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    <title>topic Re: Running hedonic house price regression in SAS Programming</title>
    <link>https://communities.sas.com/t5/SAS-Programming/Running-hedonic-house-price-regression/m-p/795050#M254976</link>
    <description>&lt;P&gt;Another option is PROC PLS, which does have a CLASS statement, and produces an output statistic called TSQUARE, which is similar in its idea to Cook's D, in that TSQUARE will identify points that are extreme in the x-directions. A difference between TSQUARE and Cook's D is that if an x-variable is not a good predictor, it is weighted very lightly in TSQUARE, and the variables that are a good predictor are weighted heavily; whereas I think Cook's D weights all the x-variables equally.&lt;/P&gt;</description>
    <pubDate>Tue, 08 Feb 2022 18:57:05 GMT</pubDate>
    <dc:creator>PaigeMiller</dc:creator>
    <dc:date>2022-02-08T18:57:05Z</dc:date>
    <item>
      <title>Running hedonic house price regression</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Running-hedonic-house-price-regression/m-p/795038#M254974</link>
      <description>&lt;P&gt;Hi All,&lt;/P&gt;&lt;P&gt;I'm trying to replicate some work a colleague has done but using some different techniques.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;PRE&gt;&lt;CODE class=""&gt;proc reg data=RegA&amp;amp;i. outest=StatsA&amp;amp;i. RSQUARE noprint;
	model log_price = &amp;amp;Variables. qrt02-qrt04;
	output out=RegB&amp;amp;i. (keep=log_price
		&amp;amp;Variables. qrt02-qrt04 mtch lev cd fv res r) 
		residual=res rstudent=r h=lev cookd=cd predicted=fv;
run;&lt;/CODE&gt;&lt;/PRE&gt;&lt;P&gt;Above is an example of some code for a hedonic regression for house prices.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I would like to build on this, but one of my issues with proc reg&amp;nbsp; is it forces me to code any non numeric variables into categories.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I would like to use either proc robust reg or proc glm for my model instead as I can specify a class statement. One of my issues is that I need to calculate cooks distance in order to take out high leverage points and rerun the regression. I can't seem to find this option in either package.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Would anyone be able to provide some insight how I could output the same kind of results for the code above with either proc robust reg or proc glm.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Many thanks,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Sean&amp;nbsp;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 08 Feb 2022 17:31:00 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Running-hedonic-house-price-regression/m-p/795038#M254974</guid>
      <dc:creator>Sean_OConnor</dc:creator>
      <dc:date>2022-02-08T17:31:00Z</dc:date>
    </item>
    <item>
      <title>Re: Running hedonic house price regression</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Running-hedonic-house-price-regression/m-p/795042#M254975</link>
      <description>&lt;P&gt;What if you use PROC GLMMOD to create the dummy variables for you automatically and then use PROC REG?&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Here's how you can use PROC GLMMOD to create those dummy variables for you and then you can use the data in PROC REG:&lt;/P&gt;
&lt;P&gt;&lt;A href="https://communities.sas.com/t5/SAS-Communities-Library/How-to-create-dummy-variables-Categorical-Variables/ta-p/308484" target="_blank"&gt;https://communities.sas.com/t5/SAS-Communities-Library/How-to-create-dummy-variables-Categorical-Variables/ta-p/308484&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;BLOCKQUOTE&gt;&lt;HR /&gt;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/116786"&gt;@Sean_OConnor&lt;/a&gt;&amp;nbsp;wrote:&lt;BR /&gt;
&lt;P&gt;Hi All,&lt;/P&gt;
&lt;P&gt;I'm trying to replicate some work a colleague has done but using some different techniques.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=""&gt;proc reg data=RegA&amp;amp;i. outest=StatsA&amp;amp;i. RSQUARE noprint;
	model log_price = &amp;amp;Variables. qrt02-qrt04;
	output out=RegB&amp;amp;i. (keep=log_price
		&amp;amp;Variables. qrt02-qrt04 mtch lev cd fv res r) 
		residual=res rstudent=r h=lev cookd=cd predicted=fv;
run;&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;Above is an example of some code for a hedonic regression for house prices.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I would like to build on this, but one of my issues with proc reg&amp;nbsp; is it forces me to code any non numeric variables into categories.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I would like to use either proc robust reg or proc glm for my model instead as I can specify a class statement. One of my issues is that I need to calculate cooks distance in order to take out high leverage points and rerun the regression. I can't seem to find this option in either package.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Would anyone be able to provide some insight how I could output the same kind of results for the code above with either proc robust reg or proc glm.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Many thanks,&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Sean&amp;nbsp;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;HR /&gt;&lt;/BLOCKQUOTE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 08 Feb 2022 17:43:29 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Running-hedonic-house-price-regression/m-p/795042#M254975</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2022-02-08T17:43:29Z</dc:date>
    </item>
    <item>
      <title>Re: Running hedonic house price regression</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Running-hedonic-house-price-regression/m-p/795050#M254976</link>
      <description>&lt;P&gt;Another option is PROC PLS, which does have a CLASS statement, and produces an output statistic called TSQUARE, which is similar in its idea to Cook's D, in that TSQUARE will identify points that are extreme in the x-directions. A difference between TSQUARE and Cook's D is that if an x-variable is not a good predictor, it is weighted very lightly in TSQUARE, and the variables that are a good predictor are weighted heavily; whereas I think Cook's D weights all the x-variables equally.&lt;/P&gt;</description>
      <pubDate>Tue, 08 Feb 2022 18:57:05 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Running-hedonic-house-price-regression/m-p/795050#M254976</guid>
      <dc:creator>PaigeMiller</dc:creator>
      <dc:date>2022-02-08T18:57:05Z</dc:date>
    </item>
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