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    <title>topic Re: How to interpret GLM ouput? in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141938#M7396</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thanks for your inputs.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;'&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; font-size: 13px; background-color: #ffffff;"&gt;1000 is the F value when H0 is true' - if F value is significant then we should rejecting the null hypothesis. isn't? So it means H0 is NOT true. Apologize if I'm wrong.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;'P' value will be 0.05 for 95% CI. Do we've any standard (like P value) F critical value for 95% CI?&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Thu, 29 Jan 2015 09:40:17 GMT</pubDate>
    <dc:creator>Babloo</dc:creator>
    <dc:date>2015-01-29T09:40:17Z</dc:date>
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      <title>How to interpret GLM ouput?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141934#M7392</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I'm new user to Modelling and now I've struck whilst interpreting the GLM output.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;What does F value and Pr &amp;gt; F denotes? I've F value as 49.63 &lt;SPAN style="font-size: 13.3333330154419px;"&gt; Pr &amp;gt; F&lt;/SPAN&gt; as &amp;lt;.0001? When I should reject my null hypothesis for 95% CI and what is the significance behind F value?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thanks for any help you offer.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Courier New'; color: black; background: white;"&gt;&lt;BR /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Courier New'; color: black; background: white;"&gt;&lt;BR /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Courier New'; color: black; background: white;"&gt;&lt;BR /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 28 Jan 2015 13:21:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141934#M7392</guid>
      <dc:creator>Babloo</dc:creator>
      <dc:date>2015-01-28T13:21:19Z</dc:date>
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    <item>
      <title>Re: How to interpret GLM ouput?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141935#M7393</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The F-statistic compares two variances by dividing them, in the simple case of a one-way PROC GLM model, it would be the variance* of the model divided by the variance* of the residuals. A large number for the F-statistic indicates that the variability of the model term being tested is larger than the error variability, and if this number is large enough, then you say the F-statistic is statistically significant (in layman's terms, the F-statistic wouldn't get this large by random chance, and so we conclude that the result is due to a statistically significant effect). The Pr&amp;gt;F idnicates the probability of getting this large of an F-statistic if the null hypothesis was true. A Pr&amp;gt;F of less than 0.05 would indicate the effect is statistically significant with 95% confidence.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;All of this is basic hypothesis testing, and if my paragraph above is not clear to you, then you probably ought to read a text on basic statistics and hypothesis testing.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;* — well actually these are not variances but rather mean squares from the model, but they are very similar to variances&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 28 Jan 2015 13:47:50 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141935#M7393</guid>
      <dc:creator>PaigeMiller</dc:creator>
      <dc:date>2015-01-28T13:47:50Z</dc:date>
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    <item>
      <title>Re: How to interpret GLM ouput?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141936#M7394</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;What if the F value is more than 1000 ,e.g.2000. Still would be significant? &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;How the critical value being calculated in &lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; font-size: 13px; background-color: #ffffff;"&gt; F-statistic? Whether the critical value keeps changes when degrees of freedom changes.?&lt;/SPAN&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 29 Jan 2015 08:09:59 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141936#M7394</guid>
      <dc:creator>Babloo</dc:creator>
      <dc:date>2015-01-29T08:09:59Z</dc:date>
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    <item>
      <title>Re: How to interpret GLM ouput?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141937#M7395</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;"&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;What if the F value is more than 1000 ,e.g.2000. Still would be significant?"&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Yes. I think so. since all the statistical estimator is estimating a deviation. 1000 is the F value when H0 is true. F=1000 is already significant , so F=2000 would be more &lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;significant (i.e. the &lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;deviation is bigger&lt;/SPAN&gt;).&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;"&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;How the critical value being calculated in &lt;/SPAN&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;F-statistic?"&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;F estimator is a combo of two Normal distributions , check its formula at wikipedia.com&amp;nbsp;&amp;nbsp; &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&lt;BR /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&lt;BR /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;"&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Whether the critical value keeps changes when degrees of freedom changes.?"&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;No. I don't think so . It is depend on your significant degree&amp;nbsp; ALPHA . i.e.&amp;nbsp; is base on the &lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;ALPHA &lt;/SPAN&gt; percentile&amp;nbsp; .&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;It have nothing to do with DF .&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Xia Keshan&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 29 Jan 2015 09:00:58 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141937#M7395</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2015-01-29T09:00:58Z</dc:date>
    </item>
    <item>
      <title>Re: How to interpret GLM ouput?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141938#M7396</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thanks for your inputs.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;'&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; font-size: 13px; background-color: #ffffff;"&gt;1000 is the F value when H0 is true' - if F value is significant then we should rejecting the null hypothesis. isn't? So it means H0 is NOT true. Apologize if I'm wrong.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;'P' value will be 0.05 for 95% CI. Do we've any standard (like P value) F critical value for 95% CI?&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 29 Jan 2015 09:40:17 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141938#M7396</guid>
      <dc:creator>Babloo</dc:creator>
      <dc:date>2015-01-29T09:40:17Z</dc:date>
    </item>
    <item>
      <title>Re: How to interpret GLM ouput?</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141939#M7397</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Yes. You are right .&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt; F critical value for 95% CI&amp;nbsp; is the 95% percentile of F distribution.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&lt;BR /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;P=0.05= Probability( F &amp;gt; F0)&lt;BR /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 29 Jan 2015 10:09:04 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-to-interpret-GLM-ouput/m-p/141939#M7397</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2015-01-29T10:09:04Z</dc:date>
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