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Which of the following measures of dispersion is used for calculating the consistency between two series?
- a)Quartile deviation
- b)Standard Deviation
- c)Coefficient of variation
- d)None of the above
Correct answer is option 'C'. Can you explain this answer?
Verified Answer
Which of the following measures of dispersion is used for calculating ...
The coefficient of standard deviation is calculated by dividing the standard deviation of the series by its mean and then multiplying it by 100. It is regarded as the best measure of dispersion to compare two different series because it is expressed in percentage.
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Which of the following measures of dispersion is used for calculating ...
Coefficient of Variation for Consistency between Two Series
The coefficient of variation (CV) is used to measure the consistency between two series. It is a statistical measure of the relative variability or dispersion of a set of data points. The CV is expressed as a percentage and is calculated by dividing the standard deviation of a data set by its mean and then multiplying the result by 100.
Formula for Coefficient of Variation
The formula for calculating the coefficient of variation is:
CV = (standard deviation / mean) x 100%
Interpretation of Coefficient of Variation
A lower CV indicates that the data points are more consistent and have less variability. A higher CV indicates that the data points are less consistent and have more variability.
Uses of Coefficient of Variation
The coefficient of variation is commonly used in fields such as finance, economics, and engineering to compare the variability of different data sets. It is particularly useful when comparing data sets with different units or scales, as it provides a standardized measure of dispersion.
Conclusion
In summary, the coefficient of variation is used for calculating the consistency between two series. It is calculated by dividing the standard deviation of a data set by its mean and multiplying the result by 100. A lower CV indicates more consistency and less variability, while a higher CV indicates less consistency and more variability.
The coefficient of variation (CV) is used to measure the consistency between two series. It is a statistical measure of the relative variability or dispersion of a set of data points. The CV is expressed as a percentage and is calculated by dividing the standard deviation of a data set by its mean and then multiplying the result by 100.
Formula for Coefficient of Variation
The formula for calculating the coefficient of variation is:
CV = (standard deviation / mean) x 100%
Interpretation of Coefficient of Variation
A lower CV indicates that the data points are more consistent and have less variability. A higher CV indicates that the data points are less consistent and have more variability.
Uses of Coefficient of Variation
The coefficient of variation is commonly used in fields such as finance, economics, and engineering to compare the variability of different data sets. It is particularly useful when comparing data sets with different units or scales, as it provides a standardized measure of dispersion.
Conclusion
In summary, the coefficient of variation is used for calculating the consistency between two series. It is calculated by dividing the standard deviation of a data set by its mean and multiplying the result by 100. A lower CV indicates more consistency and less variability, while a higher CV indicates less consistency and more variability.
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Which of the following measures of dispersion is used for calculating ...
ANSWER c)
Coefficient of variation
The coefficient of standard deviation is calculated by dividing the standard deviation of the series by its mean and then multiplying it by 100. It is regarded as the best measure of dispersion to compare two different series because it is expressed in percentage.
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Which of the following measures of dispersion is used for calculating the consistency between two series?a)Quartile deviationb)Standard Deviationc)Coefficient of variationd)None of the aboveCorrect answer is option 'C'. Can you explain this answer?
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