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    <title>topic Re: What does ill-conditioned regression problem alert mean? in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/What-does-ill-conditioned-regression-problem-alert-mean/m-p/17104#M15604</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The attached (encrypted) script is taken from the book I helped to write: &lt;A href="https://www.sas.com/store/books/categories/usage-and-reference/discovering-partial-least-squares-with-jmp-/prodBK_65346_en.html"&gt;https://www.sas.com/store/books/categories/usage-and-reference/discovering-partial-least-squares-with-jmp-/prodBK_65346_en.html&lt;/A&gt; and it shows the effect. As the correlation between X1 and X2 near +1 (or -1), the problem becomes ill-conditioned. So I assume the data you have did not come from a designed experiment, and that you will need to drop some variables if you want to use MLR.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Mon, 22 Feb 2016 14:34:02 GMT</pubDate>
    <dc:creator>ian_jmp</dc:creator>
    <dc:date>2016-02-22T14:34:02Z</dc:date>
    <item>
      <title>What does ill-conditioned regression problem alert mean?</title>
      <link>https://community.jmp.com/t5/Discussions/What-does-ill-conditioned-regression-problem-alert-mean/m-p/17103#M15603</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I'm&amp;nbsp; using Fit Model &amp;gt; Standard Least Squares, Effect Screening using Macros &amp;gt; Factor to Degree (2). and get the following alert:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="11043_pastedImage_0.png" style="width: 345px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/2769iCBA238B035F43BF9/image-size/medium?v=v2&amp;amp;px=400" role="button" title="11043_pastedImage_0.png" alt="11043_pastedImage_0.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Any help to understand and resolve would be greatly appreciated.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 19 Oct 2016 01:58:29 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/What-does-ill-conditioned-regression-problem-alert-mean/m-p/17103#M15603</guid>
      <dc:creator>jeff_kolton1</dc:creator>
      <dc:date>2016-10-19T01:58:29Z</dc:date>
    </item>
    <item>
      <title>Re: What does ill-conditioned regression problem alert mean?</title>
      <link>https://community.jmp.com/t5/Discussions/What-does-ill-conditioned-regression-problem-alert-mean/m-p/17104#M15604</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The attached (encrypted) script is taken from the book I helped to write: &lt;A href="https://www.sas.com/store/books/categories/usage-and-reference/discovering-partial-least-squares-with-jmp-/prodBK_65346_en.html"&gt;https://www.sas.com/store/books/categories/usage-and-reference/discovering-partial-least-squares-with-jmp-/prodBK_65346_en.html&lt;/A&gt; and it shows the effect. As the correlation between X1 and X2 near +1 (or -1), the problem becomes ill-conditioned. So I assume the data you have did not come from a designed experiment, and that you will need to drop some variables if you want to use MLR.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 22 Feb 2016 14:34:02 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/What-does-ill-conditioned-regression-problem-alert-mean/m-p/17104#M15604</guid>
      <dc:creator>ian_jmp</dc:creator>
      <dc:date>2016-02-22T14:34:02Z</dc:date>
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