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    <title>topic Bivariate analysis in SAS Procedures</title>
    <link>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11960#M1545</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;yes its in a Yes or No format. Proc freq works out fine. Thanks. Now what if I have a continuous variable vs a categorical variable. I'm thinking proc reg should work?&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Sat, 10 Dec 2011 21:19:56 GMT</pubDate>
    <dc:creator>jazzblues24</dc:creator>
    <dc:date>2011-12-10T21:19:56Z</dc:date>
    <item>
      <title>Bivariate analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11958#M1543</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt; What is the best method of conducting a bivariate analysis of two categorical&amp;nbsp; variables? I used proc freq but I wasn't sure what to look for as a result. proc ttest doesn't seem right because I have no results for the Satterthwaite method. Any advice would be great. &lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 10 Dec 2011 20:36:04 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11958#M1543</guid>
      <dc:creator>jazzblues24</dc:creator>
      <dc:date>2011-12-10T20:36:04Z</dc:date>
    </item>
    <item>
      <title>Bivariate analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11959#M1544</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Are your data simply frequencies in an X x Y table?&amp;nbsp; If so, then I'd think that proc frequency's chi square statistic might suffice.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 10 Dec 2011 21:14:43 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11959#M1544</guid>
      <dc:creator>art297</dc:creator>
      <dc:date>2011-12-10T21:14:43Z</dc:date>
    </item>
    <item>
      <title>Bivariate analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11960#M1545</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;yes its in a Yes or No format. Proc freq works out fine. Thanks. Now what if I have a continuous variable vs a categorical variable. I'm thinking proc reg should work?&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 10 Dec 2011 21:19:56 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11960#M1545</guid>
      <dc:creator>jazzblues24</dc:creator>
      <dc:date>2011-12-10T21:19:56Z</dc:date>
    </item>
    <item>
      <title>Bivariate analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11961#M1546</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I am not a statistician.&amp;nbsp; However, if your dichotomies are all 1 and 0, and your continuous variables are at least interval or interval appearing (e.g., a Likert-type scale), I think you could use either for both.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 10 Dec 2011 22:09:39 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11961#M1546</guid>
      <dc:creator>art297</dc:creator>
      <dc:date>2011-12-10T22:09:39Z</dc:date>
    </item>
    <item>
      <title>Bivariate analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11962#M1547</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;You can't do it with proc reg because residual is required to conform Normal Distribution.&lt;/P&gt;&lt;P&gt;But for your situation, Logistic Model or proc mixed is a good choice.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Ksharp&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 12 Dec 2011 02:16:11 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11962#M1547</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2011-12-12T02:16:11Z</dc:date>
    </item>
    <item>
      <title>Bivariate analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11963#M1548</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;KSharp,&amp;nbsp; Just to make the discussion and possibilities more interesting, here is the start of a chapter from one of Gene Glass's books: &lt;A href="http://epm.sagepub.com/content/26/3/623.extract"&gt;http://epm.sagepub.com/content/26/3/623.extract&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;As I learned, again while not a statistician, one can assume that a dichotomy was forced from a factor that was actually normalliy distributed.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 12 Dec 2011 05:19:33 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11963#M1548</guid>
      <dc:creator>art297</dc:creator>
      <dc:date>2011-12-12T05:19:33Z</dc:date>
    </item>
    <item>
      <title>Bivariate analysis</title>
      <link>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11964#M1549</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Art.&lt;/P&gt;&lt;P&gt;Yes. dichotomy will conform Normal Distribution, if your sample size is quite large.&lt;/P&gt;&lt;P&gt;Actually Normal Distribution is from bivariate distribution.&lt;/P&gt;&lt;P&gt;Normal Distribution can be deduced by formula of bivariate distribution when N is large enough.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Ksharp&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 12 Dec 2011 06:05:01 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Procedures/Bivariate-analysis/m-p/11964#M1549</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2011-12-12T06:05:01Z</dc:date>
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