Search results
Results from the WOW.Com Content Network
Genetic variation can be identified at many levels. Identifying genetic variation is possible from observations of phenotypic variation in either quantitative traits (traits that vary continuously and are coded for by many genes, e.g., leg length in dogs) or discrete traits (traits that fall into discrete categories and are coded for by one or a few genes, e.g., white, pink, or red petal color ...
In contrast, a variable is a discrete variable if and only if there exists a one-to-one correspondence between this variable and a subset of , the set of natural numbers. [8] In other words, a discrete variable over a particular interval of real values is one for which, for any value in the range that the variable is permitted to take on, there ...
Polymorphism does not cover characteristics showing continuous variation (such as weight), though this has a heritable component. Polymorphism deals with forms in which the variation is discrete (discontinuous) or strongly bimodal or polymodal. [4]
The size of a tomato is one example of a complex trait. Complex traits are phenotypes that are controlled by two or more genes and do not follow Mendel's Law of Dominance. They may have a range of expression which is typically continuous. Both environmental and genetic factors often impact the variation in expression.
Calculus of variations is concerned with variations of functionals, which are small changes in the functional's value due to small changes in the function that is its argument. The first variation [l] is defined as the linear part of the change in the functional, and the second variation [m] is defined as the quadratic part. [22]
This implies that in a weighted sum of variables, the variable with the largest weight will have a disproportionally large weight in the variance of the total. For example, if X and Y are uncorrelated and the weight of X is two times the weight of Y, then the weight of the variance of X will be four times the weight of the variance of Y.
Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered. On the other hand, when the variance is small, the data in the set is clustered.
The total variation distance is half of the L 1 distance between the probability functions: on discrete domains, this is the distance between the probability mass functions [4] (,) = | () |, and when the distributions have standard probability density functions p and q, [5]