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    <title>topic Re: distribution assumption using proc sgplot in SAS Programming</title>
    <link>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/358327#M274084</link>
    <description>&lt;P&gt;Typically data are not distributed as t. The t distribution arises as the sampling distribution of statistics. See the article &lt;A href="http://blogs.sas.com/content/iml/2015/12/07/proc-univariate-distributions.html" target="_self"&gt;"Why doesn't PROC UNIVARIATE support certain common distributions?"&lt;/A&gt;&lt;/P&gt;</description>
    <pubDate>Fri, 12 May 2017 19:25:47 GMT</pubDate>
    <dc:creator>Rick_SAS</dc:creator>
    <dc:date>2017-05-12T19:25:47Z</dc:date>
    <item>
      <title>distribution assumption using proc sgplot</title>
      <link>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/356005#M274080</link>
      <description>&lt;P&gt;I am using&amp;nbsp;proc sgplot to plot CDF (series statement) and PDF (density statement) for a&amp;nbsp;dataset.&amp;nbsp;&lt;/P&gt;&lt;P&gt;Does it always require to choose the distribution assumption? e.g., type= normal&lt;/P&gt;&lt;P&gt;Is it possible to plot CDF and PDF witout underline distribution assumption? and if, how to achieve that?&lt;/P&gt;&lt;P&gt;Many thanks!&lt;/P&gt;</description>
      <pubDate>Thu, 04 May 2017 15:25:29 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/356005#M274080</guid>
      <dc:creator>Jonate_H</dc:creator>
      <dc:date>2017-05-04T15:25:29Z</dc:date>
    </item>
    <item>
      <title>Re: distribution assumption using proc sgplot</title>
      <link>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/356055#M274081</link>
      <description>&lt;P&gt;Not sure if this is what you're asking, but you can plot densities and distributions in PROC SGPLOT like this&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;data normal;
   do x = -3 to 3 by 0.01;
      y_pdf = pdf('normal',x);
      y_cdf = cdf('normal',x);
      output;
   end;
run;

title 'Normal Distribution';
proc sgplot data = normal;
band x = x upper = y_pdf lower = 0 / legendlabel = 'Density';
series x = x y = y_cdf / legendlabel = 'CDF';

keylegend / location = inside position = topleft across = 1; 
yaxis label = 'Density/Probability';
xaxis label = 'x';
run;
title;&lt;/CODE&gt;&lt;/PRE&gt;</description>
      <pubDate>Thu, 04 May 2017 17:39:34 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/356055#M274081</guid>
      <dc:creator>PeterClemmensen</dc:creator>
      <dc:date>2017-05-04T17:39:34Z</dc:date>
    </item>
    <item>
      <title>Re: distribution assumption using proc sgplot</title>
      <link>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/356346#M274082</link>
      <description>&lt;P&gt;For continuous distributions, the easiest way is&amp;nbsp;to use PROC UNIVARIATE to create the CDF and PDF plots. The HISTOGRAM statement fits and optionally overlays a nonparamwetric&amp;nbsp;kernel density estimate. The CDFPLOT statement displays the empirical CDF. Here is an example:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;proc univariate data=sashelp.cars;
var mpg_highway;
histogram mpg_highway / kernel;  /* nonparametric density estimate */
cdfplot mpg_highway;
ods select Histogram CDFPlot;
run;&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;You can also fit and overlay parametric distributions. PROC UNIVARIATE supports about 20 common distributions. Here is an example of fitting lognormal distribution (maximum likelihood estimation) to the same data:&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;proc univariate data=sashelp.cars;
var mpg_highway;
histogram mpg_highway / lognormal;  /* overlay PDF */
cdfplot   mpg_highway / lognormal;  /* overlay CDF */
ods select Histogram CDFPlot;
run;&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Fri, 05 May 2017 12:55:24 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/356346#M274082</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2017-05-05T12:55:24Z</dc:date>
    </item>
    <item>
      <title>Re: distribution assumption using proc sgplot</title>
      <link>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/358319#M274083</link>
      <description>&lt;P&gt;Thank you all!&lt;/P&gt;&lt;P&gt;by the way, how can I specify the distribution as t-distribution? without enough observations, I try to avoid normal distribution assumption.&lt;/P&gt;</description>
      <pubDate>Fri, 12 May 2017 18:54:30 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/358319#M274083</guid>
      <dc:creator>Jonate_H</dc:creator>
      <dc:date>2017-05-12T18:54:30Z</dc:date>
    </item>
    <item>
      <title>Re: distribution assumption using proc sgplot</title>
      <link>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/358327#M274084</link>
      <description>&lt;P&gt;Typically data are not distributed as t. The t distribution arises as the sampling distribution of statistics. See the article &lt;A href="http://blogs.sas.com/content/iml/2015/12/07/proc-univariate-distributions.html" target="_self"&gt;"Why doesn't PROC UNIVARIATE support certain common distributions?"&lt;/A&gt;&lt;/P&gt;</description>
      <pubDate>Fri, 12 May 2017 19:25:47 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/distribution-assumption-using-proc-sgplot/m-p/358327#M274084</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2017-05-12T19:25:47Z</dc:date>
    </item>
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