Standard Deviation given Coefficient of Variation Calculator

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Standard Deviation Quartile Deviation Variance

✖Mean of Data is the average value of all the data points in a dataset. It represents the central tendency of the data.ⓘ Mean of Data [μ]			+10% -10%
✖Coefficient of Variation Ratio is the ratio of the standard deviation to the mean of the data.ⓘ Coefficient of Variation Ratio [CV_Ratio]			+10% -10%

✖Standard Deviation of Data is the measure of how much the values in a dataset vary. It quantifies the dispersion of data points around the mean.ⓘ Standard Deviation given Coefficient of Variation [σ]

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Standard Deviation given Coefficient of Variation Solution

STEP 0: Pre-Calculation Summary

Formula Used

Standard Deviation of Data = Mean of Data*Coefficient of Variation Ratio
σ = μ*CV_Ratio
This formula uses 3 Variables

Variables Used

Standard Deviation of Data - Standard Deviation of Data is the measure of how much the values in a dataset vary. It quantifies the dispersion of data points around the mean.
Mean of Data - Mean of Data is the average value of all the data points in a dataset. It represents the central tendency of the data.
Coefficient of Variation Ratio - Coefficient of Variation Ratio is the ratio of the standard deviation to the mean of the data.

STEP 1: Convert Input(s) to Base Unit

Mean of Data: 1.5 --> No Conversion Required
Coefficient of Variation Ratio: 1.67 --> No Conversion Required

STEP 2: Evaluate Formula

Substituting Input Values in Formula

σ = μ*CV_Ratio --> 1.5*1.67

Evaluating ... ...

σ = 2.505

STEP 3: Convert Result to Output's Unit

2.505 --> No Conversion Required

FINAL ANSWER

2.505 <-- Standard Deviation of Data

(Calculation completed in 00.004 seconds)

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< Standard Deviation Calculators

Pooled Standard Deviation

LaTeX Go Pooled Standard Deviation = sqrt((((Size of Sample X-1)*(Standard Deviation of Sample X^2))+((Size of Sample Y-1)*(Standard Deviation of Sample Y^2)))/(Size of Sample X+Size of Sample Y-2))

Standard Deviation of Sum of Independent Random Variables

LaTeX Go Standard Deviation of Sum of Random Variables = sqrt((Standard Deviation of Random Variable X^2)+(Standard Deviation of Random Variable Y^2))

Standard Deviation given Coefficient of Variation Percentage

LaTeX Go Standard Deviation of Data = (Mean of Data*Coefficient of Variation Percentage)/100

Standard Deviation given Variance

LaTeX Go Standard Deviation of Data = sqrt(Variance of Data)

Standard Deviation given Coefficient of Variation Formula

LaTeX Go

Standard Deviation of Data = Mean of Data*Coefficient of Variation Ratio
σ = μ*CV_Ratio

What is Standard Deviation in Statistics?

In Statistics, the Standard Deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range. A useful property of the standard deviation is that, unlike the variance, it is expressed in the same unit as the data. The Standard Deviation of a random variable, sample, statistical population, data set, or probability distribution is defined and calculated as the square root of its variance.

How to Calculate Standard Deviation given Coefficient of Variation?

Standard Deviation given Coefficient of Variation calculator uses Standard Deviation of Data = Mean of Data*Coefficient of Variation Ratio to calculate the Standard Deviation of Data, Standard Deviation given Coefficient of Variation formula is defined as the measure of how much the values in a dataset vary. It quantifies the dispersion of data points around the mean, and calculated using the coefficient of variation ratio of the given data. Standard Deviation of Data is denoted by σ symbol.

How to calculate Standard Deviation given Coefficient of Variation using this online calculator? To use this online calculator for Standard Deviation given Coefficient of Variation, enter Mean of Data (μ) & Coefficient of Variation Ratio (CV_Ratio) and hit the calculate button. Here is how the Standard Deviation given Coefficient of Variation calculation can be explained with given input values -> 2.505 = 1.5*1.67.

FAQ

What is Standard Deviation given Coefficient of Variation?

Standard Deviation given Coefficient of Variation formula is defined as the measure of how much the values in a dataset vary. It quantifies the dispersion of data points around the mean, and calculated using the coefficient of variation ratio of the given data and is represented as σ = μ*CV_Ratio or Standard Deviation of Data = Mean of Data*Coefficient of Variation Ratio. Mean of Data is the average value of all the data points in a dataset. It represents the central tendency of the data & Coefficient of Variation Ratio is the ratio of the standard deviation to the mean of the data.

How to calculate Standard Deviation given Coefficient of Variation?

Standard Deviation given Coefficient of Variation formula is defined as the measure of how much the values in a dataset vary. It quantifies the dispersion of data points around the mean, and calculated using the coefficient of variation ratio of the given data is calculated using Standard Deviation of Data = Mean of Data*Coefficient of Variation Ratio. To calculate Standard Deviation given Coefficient of Variation, you need Mean of Data (μ) & Coefficient of Variation Ratio (CV_Ratio). With our tool, you need to enter the respective value for Mean of Data & Coefficient of Variation Ratio and hit the calculate button. You can also select the units (if any) for Input(s) and the Output as well.

How many ways are there to calculate Standard Deviation of Data?

In this formula, Standard Deviation of Data uses Mean of Data & Coefficient of Variation Ratio. We can use 3 other way(s) to calculate the same, which is/are as follows -

Standard Deviation of Data = sqrt(Variance of Data)
Standard Deviation of Data = (Mean of Data*Coefficient of Variation Percentage)/100
Standard Deviation of Data = sqrt((Sum of Squares of Individual Values/Number of Individual Values)-(Mean of Data^2))

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