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The coefficient of variance (C.V.) is also known as the relative standard deviation. Karl Pearson developed it and it is commonly used to measure relative variation. The coefficient of Variation is used for expressing standard deviation as a percentage of the mean. C.V. is used when the variability of either two or more series is compared. Students pursuing Statistics are often assigned assignments on C.V. This being a complex topic, student face problems to prepare assignments. This makes them avail of the Coefficient of Variation assignment help online from BookMyEssay.

What is the Coefficient of Variation?

The C.V. or the coefficient of variation is a statistical measure of data points dispersion in data series around mean. C.V. represents the ratio of the standard deviation to mean. It is a helpful statistic to compare the degree of variation from a single data series to some other though the means are significantly different from one another.

The formula of the Coefficient of Variation of C.V. is

(Standard Deviation/Mean)* 100


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 It should be used for comparing the variability of positive data on a ratio scale. It has no or little meaning regarding measurement on an interval scale. We have employed Statistics assignment experts who can solve problems and can make you understand the topic properly when you ask, "do my assignment for me on Coefficient of Variation."

The Usefulness of the Coefficient of Variation

The coefficient of variation depicts the extent of data variability in samples related to the population’s mean. In finance, C.V. enables investors to decide the amount of risk or volatility is assumed to compare the return that you expect from investments. The formula of C.V. must give a lower ratio to mean return.

The usefulness of the Coefficient of Variation or C.V. is useful while using the reward/risk ratio to investments. Investors who are risk-averse want their assets with low volatility and a greater degree of return. Conversely, risk-taking investors may invest in assets that have a greater degree of volatility. Though mostly C.V. is used for analyzing dispersion around the quartile, mean, decile, or quintile, it is used to know variation around the 10th percentile or mean.

Advantages of Coefficient of Variation

The benefits of C.V. are many. They are discussed in our (Coefficient of Variation) C.V. homework writing help. C.V. is unitless and this enables it to get compared to one another in manners that others such as standard deviations cannot be.

In a variable CV setting

The standard deviation of any two variables cannot be compared to one another though both measure dispersion as they can differ hugely in the units including the means they occur. The mean and the standard deviation of a variable have the same units so the ratios of them cause the units to get canceled. You can compare the ratio to other ratios meaningfully. The variable having a smaller CV is comparatively less dispersed than a variable with a greater CV.

In a Model CV setting

Root mean squared error or RMSE measure the magnitude of residues, however, they may not be compared with one another meaningfully for deciding which model offers a better prediction. The mean and the model RMSE are expressed in the same units and therefore, the ratio of them enables them to cancel the units. However, this ratio may be compared to other ratios meaningfully between two models, the model having a smaller CV has its predicted values, which are closer to an actual value. We, at BookMyEssay have a team of expert writers who assist you rightly and offer you top quality assignment writing help with Coefficient of Variation for your college/university projects.

Coefficient of Variation in Excel

The formula of the Coefficient of Variation (C.V) can be done in Excel using the function of Standard Deviation for a data set. Thereafter, the mean is calculated using an Excel function. As the C.V. is a standard deviation that is divided by the mean, you need to divide the cell that contains standard deviation by a cell that contains the mean.


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