<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Re: collinearity between two variables in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492498#M25554</link>
    <description>&lt;P&gt;Collinearity is not the right word for categorical variables.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;p&amp;lt;0.05 indicates lack of independence.&lt;/P&gt;</description>
    <pubDate>Tue, 04 Sep 2018 22:40:52 GMT</pubDate>
    <dc:creator>PaigeMiller</dc:creator>
    <dc:date>2018-09-04T22:40:52Z</dc:date>
    <item>
      <title>collinearity between two variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492491#M25551</link>
      <description>&lt;P&gt;I'm creating a regression model and I want to test if some co-variables are linear or co-dependent? I am thinking, among other things, of '' access to land '' and '' possession of livestock ''.&amp;nbsp;&lt;/P&gt;&lt;P&gt;Which test can I use to show the&amp;nbsp;&lt;STRONG&gt;collinearity between two categorical variables?&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;Thanks&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 04 Sep 2018 21:54:06 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492491#M25551</guid>
      <dc:creator>sebai</dc:creator>
      <dc:date>2018-09-04T21:54:06Z</dc:date>
    </item>
    <item>
      <title>Re: collinearity between two variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492495#M25552</link>
      <description>&lt;P&gt;The Chi-squared test in PROC FREQ can test the independence of two categorical variables.&lt;/P&gt;</description>
      <pubDate>Tue, 04 Sep 2018 22:31:02 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492495#M25552</guid>
      <dc:creator>PaigeMiller</dc:creator>
      <dc:date>2018-09-04T22:31:02Z</dc:date>
    </item>
    <item>
      <title>Re: collinearity between two variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492496#M25553</link>
      <description>&lt;P&gt;oh ok, thank u so much for ur answer!&amp;nbsp;&lt;/P&gt;&lt;P&gt;Then if the p-value&amp;nbsp;of the chi-square&amp;nbsp;test is less than 0.05 ==&amp;gt;&amp;nbsp;Can I assume that there is a collinearity between the two variables&lt;/P&gt;&lt;P&gt;and if the p-value is higher than 0.05 ==&amp;gt; then the 2 variables are independent&amp;nbsp;(no &lt;SPAN&gt;collinearity)&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Is this right!?&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 04 Sep 2018 22:37:12 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492496#M25553</guid>
      <dc:creator>sebai</dc:creator>
      <dc:date>2018-09-04T22:37:12Z</dc:date>
    </item>
    <item>
      <title>Re: collinearity between two variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492498#M25554</link>
      <description>&lt;P&gt;Collinearity is not the right word for categorical variables.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;p&amp;lt;0.05 indicates lack of independence.&lt;/P&gt;</description>
      <pubDate>Tue, 04 Sep 2018 22:40:52 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/collinearity-between-two-variables/m-p/492498#M25554</guid>
      <dc:creator>PaigeMiller</dc:creator>
      <dc:date>2018-09-04T22:40:52Z</dc:date>
    </item>
  </channel>
</rss>

