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    <title>topic Comparing Regression Curves in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Comparing-Regression-Curves/m-p/544#M544</link>
    <description>I have two sets of data with Y's linearly dependent on X's.  I am trying to determine if the two sets of data are the same line or not.&lt;BR /&gt;I've looked at literature and there are 2 ways to do this.&lt;BR /&gt;1.  Compare the slopes and see if they are equal; then compare the intercepts and see if they are equal.  Using the t-test to determine if the slopes and intercepts are equal.  If both the slopes and intercepts are equal the lines are coincident.&lt;BR /&gt;&lt;BR /&gt;2.  Create a single model and with a dummy variable (Z) where Z = 1 for the first data set and Z = 0 for the second data set.  Which would give y = a + bX +cZ + dXZ.  Where a, b,c, d are coefficients, X is the independent variable, and Z is the dummy variable.  You then perform a multiple partial F-test to determine if the lines are coincident by comparing the model with the Z and XZ terms to the model without those terms.&lt;BR /&gt;&lt;BR /&gt;Is there an easy way to do either of these tests with the JMP software?&lt;BR /&gt;So far I have fit a model with X, Z, and XZ (with the center polynomials checked).  Looking at the effects tests the Z has an F ratio of 31 and Prob &amp;gt; F is &amp;lt;0.0001.  The X*Z has an F ratio of 0.3178 and Prob &amp;gt; F is 0.5765.  I am using 0.05 significance level with 21 and 18 degrees of freedom for the 2 sets of data.</description>
    <pubDate>Fri, 26 Jun 2009 01:51:28 GMT</pubDate>
    <dc:creator />
    <dc:date>2009-06-26T01:51:28Z</dc:date>
    <item>
      <title>Comparing Regression Curves</title>
      <link>https://community.jmp.com/t5/Discussions/Comparing-Regression-Curves/m-p/544#M544</link>
      <description>I have two sets of data with Y's linearly dependent on X's.  I am trying to determine if the two sets of data are the same line or not.&lt;BR /&gt;I've looked at literature and there are 2 ways to do this.&lt;BR /&gt;1.  Compare the slopes and see if they are equal; then compare the intercepts and see if they are equal.  Using the t-test to determine if the slopes and intercepts are equal.  If both the slopes and intercepts are equal the lines are coincident.&lt;BR /&gt;&lt;BR /&gt;2.  Create a single model and with a dummy variable (Z) where Z = 1 for the first data set and Z = 0 for the second data set.  Which would give y = a + bX +cZ + dXZ.  Where a, b,c, d are coefficients, X is the independent variable, and Z is the dummy variable.  You then perform a multiple partial F-test to determine if the lines are coincident by comparing the model with the Z and XZ terms to the model without those terms.&lt;BR /&gt;&lt;BR /&gt;Is there an easy way to do either of these tests with the JMP software?&lt;BR /&gt;So far I have fit a model with X, Z, and XZ (with the center polynomials checked).  Looking at the effects tests the Z has an F ratio of 31 and Prob &amp;gt; F is &amp;lt;0.0001.  The X*Z has an F ratio of 0.3178 and Prob &amp;gt; F is 0.5765.  I am using 0.05 significance level with 21 and 18 degrees of freedom for the 2 sets of data.</description>
      <pubDate>Fri, 26 Jun 2009 01:51:28 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Comparing-Regression-Curves/m-p/544#M544</guid>
      <dc:creator />
      <dc:date>2009-06-26T01:51:28Z</dc:date>
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    <item>
      <title>Re: Comparing Regression Curves</title>
      <link>https://community.jmp.com/t5/Discussions/Comparing-Regression-Curves/m-p/545#M545</link>
      <description>I think you are on the right track using Analysis of Covariance. Your result indicates that the slopes do not differ significantly (XZ-term) but the levels ("intercept") differ between the two data sets (Z-term). &lt;BR /&gt;&lt;BR /&gt;In JMP you do not need to explicitly create a 0/1 dummy variable to do this. You can just have a nominal variable with the name or ID of the two data sets.&lt;BR /&gt;&lt;BR /&gt;See example under "Analysis of Covariance with Separate Slopes" in Chapter 12 in the manual (JMP Stat Graph Guide, page 250 for JMP 8)&lt;/img&gt;</description>
      <pubDate>Fri, 26 Jun 2009 11:01:10 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Comparing-Regression-Curves/m-p/545#M545</guid>
      <dc:creator>ms</dc:creator>
      <dc:date>2009-06-26T11:01:10Z</dc:date>
    </item>
    <item>
      <title>Re: Comparing Regression Curves</title>
      <link>https://community.jmp.com/t5/Discussions/Comparing-Regression-Curves/m-p/546#M546</link>
      <description>&amp;gt; Is there an easy way to do either of these tests with&lt;BR /&gt;&amp;gt; the JMP software?&lt;BR /&gt;&lt;BR /&gt;I don't think there is an easier way to do this.</description>
      <pubDate>Fri, 26 Jun 2009 13:54:03 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Comparing-Regression-Curves/m-p/546#M546</guid>
      <dc:creator />
      <dc:date>2009-06-26T13:54:03Z</dc:date>
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