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What is the use of coefficient of variability in statistical analysis in explaining analytical result?

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November 01, 2007 6:13PM

The coefficient of variablility is usually referred to as R-squared. It is the percentage (written in decimal form - like .80 means 80%) of the variance in the data that is explained. You want that number to get as close to 1.00 (which means 100%) as possible. If your R-squared is .65, that means that you have explained 65% of the variance, or fluctuation, in the data. To get the percentage higher, you can add more variables to your model, or attempt transformations of the current variables in your model. There is no set value that the R-squared needs to be - it is dependent on what type of analysis you are doing and what you are trying to explain. Be cautious in adding additional variables to a model just to make only a small gain in your R-squared (like 2% or less), as more variables means more potential for multicollinearity in your model. The Coefficient of Variability (CV) allows comparison of the standard deviations of different variables that are in different units of measure. For example, if you wanted to compare the length of a course of recovery from a specific infectious illness with the number of times that the patient had had that illness, you could approach the study with the coefficient of variability. In that instance, a CV might tell you -- if the numbers happened to work out this way -- that in your sampled population, relative to their means, the variability in length of illness was greater than the variability in number of times the patients had had the illness. Technically, CV is used with ratio scale variables where zero is an "absolute" zero point; i.e. a score of 0 = nothing.

CV = [(100) (s) / X ]

This statistic measures the ratio of the standard deviation of a variable relative to its mean