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A first-degree relative (FDR) is a person's parent (father or mother), sibling (brother or sister) or child (son or daughter). [1] It constitutes a category of family members that largely overlaps with the term nuclear family, but without spouses. [2] If the persons are related by blood, the first degree relatives share approximately 50% of ...
In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary. [1] Estimates of statistical parameters can be based upon different amounts of information or data. The number of independent pieces of information that go into the estimate of a parameter is called the degrees ...
One legal definition of degrees of consanguinity. [1] The number next to each box in the table indicates the degree of relationship relative to the given person. Consanguinity (from Latin consanguinitas 'blood relationship') is the characteristic of having a kinship with a relative who is descended from a common ancestor.
The relative efficiency of two unbiased estimators is defined as [12] (,) = [()] [()] = ()Although is in general a function of , in many cases the dependence drops out; if this is so, being greater than one would indicate that is preferable, regardless of the true value of .
In statistics, the uncertainty coefficient, also called proficiency, entropy coefficient or Theil's U, is a measure of nominal association. It was first introduced by Henri Theil [ citation needed ] and is based on the concept of information entropy .
A strictly proper transfer function is a transfer function where the degree of the numerator is less than the degree of the denominator. The difference between the degree of the denominator (number of poles) and degree of the numerator (number of zeros) is the relative degree of the transfer function.
Intuitively, the Spearman correlation between two variables will be high when observations have a similar (or identical for a correlation of 1) rank (i.e. relative position label of the observations within the variable: 1st, 2nd, 3rd, etc.) between the two variables, and low when observations have a dissimilar (or fully opposed for a ...
In Bayesian statistics, the model is extended by adding a probability distribution over the parameter space . A statistical model can sometimes distinguish two sets of probability distributions. The first set Q = { F θ : θ ∈ Θ } {\displaystyle {\mathcal {Q}}=\{F_{\theta }:\theta \in \Theta \}} is the set of models considered for inference.