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The coefficient of relationship is a measure of the degree of consanguinity (or biological relationship) between two individuals. The term coefficient of relationship was defined by Sewall Wright in 1922, and was derived from his definition of the coefficient of inbreeding of 1921. The measure is most commonly used in genetics and genealogy.
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The phrase "coefficient of inbreeding" shows up in Google results over 5 million times, while "coefficient of relationship" shows up only 3.5 million times. Since "coefficient of inbreeding" is by far the more commonly used phrase, shouldn't that be the article's title (with "coefficient of relationship" redirecting to it?
The correlation coefficient is +1 in the case of a perfect direct (increasing) linear relationship (correlation), −1 in the case of a perfect inverse (decreasing) linear relationship (anti-correlation), [5] and some value in the open interval (,) in all other cases, indicating the degree of linear dependence between the variables. As it ...
Therefore the coefficient of inbreeding of individual G is = (+) = + = %. If the parents of an individual are not inbred themselves, the coefficient of inbreeding of the individual is one-half the coefficient of relationship between the parents. This can be verified in the previous example, as 12.5% is one-half of 25%, the coefficient of ...
A correlation coefficient is a numerical measure of some type of linear correlation, meaning a statistical relationship between two variables. [ a ] The variables may be two columns of a given data set of observations, often called a sample , or two components of a multivariate random variable with a known distribution .
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.
In mathematics, a function is a rule for taking an input (in the simplest case, a number or set of numbers) [5] and providing an output (which may also be a number). [5] A symbol that stands for an arbitrary input is called an independent variable, while a symbol that stands for an arbitrary output is called a dependent variable. [6]