Standard Deviation of Data Solution

STEP 0: Pre-Calculation Summary
Formula Used
Standard Deviation of Data = sqrt((Sum of Squares of Individual Values/Number of Individual Values)-((Sum of Individual Values/Number of Individual Values)^2))
σ = sqrt((Σx2/N)-((Σx/N)^2))
This formula uses 1 Functions, 4 Variables
Functions Used
sqrt - A square root function is a function that takes a non-negative number as an input and returns the square root of the given input number., sqrt(Number)
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.
Sum of Squares of Individual Values - Sum of Squares of Individual Values is the sum of the squared differences between each data point and the mean of the dataset.
Number of Individual Values - Number of Individual Values is the total count of distinct data points in a dataset.
Sum of Individual Values - Sum of Individual Values is the total of all the data points in a dataset.
STEP 1: Convert Input(s) to Base Unit
Sum of Squares of Individual Values: 85 --> No Conversion Required
Number of Individual Values: 10 --> No Conversion Required
Sum of Individual Values: 15 --> No Conversion Required
STEP 2: Evaluate Formula
Substituting Input Values in Formula
σ = sqrt((Σx2/N)-((Σx/N)^2)) --> sqrt((85/10)-((15/10)^2))
Evaluating ... ...
σ = 2.5
STEP 3: Convert Result to Output's Unit
2.5 --> No Conversion Required
FINAL ANSWER
2.5 <-- 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 of Data Formula

​LaTeX ​Go
Standard Deviation of Data = sqrt((Sum of Squares of Individual Values/Number of Individual Values)-((Sum of Individual Values/Number of Individual Values)^2))
σ = sqrt((Σx2/N)-((Σx/N)^2))

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 of Data?

Standard Deviation of Data calculator uses Standard Deviation of Data = sqrt((Sum of Squares of Individual Values/Number of Individual Values)-((Sum of Individual Values/Number of Individual Values)^2)) to calculate the Standard Deviation of Data, Standard Deviation of Data 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. Standard Deviation of Data is denoted by σ symbol.

How to calculate Standard Deviation of Data using this online calculator? To use this online calculator for Standard Deviation of Data, enter Sum of Squares of Individual Values (Σx2), Number of Individual Values (N) & Sum of Individual Values (Σx) and hit the calculate button. Here is how the Standard Deviation of Data calculation can be explained with given input values -> 5.267827 = sqrt((85/10)-((15/10)^2)).

FAQ

What is Standard Deviation of Data?
Standard Deviation of Data 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 is represented as σ = sqrt((Σx2/N)-((Σx/N)^2)) or Standard Deviation of Data = sqrt((Sum of Squares of Individual Values/Number of Individual Values)-((Sum of Individual Values/Number of Individual Values)^2)). Sum of Squares of Individual Values is the sum of the squared differences between each data point and the mean of the dataset, Number of Individual Values is the total count of distinct data points in a dataset & Sum of Individual Values is the total of all the data points in a dataset.
How to calculate Standard Deviation of Data?
Standard Deviation of Data 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 is calculated using Standard Deviation of Data = sqrt((Sum of Squares of Individual Values/Number of Individual Values)-((Sum of Individual Values/Number of Individual Values)^2)). To calculate Standard Deviation of Data, you need Sum of Squares of Individual Values (Σx2), Number of Individual Values (N) & Sum of Individual Values (Σx). With our tool, you need to enter the respective value for Sum of Squares of Individual Values, Number of Individual Values & Sum of Individual Values 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 Sum of Squares of Individual Values, Number of Individual Values & Sum of Individual Values. 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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