The relation between variance and standard deviation is __________
A - variance is the square root of standard deviation
B - square of the standard deviation is equal to variance
C - variance is equal to standard deviation
D - standard deviation is the square of the variance
Answer
610.2k+ views
Hint: As we know that the formula of standard deviation is $\sigma = \sqrt {\dfrac{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - \mathop x\limits_{}^\_ } \right)}^2}} }}{n}} $ it will told how the data will deviated from its mean and the formula of the variance is the $\dfrac{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - \mathop x\limits_{}^\_ } \right)}^2}} }}{n}$ hence from both these we can find the solution .
Complete step-by-step answer:
As the standard deviation told us that the measure of how spread out numbers are is equal to the square root of the arithmetic mean of the squares of the deviations measured from the arithmetic mean of the data .
Standard deviation formula is $\sigma = \sqrt {\dfrac{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - \mathop x\limits_{}^\_ } \right)}^2}} }}{n}} $ .......(i)
${x_i}$ the ${i_{th}}$ data point
$\mathop x\limits^\_ $ the mean of all data points
n = the number of data points
And the variance mean statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set. The average of the squared differences from the Mean.
Variance is equal to the = $\dfrac{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - \mathop x\limits_{}^\_ } \right)}^2}} }}{n}$ ......(ii)
where
${x_i}$ the ${i_{th}}$ data point
$\mathop x\limits^\_ $ the mean of all data points
n = the number of data points
Hence from the (i) and the (ii) we can say that the square of the standard deviation is equal to variance.
So option B is the correct answer .
Note: Standard deviation along median is being calculated as the formula is $\sigma = \sqrt {\dfrac{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - M} \right)}^2}} }}{n}} $
${x_i}$ the ${i_{th}}$ data point
M the median of all data points
n = the number of data points
It will show how much the data is deviated from Median .
Complete step-by-step answer:
As the standard deviation told us that the measure of how spread out numbers are is equal to the square root of the arithmetic mean of the squares of the deviations measured from the arithmetic mean of the data .
Standard deviation formula is $\sigma = \sqrt {\dfrac{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - \mathop x\limits_{}^\_ } \right)}^2}} }}{n}} $ .......(i)
${x_i}$ the ${i_{th}}$ data point
$\mathop x\limits^\_ $ the mean of all data points
n = the number of data points
And the variance mean statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set. The average of the squared differences from the Mean.
Variance is equal to the = $\dfrac{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - \mathop x\limits_{}^\_ } \right)}^2}} }}{n}$ ......(ii)
where
${x_i}$ the ${i_{th}}$ data point
$\mathop x\limits^\_ $ the mean of all data points
n = the number of data points
Hence from the (i) and the (ii) we can say that the square of the standard deviation is equal to variance.
So option B is the correct answer .
Note: Standard deviation along median is being calculated as the formula is $\sigma = \sqrt {\dfrac{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - M} \right)}^2}} }}{n}} $
${x_i}$ the ${i_{th}}$ data point
M the median of all data points
n = the number of data points
It will show how much the data is deviated from Median .
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