Yule's Y
   HOME

TheInfoList



OR:

In statistics, Yule's ''Y'', also known as the coefficient of colligation, is a measure of association between two binary variables. The measure was developed by George Udny Yule in 1912,Michel G. Soete. A new theory on the measurement of association between two binary variables in medical sciences: association can be expressed in a fraction (per unum, percentage, pro mille....) of perfect association (2013), e-article, BoekBoek.be and should not be confused with Yule's coefficient for measuring
skewness In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive, zero, negative, or undefined. For a unimodal ...
based on quartiles.


Formula

For a 2×2 table for binary variables ''U'' and ''V'' with frequencies or proportions : Yule's ''Y'' is given by :Y = \frac. Yule's ''Y'' is closely related to the
odds ratio An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B. The odds ratio is defined as the ratio of the odds of event A taking place in the presence of B, and the odds of A in the absence of B ...
''OR'' = ''ad''/(''bc'') as is seen in following formula: :Y = \frac Yule's ''Y'' varies from −1 to +1. −1 reflects total negative
correlation In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics ...
, +1 reflects perfect positive association while 0 reflects no association at all. These correspond to the values for the more common
Pearson correlation In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviation ...
. Yule's ''Y'' is also related to the similar Yule's ''Q'', which can also be expressed in terms of the odds ratio. ''Q'' and ''Y'' are related by: :Q = \frac\ , :Y = \frac\ .


Interpretation

Yule's ''Y'' gives the fraction of perfect association in per
unum Unum Group is an American insurance company headquartered in Chattanooga, Tennessee. Founded as Union Mutual in 1848 and known as UnumProvident from 1999-2007, the company is part of the Fortune 500. Unum Group was created by the 1999 merger of ...
(multiplied by 100 it represents this fraction in a more familiar percentage). Indeed, the formula transforms the original 2×2 table in a crosswise symmetric table wherein ''b'' = ''c'' = 1 and ''a'' = ''d'' = . For a crosswise symmetric table with frequencies or proportions ''a'' = ''d'' and ''b'' = ''c'' it is very easy to see that it can be split up in two tables. In such tables association can be measured in a perfectly clear way by dividing (''a'' – ''b'') by (''a'' + ''b''). In transformed tables b has to be substituted by 1 and a by . The transformed table has the same degree of association (the same OR) as the original not-crosswise symmetric table. Therefore, the association in asymmetric tables can be measured by Yule's ''Y'', interpreting it in just the same way as with symmetric tables. Of course, Yule's ''Y'' and (''a'' − ''b'')/(''a'' + ''b'') give the same result in crosswise symmetric tables, presenting the association as a fraction in both cases. Yule's ''Y'' measures association in a substantial, intuitively understandable way and therefore it is the measure of preference to measure association.


Examples

The following crosswise symmetric table : can be split up into two tables: : and : It is obvious that the degree of association equals 0.6 per unum (60%). The following asymmetric table can be transformed in a table with an equal degree of association (the odds ratios of both tables are equal). : Here follows the transformed table: : The odds ratios of both tables are equal to 9. ''Y'' = (3 − 1)/(3 + 1) = 0.5 (50%)


References

{{Reflist Summary statistics for contingency tables