
    </item>
    <item>
      <title>Re: Non-parametric coefficient of determination</title>
      <link>https://community.jmp.com/t5/Discussions/Non-parametric-coefficient-of-determination/m-p/222529#M44404</link>
      <description>&lt;P&gt;If the anomaly is merely that the residuals from the model are not exhibiting constant variance, then there is an alternative. I assume that your response is continuous numeric data.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Select &lt;STRONG&gt;Analyze&lt;/STRONG&gt; &amp;gt; &lt;STRONG&gt;Fit Model&lt;/STRONG&gt;. Click the drop-down button next to &lt;STRONG&gt;Personality&lt;/STRONG&gt; and select &lt;STRONG&gt;Loglinear Variance&lt;/STRONG&gt;. Select the &lt;STRONG&gt;response&lt;/STRONG&gt; data column and click &lt;STRONG&gt;Y&lt;/STRONG&gt;. Select the &lt;STRONG&gt;factor or predictor&lt;/STRONG&gt; data columns. Use the &lt;STRONG&gt;Add&lt;/STRONG&gt;, &lt;STRONG&gt;Cross&lt;/STRONG&gt;, or &lt;STRONG&gt;Macros&lt;/STRONG&gt; buttons to add the model terms to the &lt;STRONG&gt;Mean Effects&lt;/STRONG&gt;. Click the &lt;STRONG&gt;Variance Effects&lt;/STRONG&gt; tab and repeat the previous step. You are estimating two models: one for the mean of the response and another for the variance of the response. This modeling does not assume that variance is constant with the predicted mean.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Note: the model for the variance is often simpler than the one for the mean. That is, it usually does not exhibit interactions or non-linear effects.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;This way does not provide the coefficient of determination, though. The trade-off is losing the COD while gaining the ability to use regression models and learn about the effects on the variance.&lt;/P&gt;</description>
      <pubDate>Wed, 21 Aug 2019 17:45:36 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Non-parametric-coefficient-of-determination/m-p/222529#M44404</guid>
      <dc:creator>Mark_Bailey</dc:creator>
      <dc:date>2019-08-21T17:45:36Z</dc:date>
    </item>
    <item>
      <title>Re: Non-parametric coefficient of determination</title>
      <link>https://community.jmp.com/t5/Discussions/Non-parametric-coefficient-of-determination/m-p/222530#M44405</link>
      <description>Hi,&lt;BR /&gt;&lt;BR /&gt;Many thanks for your response. With this information I can continue my analysis :)&lt;/img&gt;&lt;BR /&gt;&lt;BR /&gt;Greetings,</description>
      <pubDate>Wed, 21 Aug 2019 17:52:37 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Non-parametric-coefficient-of-determination/m-p/222530#M44405</guid>
      <dc:creator>tomasVH</dc:creator>
      <dc:date>2019-08-21T17:52:37Z</dc:date>
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