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    <title>topic Overall mean of general linear model in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48686#M2170</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt; Quite possibly a mixture problem.&amp;nbsp; It looks like 0.13% of the data significantly elevate the intercept.&amp;nbsp; That can happen.&amp;nbsp; Consider the mean net worth of a very rural county, where 200 people live, say $50,000 per person.&amp;nbsp; All of a sudden Bill Gates moves in, worth say $20B.&amp;nbsp; The mean net worth is now 1991 times as large, with only a change of 0.5% of the data.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I would wager that those 1307 exceptional cases, when examined separately, tell you something quite interesting.&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>Thu, 17 Nov 2011 15:15:21 GMT</pubDate>
    <dc:creator>SteveDenham</dc:creator>
    <dc:date>2011-11-17T15:15:21Z</dc:date>
    <item>
      <title>Overall mean of general linear model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48683#M2167</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;This is a very tricky but interesting problem. I hope some statistical expert can really help. Below is the description of the data and analysis.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;1) Data&lt;/P&gt;&lt;P&gt;1 million cases, 40 categorical variables with levels ranging from 2 to 50. The dependent variable is continuous which is pretty normally distributed.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;2) Analysis: linear regression with effect coding for all categtorical variables.&lt;/P&gt;&lt;P&gt;Step 1): The intercept (i.e., overall mean) was estimated to be 570, with all the final significant variables in the model.&lt;/P&gt;&lt;P&gt;Step 2): remove 1307 exceptional cases (with leverage&amp;gt;2p/n and standardized residuals &amp;gt; 2), the intercept became 270.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;For this analysis, the intercept changed so much from 570 to 270, with the fact that only 1307 cases deleted (compared to the sheer large sample size of 1 million). Because the intercept standards for the overall mean, this caused potential problem for the interpretation of the model: how can the overall mean change so significantly with only 1307 cases deleted?&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I don't know how the intercept (overall mean) is estimated in general linear model. Any reference book?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Please help. Thanks.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 16 Nov 2011 15:17:56 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48683#M2167</guid>
      <dc:creator>bncoxuk</dc:creator>
      <dc:date>2011-11-16T15:17:56Z</dc:date>
    </item>
    <item>
      <title>Overall mean of general linear model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48684#M2168</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Those deleted obs maybe valuable obs.&lt;/P&gt;&lt;P&gt;DId you check the COOK distance&amp;nbsp; to see the contrubution of these obs to your model?&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>Thu, 17 Nov 2011 09:54:47 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48684#M2168</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2011-11-17T09:54:47Z</dc:date>
    </item>
    <item>
      <title>Overall mean of general linear model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48685#M2169</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt; Thanks, Ksharp. COOK distance helped the model. &lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 17 Nov 2011 10:26:42 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48685#M2169</guid>
      <dc:creator>bncoxuk</dc:creator>
      <dc:date>2011-11-17T10:26:42Z</dc:date>
    </item>
    <item>
      <title>Overall mean of general linear model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48686#M2170</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt; Quite possibly a mixture problem.&amp;nbsp; It looks like 0.13% of the data significantly elevate the intercept.&amp;nbsp; That can happen.&amp;nbsp; Consider the mean net worth of a very rural county, where 200 people live, say $50,000 per person.&amp;nbsp; All of a sudden Bill Gates moves in, worth say $20B.&amp;nbsp; The mean net worth is now 1991 times as large, with only a change of 0.5% of the data.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I would wager that those 1307 exceptional cases, when examined separately, tell you something quite interesting.&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>Thu, 17 Nov 2011 15:15:21 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Overall-mean-of-general-linear-model/m-p/48686#M2170</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2011-11-17T15:15:21Z</dc:date>
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