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    <title>topic Re: Proc GLM: How to do partial F test? in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108167#M5724</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thanks Steve. I corrected the typo. - PG&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Mon, 17 Jun 2013 14:13:47 GMT</pubDate>
    <dc:creator>PGStats</dc:creator>
    <dc:date>2013-06-17T14:13:47Z</dc:date>
    <item>
      <title>Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108159#M5716</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Suppose we have a regression model between two variables: Yvar and Xvar. Suppose the results are grouped in terms of a variable with 3 levels (say this variable is for race: asian, black, white). If I observe the scatterplot in terms of these groupings (using different colors) and I observe that all 3 have different slopes - how I do I test this using the proc glm procedure?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;That is, if I want to test that the slopes for Yvar and Xvar are different for each level of the race variable, how do I do this with proc glm? I'm pretty sure it involves partial F-tests, but I only have experience with those using the proc reg procedure.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thanks in advance.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 14 Jun 2013 21:31:18 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108159#M5716</guid>
      <dc:creator>disguy</dc:creator>
      <dc:date>2013-06-14T21:31:18Z</dc:date>
    </item>
    <item>
      <title>Re: Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108160#M5717</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I could be entirely wrong here:&lt;/P&gt;&lt;P&gt;1. Isn't that the parameter estimated for the categorical variable?&lt;/P&gt;&lt;P&gt;2. You could use contrast statements to test specific hypothesis.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 14 Jun 2013 21:58:38 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108160#M5717</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2013-06-14T21:58:38Z</dc:date>
    </item>
    <item>
      <title>Re: Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108161#M5718</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The indicator (which has 3 levels here) is categorical, but the Y and X variables are numerical.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;What do contrast statements look like?&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 14 Jun 2013 22:18:14 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108161#M5718</guid>
      <dc:creator>disguy</dc:creator>
      <dc:date>2013-06-14T22:18:14Z</dc:date>
    </item>
    <item>
      <title>Re: Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108162#M5719</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Take a look at the Example in the documentation:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;A class="active_link" href="http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_glm_sect051.htm" title="http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_glm_sect051.htm"&gt;SAS/STAT(R) 9.22 User's Guide&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&lt;A class="active_link" href="http://www.ats.ucla.edu/stat/sas/output/sas_glm_output.htm" title="http://www.ats.ucla.edu/stat/sas/output/sas_glm_output.htm"&gt;http://www.ats.ucla.edu/stat/sas/output/sas_glm_output.htm&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;A bit more complex than what you're doing, but hopefully gives you an idea of how to specify the class statement for your categorical variable and use contrasts for testing. &lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 14 Jun 2013 22:32:27 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108162#M5719</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2013-06-14T22:32:27Z</dc:date>
    </item>
    <item>
      <title>Re: Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108163#M5720</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Testing for differences in slopes is done routinely prior to analysis of covariance (check the example in &lt;A href="http://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#statug_glm_sect050.htm" title="http://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#statug_glm_sect050.htm"&gt;SAS/STAT(R) 9.3 User's Guide&lt;/A&gt;).&amp;nbsp; You simply need to include a slope-by-class interaction term in the ANCOVA model to fit separate slopes for each class (Race) :&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;&amp;nbsp;&amp;nbsp; class Race;&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;&amp;nbsp;&amp;nbsp; model VarY = Race|VarX / solution;&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;&amp;nbsp;&amp;nbsp; estimate 'Asian vs Black' Race*VarX 1 -1 0;&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The Race*VarX term in the analysis of variance table tests for overall slope homogeneity.ESTIMATE statements test for individual differences.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;PG&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 15 Jun 2013 01:24:55 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108163#M5720</guid>
      <dc:creator>PGStats</dc:creator>
      <dc:date>2013-06-15T01:24:55Z</dc:date>
    </item>
    <item>
      <title>Re: Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108164#M5721</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thanks. I've been a little confused, and I think it might stem from my understanding of the partial F test rather than how to use SAS. So I would like to clarify that I have the right approach:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I used proc glm to get various sum of squares information to do a partial F test.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Now, for testing the hypothesis that the slopes are all different, would it be correct to describe the model as:&lt;/P&gt;&lt;P&gt;Let R be indicator variable with 3 levels.&lt;/P&gt;&lt;P&gt;Y = B_0 + B_1 X + B_2 R + B_3 XR&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Then, I would test the hypothesis that B_1 = B_2 = B_3 by setting up the model Y = B_0 + B (X + R + (X)(R))? And finally, just use the sum of squares information where this becomes my reduced model?&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 15 Jun 2013 02:13:41 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108164#M5721</guid>
      <dc:creator>disguy</dc:creator>
      <dc:date>2013-06-15T02:13:41Z</dc:date>
    </item>
    <item>
      <title>Re: Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108165#M5722</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The parallel slopes testing model is:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt; Y = B0 + B1*Asian + B2*Black + B3*White + B4*X + B5*X*Asian + B6*X*Black + B7*X*White + e&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The CLASS RACE statement creates dummy variables (Asian, Black, White) with values (1,0,0) for RACE="Asian", (0,1,0) for RACE="Black" and (0,0,1) for RACE="White". SAS will force parameters B3 and B7 to zero because they are redundant, so that the linear equations are:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Asian : Y = (B0+B1) + (B4+B5)*X&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt; Black : Y = (B0+B2) + (B4+B6)*X&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt; White : Y = B0 + B4*X&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The F test for effect RACE*X tests the hypothesis B5=B6=B7=0 which, if true, would mean that all slopes are equal.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;hth&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;PG&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Message was edited by: PG corrected typo reported by Steve Denham.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 15 Jun 2013 16:27:37 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108165#M5722</guid>
      <dc:creator>PGStats</dc:creator>
      <dc:date>2013-06-15T16:27:37Z</dc:date>
    </item>
    <item>
      <title>Re: Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108166#M5723</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;PG, I always believed that the test for the hypothesis of equal slopes (B5=B6=B7=0) was RACE*VarX, as you mentioned in your first post, and the F test for effect RACE tested B1=B2=B3=0, or equality of intercepts.&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, 17 Jun 2013 13:49:53 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108166#M5723</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2013-06-17T13:49:53Z</dc:date>
    </item>
    <item>
      <title>Re: Proc GLM: How to do partial F test?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108167#M5724</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thanks Steve. I corrected the typo. - PG&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 17 Jun 2013 14:13:47 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-GLM-How-to-do-partial-F-test/m-p/108167#M5724</guid>
      <dc:creator>PGStats</dc:creator>
      <dc:date>2013-06-17T14:13:47Z</dc:date>
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