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    <title>topic Re: Effect of a variable in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767882#M37540</link>
    <description>&lt;BLOCKQUOTE&gt;
&lt;P&gt;&lt;SPAN&gt;I first checked normality of severity with a histogram plot with kernel and normal overlay. CONCLUDED normality.&lt;/SPAN&gt;&lt;/P&gt;
&lt;/BLOCKQUOTE&gt;
&lt;P&gt;&lt;SPAN&gt;&amp;nbsp;Completely unnecessary and not needed for PROC GLM. see&amp;nbsp;&lt;A href="https://blogs.sas.com/content/iml/2018/08/27/on-the-assumptions-and-misconceptions-of-linear-regression.html" target="_blank"&gt;https://blogs.sas.com/content/iml/2018/08/27/on-the-assumptions-and-misconceptions-of-linear-regression.html&lt;/A&gt;&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;BLOCKQUOTE&gt;
&lt;P&gt;&lt;SPAN&gt;and I looked at the Pr &amp;gt; F column in the model row of the output, which is: &amp;lt;.0001:&amp;nbsp; Does this mean I have homogeneity in my variances?&lt;/SPAN&gt;&lt;/P&gt;
&lt;/BLOCKQUOTE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;Nothing in your code requests a homogeneity of variance test. So your output does not contain a homogeneity of variance test. To obtain a homogeneity of variance test, you would need to use the MEANS command with the &lt;A href="https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_glm_syntax15.htm#statug.glm.means_opt_hov" target="_self"&gt;HOVTEST option&lt;/A&gt;.&lt;/SPAN&gt;&lt;/P&gt;</description>
    <pubDate>Wed, 15 Sep 2021 12:30:02 GMT</pubDate>
    <dc:creator>PaigeMiller</dc:creator>
    <dc:date>2021-09-15T12:30:02Z</dc:date>
    <item>
      <title>Effect of a variable</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767880#M37539</link>
      <description>&lt;P&gt;Good day&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;In my data set I have three grouping variables in total, age_group no_jabs and illness, and a dependent variable severity.&amp;nbsp;&lt;/P&gt;&lt;P&gt;I want to determine whether a grouping variable, jabs, has an effect on my continuous variable, severity.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I first checked normality of severity with a histogram plot with kernel and normal overlay. CONCLUDED normality.&lt;/P&gt;&lt;P&gt;Thereafter I want to test homogeneity of my variances using Levene's test, code I used:&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;proc GLM data = health; *Variance/Levene's test;&lt;BR /&gt;&amp;nbsp; CLASS age_group no_jabs comorb;&lt;BR /&gt;&amp;nbsp; MODEL severity = age_group|no_jabs|comorb;&lt;BR /&gt;&amp;nbsp; output out=ResData r=ResVar p=PredVar;&lt;BR /&gt;run;quit;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;and I looked at the Pr &amp;gt; F column in the model row of the output, which is: &amp;lt;.0001:&amp;nbsp; Does this mean I have homogeneity in my variances?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;If so, how do I finally test the effect of the no_jabs variable on my severity variable?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 15 Sep 2021 12:14:21 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767880#M37539</guid>
      <dc:creator>Erik3</dc:creator>
      <dc:date>2021-09-15T12:14:21Z</dc:date>
    </item>
    <item>
      <title>Re: Effect of a variable</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767882#M37540</link>
      <description>&lt;BLOCKQUOTE&gt;
&lt;P&gt;&lt;SPAN&gt;I first checked normality of severity with a histogram plot with kernel and normal overlay. CONCLUDED normality.&lt;/SPAN&gt;&lt;/P&gt;
&lt;/BLOCKQUOTE&gt;
&lt;P&gt;&lt;SPAN&gt;&amp;nbsp;Completely unnecessary and not needed for PROC GLM. see&amp;nbsp;&lt;A href="https://blogs.sas.com/content/iml/2018/08/27/on-the-assumptions-and-misconceptions-of-linear-regression.html" target="_blank"&gt;https://blogs.sas.com/content/iml/2018/08/27/on-the-assumptions-and-misconceptions-of-linear-regression.html&lt;/A&gt;&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;BLOCKQUOTE&gt;
&lt;P&gt;&lt;SPAN&gt;and I looked at the Pr &amp;gt; F column in the model row of the output, which is: &amp;lt;.0001:&amp;nbsp; Does this mean I have homogeneity in my variances?&lt;/SPAN&gt;&lt;/P&gt;
&lt;/BLOCKQUOTE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;Nothing in your code requests a homogeneity of variance test. So your output does not contain a homogeneity of variance test. To obtain a homogeneity of variance test, you would need to use the MEANS command with the &lt;A href="https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_glm_syntax15.htm#statug.glm.means_opt_hov" target="_self"&gt;HOVTEST option&lt;/A&gt;.&lt;/SPAN&gt;&lt;/P&gt;</description>
      <pubDate>Wed, 15 Sep 2021 12:30:02 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767882#M37540</guid>
      <dc:creator>PaigeMiller</dc:creator>
      <dc:date>2021-09-15T12:30:02Z</dc:date>
    </item>
    <item>
      <title>Re: Effect of a variable</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767908#M37550</link>
      <description>Thank you PaigeMiller, I now use(effect of jabs on severity, use no_jabs as my GrpVar)&lt;BR /&gt;&lt;BR /&gt;ods select hovftest;&lt;BR /&gt;proc GLM data = health;&lt;BR /&gt;CLASS no_jabs;&lt;BR /&gt;MODEL severity = no_jabs / SS3;&lt;BR /&gt;means no_jabs / hovtest;&lt;BR /&gt;run;quit;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;instead of putting in ' age_group|no_jabs|comorb ' in my model statement. I got Levene's test output and assume I'm on a more correct path now.&lt;BR /&gt;&lt;BR /&gt;If I now want to determine whether no_jabs has a significant effect on my severity I used:&lt;BR /&gt;proc GLM data = health;&lt;BR /&gt;CLASS no_jabs;&lt;BR /&gt;MODEL severity = no_jabs / SS3;&lt;BR /&gt;lsmeans no_jabs / adjust=tukey pdiff;&lt;BR /&gt;run;quit;&lt;BR /&gt;&lt;BR /&gt;Does this seem OK?</description>
      <pubDate>Wed, 15 Sep 2021 14:23:10 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767908#M37550</guid>
      <dc:creator>Erik3</dc:creator>
      <dc:date>2021-09-15T14:23:10Z</dc:date>
    </item>
    <item>
      <title>Re: Effect of a variable</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767921#M37552</link>
      <description>&lt;P&gt;First, you used a model with all main effects, all two-way interactions and the three-way interaction. Now you are using a model with just one variable. I'm not sure why you switched, but if you believe that there are interactions, or even other main effects, I would use the larger model to determine what is and is not statistically significant.&lt;/P&gt;</description>
      <pubDate>Wed, 15 Sep 2021 15:20:04 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767921#M37552</guid>
      <dc:creator>PaigeMiller</dc:creator>
      <dc:date>2021-09-15T15:20:04Z</dc:date>
    </item>
    <item>
      <title>Re: Effect of a variable</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767926#M37553</link>
      <description>Ok, I'll make use of the larger model rather. I only want to see whether the one variable has an effect. But I understand your reasoning.&lt;BR /&gt;Thank you!!</description>
      <pubDate>Wed, 15 Sep 2021 16:01:37 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Effect-of-a-variable/m-p/767926#M37553</guid>
      <dc:creator>Erik3</dc:creator>
      <dc:date>2021-09-15T16:01:37Z</dc:date>
    </item>
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