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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
For example, five-, seven- and nine-point scales with a uniform distribution of responses give PCIs of 0.60, 0.57 and 0.50 respectively. The first of these problems is relatively minor as most ordinal scales with an even number of response can be extended (or reduced) by a single value to give an odd number of possible responses.
Commonality analysis is a statistical technique within multiple linear regression that decomposes a model's R 2 statistic (i.e., explained variance) by all independent variables on a dependent variable in a multiple linear regression model into commonality coefficients.
One way they might be heteroscedastic is if = (an example of a scedastic function), so the variance is proportional to the value of . More generally, if the variance-covariance matrix of disturbance ε i {\displaystyle \varepsilon _{i}} across i {\displaystyle i} has a nonconstant diagonal, the disturbance is heteroscedastic. [ 9 ]
For a given number of phases, the Erlang distribution is the phase type distribution with smallest coefficient of variation. [ 2 ] The hypoexponential distribution is a generalisation of the Erlang distribution by having different rates for each transition (the non-homogeneous case).
An upper bound on the relative bias of the estimate is provided by the coefficient of variation (the ratio of the standard deviation to the mean). [2] Under simple random sampling the relative bias is O ( n −1/2 ).
Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).
For example, a possible sampling design might be such that each element in the sample may have a different probability to be selected. In such cases, the level of correlation between the probability of selection for an element and its measured outcome can have a direct influence on the subsequent design effect.