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    <title>topic Ratio variables in regression analysis in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Ratio-variables-in-regression-analysis/m-p/49840#M19836</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi all,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Should we be using ratio variable as predictors in regression analysis? If yes, how the estimate should be interpreted?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Fri, 30 Mar 2012 13:25:18 GMT</pubDate>
    <dc:creator>Amruta</dc:creator>
    <dc:date>2012-03-30T13:25:18Z</dc:date>
    <item>
      <title>Ratio variables in regression analysis</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Ratio-variables-in-regression-analysis/m-p/49840#M19836</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi all,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Should we be using ratio variable as predictors in regression analysis? If yes, how the estimate should be interpreted?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 30 Mar 2012 13:25:18 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Ratio-variables-in-regression-analysis/m-p/49840#M19836</guid>
      <dc:creator>Amruta</dc:creator>
      <dc:date>2012-03-30T13:25:18Z</dc:date>
    </item>
    <item>
      <title>Ratio variables in regression analysis</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Ratio-variables-in-regression-analysis/m-p/49841#M19837</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Many predictors are natural ratios and are interpreted like any other continuous measure.&amp;nbsp; The place that gets tricky is if the ratio is bounded and the data are near the bound.&amp;nbsp; For instance, often a % is bounded at 0 and 100.&amp;nbsp; If the data are in the middle, the usual normal theory works fine (An example of that is the "ejection fraction" for the heart.&amp;nbsp; It can officially be between 0 and 100%, but 60% is "normal" and it is rarely observed outside 10-90%, so we just use the standard analysis approach).&amp;nbsp; If the data are near the edge, then you probably need to explore some variance stabilizing transformation (see any good regression reference).&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Doc Muhlbaier&lt;/P&gt;&lt;P&gt;Duke&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 30 Mar 2012 13:46:34 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Ratio-variables-in-regression-analysis/m-p/49841#M19837</guid>
      <dc:creator>Doc_Duke</dc:creator>
      <dc:date>2012-03-30T13:46:34Z</dc:date>
    </item>
    <item>
      <title>Ratio variables in regression analysis</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Ratio-variables-in-regression-analysis/m-p/49842#M19838</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Doc's advice is excellent. My concern with ratio variables is that they are often bounded below by zero, but unbounded above.&amp;nbsp; One way to address this is to separate the numerator and denominator in the predictor by taking the logs of both and including those as predictors.&amp;nbsp; The correlation between the two predictors should (note SHOULD) cover the situation.&amp;nbsp; It is even more important for ratio variables as dependent variables.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Steve Denham&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 02 Apr 2012 11:52:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Ratio-variables-in-regression-analysis/m-p/49842#M19838</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2012-04-02T11:52:19Z</dc:date>
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