The coefficient of variation and standard deviation for a dataset are 23 and 11, then the mean is approximately equal to:
47.826
The coefficient of variation (CV) is defined as the ratio of the standard deviation (σ) to the mean (μ). The formula for CV is:
CV = (σ / μ) * 100. Rearranging the formula to solve for μ:
μ = σ / (CV / 100).
Given that CV = 23 and σ = 11, we can calculate the mean as:
μ = 11 / (23 / 100) = 47.826.
Similar Questions
The coefficient of kurtosis (β2) of standard normal distribution is equal to:
Half of the difference between the 75th percentile and 25th percentile is called:
What is the value of the z-score for x = 125 if population mean and variance are 121 and 4 respectively?
The Fisher's Index
If x i| f i, i = 1, 2 … n is a frequency distribution with standard deviation 15 and mean 30, the coefficient of variation will be equals to:
If Arithmetic mean and coefficient of variation of x are 10 and 40 respectively, then the variance of y = 10 - 2x is:
For the study purpose, the mean of the observations is 148 gm and standard deviation is 17.4 gm. Approximately, the coefficient of variation equals to:
For the recorded observation, the coefficient of variation is 20 and the variance is 16. The arithmetic mean is:
Approximately, the coefficient of variation for the given data where Pearson's second measure of skewness = 0.42, arithmetic mean = 86 and median = 80, is:
Important Questions from Coefficient of Variation
The coefficient of kurtosis (β2) of standard normal distribution is equal to:
Half of the difference between the 75th percentile and 25th percentile is called:
What is the value of the z-score for x = 125 if population mean and variance are 121 and 4 respectively?
The Fisher's Index
If x i| f i, i = 1, 2 … n is a frequency distribution with standard deviation 15 and mean 30, the coefficient of variation will be equals to:
