File Name: variance and coeffecient of variance of .zip
It is helpful instead to have a dimensionless measure of dependency, such as the correlation coefficient does. So now the natural question is "what does that tell us?
In probability theory and statistics , the coefficient of variation CV , also known as relative standard deviation RSD , is a standardized measure of dispersion of a probability distribution or frequency distribution. The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay. The coefficient of variation should be computed only for data measured on a ratio scale , that is, scales that have a meaningful zero and hence allow relative comparison of two measurements i. The coefficient of variation may not have any meaning for data on an interval scale. On the other hand, Kelvin temperature has a meaningful zero, the complete absence of thermal energy, and thus is a ratio scale.
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The coefficient of variation CV is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean. It is generally expressed as a percentage. Without units, it allows for comparison between distributions of values whose scales of measurement are not comparable. When we are presented with estimated values, the CV relates the standard deviation of the estimate to the value of this estimate.
2 requires the precomputed value of before we can compute. For this reason, Eq. 4 is used often in the computations of the mean and variance. However, if you.
The variance of a discrete random variable is the sum of the square of all the values the variable can take times the probability of that value occurring minus the sum of all the values the variable can take times the probability of that value occurring squared as shown in the formula below:. The coefficient of variation of a random variable can be defined as the standard deviation divided by the mean or expected value of X, as shown in the formula below:. The variance of X is sometimes often referred to as the second moment of X about the mean.
A coefficient of variation CV can be calculated and interpreted in two different settings: analyzing a single variable and interpreting a model. The standard formulation of the CV, the ratio of the standard deviation to the mean, applies in the single variable setting. In the modeling setting, the CV is calculated as the ratio of the root mean squared error RMSE to the mean of the dependent variable. In both settings, the CV is often presented as the given ratio multiplied by The higher the CV, the greater the dispersion in the variable. The CV for a model aims to describe the model fit in terms of the relative sizes of the squared residuals and outcome values. The lower the CV, the smaller the residuals relative to the predicted value.
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