Search results
Results from the WOW.Com Content Network
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
In statistics, McKay's approximation of the coefficient of variation is a statistic based on a sample from a normally distributed population. It was introduced in 1932 by A. T. McKay. [1] Statistical methods for the coefficient of variation often utilizes McKay's approximation. [2] [3] [4] [5]
In statistics, the mode is the value that appears most often in a set of data values. [1] If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value (i.e., x=argmax x i P(X = x i)).
Khan Academy is an American non-profit [3] educational organization created in 2006 by Sal Khan. [1] Its goal is to create a set of online tools that help educate students. [ 4 ] The organization produces short video lessons. [ 5 ]
Coefficient of variation (CV) used as a measure of income inequality is conducted by dividing the standard deviation of the income (square root of the variance of the incomes) by the mean of income. Coefficient of variation will be therefore lower in countries with smaller standard deviations implying more equal income distribution.
Kingman's approximation states: () (+)where () is the mean waiting time, τ is the mean service time (i.e. μ = 1/τ is the service rate), λ is the mean arrival rate, ρ = λ/μ is the utilization, c a is the coefficient of variation for arrivals (that is the standard deviation of arrival times divided by the mean arrival time) and c s is the coefficient of variation for service times.
This term was intended to be analogous to the coefficient of variation, for describing multiplicative variation in log-normal data, but this definition of GCV has no theoretical basis as an estimate of itself (see also Coefficient of variation). Note that the geometric mean is smaller than the arithmetic mean.
In statistics, the Fano factor, [1] like the coefficient of variation, is a measure of the dispersion of a counting process. It was originally used to measure the Fano noise in ion detectors. It is named after Ugo Fano, an Italian-American physicist. The Fano factor after a time is defined as