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    <title>topic Kendall's Tau in Quick Measures Gallery</title>
    <link>https://community.fabric.microsoft.com/t5/Quick-Measures-Gallery/Kendall-s-Tau/m-p/625107#M307</link>
    <description>&lt;P&gt;As explained here:&amp;nbsp;&lt;A href="https://www.statisticshowto.datasciencecentral.com/kendalls-tau/" target="_blank"&gt;https://www.statisticshowto.datasciencecentral.com/kendalls-tau/&lt;/A&gt;, Kendall's Tau, or Kendall Rank Correlation Coefficient, is a &lt;A style="border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; color: #05a9c5; font-family: pt sans,sans-serif; font-size: 13px; font-style: normal; font-weight: 400; outline-color: invert; outline-style: none; outline-width: 0px; text-decoration: none; vertical-align: baseline; padding: 0px; margin: 0px; border: 0px none #05a9c5;" href="https://www.statisticshowto.datasciencecentral.com/parametric-and-non-parametric-data/" target="_blank"&gt;non-parametric&lt;/A&gt; measure of relationships between columns of ranked data. The Tau correlation coefficient returns a value of 0 to 1, where:&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;0 is no relationship&lt;/LI&gt;
&lt;LI&gt;1 is a perfect relationship&lt;/LI&gt;
&lt;/UL&gt;
&lt;P&gt;A quirk of this test is that it can also produce negative values (i.e. from -1 to 0). Unlike a linear graph, a negative relationship doesn’t mean much with ranked columns (other than you perhaps switched the columns around), so just remove the negative sign when you’re interpreting Tau.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Several version’s of Tau exist.&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;Tau-A and Tau-B are usually used for square tables (with equal columns and rows). Tau-B will adjust for tied ranks&lt;/LI&gt;
&lt;LI&gt;Tau-C is usually used for rectangular tables. For square tables, Tau-B and Tau-C are essentially the same.&amp;nbsp;&lt;/LI&gt;
&lt;/UL&gt;
&lt;P&gt;Most statistical packages have Tau-B built in, but you can use the following formula to calculate it by hand:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;STRONG style="border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; font-family: pt sans,sans-serif; font-size: 13px; font-style: normal; font-weight: bold; outline-color: invert; outline-style: none; outline-width: 0px; vertical-align: baseline; padding: 0px; margin: 0px; border: 0px none #777777;"&gt;Kendall’s Tau = (C – D / C + D) &lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Where C is the number of&lt;A style="border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; color: #05a9c5; font-family: pt sans,sans-serif; font-size: 13px; font-style: normal; font-weight: 400; outline-color: invert; outline-style: none; outline-width: 0px; text-decoration: none; vertical-align: baseline; padding: 0px; margin: 0px; border: 0px none #05a9c5;" href="https://www.statisticshowto.datasciencecentral.com/concordant-pairs-discordant-pairs/" target="_blank"&gt; concordant pairs&lt;/A&gt; and D is the number of &lt;A style="border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; color: #05a9c5; font-family: pt sans,sans-serif; font-size: 13px; font-style: normal; font-weight: 400; outline-color: invert; outline-style: none; outline-width: 0px; text-decoration: none; vertical-align: baseline; padding: 0px; margin: 0px; border: 0px none #05a9c5;" href="https://www.statisticshowto.datasciencecentral.com/concordant-pairs-discordant-pairs/" target="_blank"&gt;discordant pairs&lt;/A&gt;.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Pay attention to the setup of this one, you need to make sure that you start with a sorted, ranked column of values. The measure in Power BI looks like this:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;Kendall's Tau = 
VAR __table = 'Data'
VAR __table1 = ADDCOLUMNS(__table,"__Concordant",COUNTROWS(FILTER(__table,[Index]&amp;gt;EARLIER([Index])&amp;amp;&amp;amp;[Interviewer2]&amp;gt;EARLIER([Interviewer2]))))
VAR __table2 = ADDCOLUMNS(__table1,"__Discordant",COUNTROWS(FILTER(__table,[Index]&amp;gt;EARLIER([Index])&amp;amp;&amp;amp;[Interviewer2]&amp;lt;EARLIER([Interviewer2]))))
VAR __C = SUMX(__table2,[__Concordant])
VAR __D = SUMX(__table2,[__Discordant])
RETURN
ABS(DIVIDE(__C - __D , __C + __D,0))&lt;/PRE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;SPAN class="reportid hidden"&gt;eyJrIjoiYzViZWY5NjQtMzM1Ny00MzQ4LTkxMzQtMjgzMWRhZjljYTc2IiwidCI6IjRhMDQyNzQzLTM3M2EtNDNkMi04MjdiLTAwM2Y0YzdiYTFlNSIsImMiOjN9&lt;/SPAN&gt;&lt;/P&gt;</description>
    <pubDate>Sat, 16 Feb 2019 20:17:46 GMT</pubDate>
    <dc:creator>Greg_Deckler</dc:creator>
    <dc:date>2019-02-16T20:17:46Z</dc:date>
    <item>
      <title>Kendall's Tau</title>
      <link>https://community.fabric.microsoft.com/t5/Quick-Measures-Gallery/Kendall-s-Tau/m-p/625107#M307</link>
      <description>&lt;P&gt;As explained here:&amp;nbsp;&lt;A href="https://www.statisticshowto.datasciencecentral.com/kendalls-tau/" target="_blank"&gt;https://www.statisticshowto.datasciencecentral.com/kendalls-tau/&lt;/A&gt;, Kendall's Tau, or Kendall Rank Correlation Coefficient, is a &lt;A style="border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; color: #05a9c5; font-family: pt sans,sans-serif; font-size: 13px; font-style: normal; font-weight: 400; outline-color: invert; outline-style: none; outline-width: 0px; text-decoration: none; vertical-align: baseline; padding: 0px; margin: 0px; border: 0px none #05a9c5;" href="https://www.statisticshowto.datasciencecentral.com/parametric-and-non-parametric-data/" target="_blank"&gt;non-parametric&lt;/A&gt; measure of relationships between columns of ranked data. The Tau correlation coefficient returns a value of 0 to 1, where:&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;0 is no relationship&lt;/LI&gt;
&lt;LI&gt;1 is a perfect relationship&lt;/LI&gt;
&lt;/UL&gt;
&lt;P&gt;A quirk of this test is that it can also produce negative values (i.e. from -1 to 0). Unlike a linear graph, a negative relationship doesn’t mean much with ranked columns (other than you perhaps switched the columns around), so just remove the negative sign when you’re interpreting Tau.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Several version’s of Tau exist.&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;Tau-A and Tau-B are usually used for square tables (with equal columns and rows). Tau-B will adjust for tied ranks&lt;/LI&gt;
&lt;LI&gt;Tau-C is usually used for rectangular tables. For square tables, Tau-B and Tau-C are essentially the same.&amp;nbsp;&lt;/LI&gt;
&lt;/UL&gt;
&lt;P&gt;Most statistical packages have Tau-B built in, but you can use the following formula to calculate it by hand:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;STRONG style="border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; font-family: pt sans,sans-serif; font-size: 13px; font-style: normal; font-weight: bold; outline-color: invert; outline-style: none; outline-width: 0px; vertical-align: baseline; padding: 0px; margin: 0px; border: 0px none #777777;"&gt;Kendall’s Tau = (C – D / C + D) &lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Where C is the number of&lt;A style="border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; color: #05a9c5; font-family: pt sans,sans-serif; font-size: 13px; font-style: normal; font-weight: 400; outline-color: invert; outline-style: none; outline-width: 0px; text-decoration: none; vertical-align: baseline; padding: 0px; margin: 0px; border: 0px none #05a9c5;" href="https://www.statisticshowto.datasciencecentral.com/concordant-pairs-discordant-pairs/" target="_blank"&gt; concordant pairs&lt;/A&gt; and D is the number of &lt;A style="border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; color: #05a9c5; font-family: pt sans,sans-serif; font-size: 13px; font-style: normal; font-weight: 400; outline-color: invert; outline-style: none; outline-width: 0px; text-decoration: none; vertical-align: baseline; padding: 0px; margin: 0px; border: 0px none #05a9c5;" href="https://www.statisticshowto.datasciencecentral.com/concordant-pairs-discordant-pairs/" target="_blank"&gt;discordant pairs&lt;/A&gt;.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Pay attention to the setup of this one, you need to make sure that you start with a sorted, ranked column of values. The measure in Power BI looks like this:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;Kendall's Tau = 
VAR __table = 'Data'
VAR __table1 = ADDCOLUMNS(__table,"__Concordant",COUNTROWS(FILTER(__table,[Index]&amp;gt;EARLIER([Index])&amp;amp;&amp;amp;[Interviewer2]&amp;gt;EARLIER([Interviewer2]))))
VAR __table2 = ADDCOLUMNS(__table1,"__Discordant",COUNTROWS(FILTER(__table,[Index]&amp;gt;EARLIER([Index])&amp;amp;&amp;amp;[Interviewer2]&amp;lt;EARLIER([Interviewer2]))))
VAR __C = SUMX(__table2,[__Concordant])
VAR __D = SUMX(__table2,[__Discordant])
RETURN
ABS(DIVIDE(__C - __D , __C + __D,0))&lt;/PRE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;SPAN class="reportid hidden"&gt;eyJrIjoiYzViZWY5NjQtMzM1Ny00MzQ4LTkxMzQtMjgzMWRhZjljYTc2IiwidCI6IjRhMDQyNzQzLTM3M2EtNDNkMi04MjdiLTAwM2Y0YzdiYTFlNSIsImMiOjN9&lt;/SPAN&gt;&lt;/P&gt;</description>
      <pubDate>Sat, 16 Feb 2019 20:17:46 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Quick-Measures-Gallery/Kendall-s-Tau/m-p/625107#M307</guid>
      <dc:creator>Greg_Deckler</dc:creator>
      <dc:date>2019-02-16T20:17:46Z</dc:date>
    </item>
    <item>
      <title>Re: Kendall's Tau</title>
      <link>https://community.fabric.microsoft.com/t5/Quick-Measures-Gallery/Kendall-s-Tau/m-p/739857#M358</link>
      <description>&lt;P&gt;This is really fantastic!&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Do you have any work around the Mann Kendall P value, I want to calcualte the Mann Kendall P value by multiple groups like (Region, Country, Department, Product).&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Thank you,&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Mon, 15 Jul 2019 09:50:55 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Quick-Measures-Gallery/Kendall-s-Tau/m-p/739857#M358</guid>
      <dc:creator>VandanaPulluri</dc:creator>
      <dc:date>2019-07-15T09:50:55Z</dc:date>
    </item>
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