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Coefficient of Variation
The standard deviation discussed above is an absolute measure of dispersion. The corresponding relative measure is known as the coefficient of variation. This relative measure of dispersion based upon standard deviation is also called coefficient of standard deviation.
It is used in such problems where we want to compare the variability, homogeneity, stability, uniformity and consistency of two or more series. That series for which the coefficient of variation is greater is said to be more variable or conversely less consistent, less uniform, less stable or less homogeneous. On the other hand, the series for which the Coefficient of Variation is less, is said to be less variable or more consistent, more uniform, more stable or more homogeneous.
Universe or Population The word universe as used in statistics denotes the aggregate from which a sample is to be taken. According to Simpson and Kafka, a universe or populatio
Mode Mode is the value of the observation which occurs with the greatest frequency and thus it is the most fashionable value, Mode has been derived from French word La m
Formation of Continuous Frequency Distribution: Continuous frequency distribution is most popular in practice. With reference to the formation of this type of frequency distr
how to find mse from ssr table not the anova table
First we look at these charts assuming that we know both the mean and the standard deviation of the process, that is μ and σ . These values represent the acceptable values (bench
In reduced rank regression (RRR), the dependent variables are first submitted to a PCA and the scores of the units are then used as dependent variables in a series of
Weighted Arithmetic Mean Another aspect to be considered is the importance we assign to each observation. The arithmetic mean as we calculated it so far gives equal
Find the Relation between two substance: The following table shows the results obtained in experiments aimed to determine how solubility of water in benzene depends on tempera
Question 1 Suppose that you have 150 observations on production (yt) and investment (it), and you have estimated the following ADL(3,2) model: (1 – 0.5L – 0.1L2 – 0.05L3)yt = 0.7
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