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Define as a column vector ... The resulting estimator is biased, however, and is known as the biased sample variation. Population variance ... Mathematics portal
In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. [1] 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.
In printed mathematics, the norm is to set variables and constants in an italic typeface. [ 17 ] For example, a general quadratic function is conventionally written as a x 2 + b x + c {\textstyle ax^{2}+bx+c\,} , where a , b and c are parameters (also called constants , because they are constant functions ), while x is the variable of the function.
In statistics, the standard deviation is a measure of the amount of variation of the values of a variable about its mean. [1] A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range.
Several measures of statistical dispersion are defined in terms of the absolute deviation. The term "average absolute deviation" does not uniquely identify a measure of statistical dispersion, as there are several measures that can be used to measure absolute deviations, and there are several measures of central tendency that can be used as well.
The data set [90, 100, 110] has more variability. Its standard deviation is 10 and its average is 100, giving the coefficient of variation as 10 / 100 = 0.1; The data set [1, 5, 6, 8, 10, 40, 65, 88] has still more variability. Its standard deviation is 32.9 and its average is 27.9, giving a coefficient of variation of 32.9 / 27.9 = 1.18
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]
The term 'random variable' in its mathematical definition refers to neither randomness nor variability [2] but instead is a mathematical function in which the domain is the set of possible outcomes in a sample space (e.g. the set { H , T } {\displaystyle \{H,T\}} which are the possible upper sides of a flipped coin heads H {\displaystyle H} or ...