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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.
F IT is the inbreeding coefficient of an individual (I) relative to the total (T) population, as above; F IS is the inbreeding coefficient of an individual (I) relative to the subpopulation (S), using the above for subpopulations and averaging them; and F ST is the effect of subpopulations (S) compared to the total population (T), and is ...
In biology, ecology, demography, epidemiology, and many other disciplines, the growth of a population, the spread of infectious disease, etc. can be fitted using the logistic function. In agriculture the inverted logistic sigmoid function (S-curve) is used to describe the relation between crop yield and growth factors. The blue figure was made ...
Robbins [4] showed that recombination is expected to decrease the value of D in each generation by a factor (1 - c), where c is the frequency of recombination. If D between alleles at two loci at generation 0 is given the designation D 0, then in the following generation : D 1 = D 0 (1 - c) and in generation t : D t = D 0 (1 - c) t
In mathematics, a coefficient is a multiplicative factor in some term of a polynomial, a series, or any expression. For example, in the polynomial 7 x 2 − 3 x y + 1.5 + y , {\displaystyle 7x^{2}-3xy+1.5+y,} with variables x {\displaystyle x} and y {\displaystyle y} , the first two terms have the coefficients 7 and −3.
Selection coefficient, usually denoted by the letter s, is a measure used in population genetics to quantify the relative fitness of a genotype compared to other genotypes. . Selection coefficients are central to the quantitative description of evolution, since fitness differences determine the change in genotype frequencies attributable to selecti
In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.
It assumes a linear relationship between the variables and is sensitive to outliers. The best-fitting linear equation is often represented as a straight line to minimize the difference between the predicted values from the equation and the actual observed values of the dependent variable. Schematic of a scatterplot with simple line regression
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