Mean = 2. Variance = 1.
Variance is the squared deviation from the mean. (X bar - X data)^2
The mean, by itself, does not provide sufficient information to make any assessment of the sample variance.
Formally, the standard deviation is the square root of the variance. The variance is the mean of the squares of the difference between each observation and their mean value. An easier to remember form for variance is: the mean of the squares minus the square of the mean.
The variance is always positive. The variance is not directly related to the sign (nor magnitude) of the mean.
var(X) = (xm/a - 1)2 a/a-2 . If a < or equal to 2, the variance does not exist.
Beta is a number that describes how the volatility of a stock varies with a nominated benchmark index. It's the covariance of the stock with respect to the index divided by the variance of the index. The related link contains more information
Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.
A variance is a measure of how far a set of numbers is spread out around its mean.
Since Variance is the average of the squared distanced from the mean, Variance must be a non negative number.
Mean = 2. Variance = 1.
Variance is the squared deviation from the mean. (X bar - X data)^2
you have to first find the Mean then subtract each of the results from the mean and then square them. then you divide by the total amount of results and that gives you the variance. If you square root the variance you will get the standard deviation
mean = 5, variance = 5
The mean, by itself, does not provide sufficient information to make any assessment of the sample variance.
Formally, the standard deviation is the square root of the variance. The variance is the mean of the squares of the difference between each observation and their mean value. An easier to remember form for variance is: the mean of the squares minus the square of the mean.
you have to first find the Mean then subtract each of the results from the mean and then square them. then you divide by the total amount of results and that gives you the variance. If you square root the variance you will get the standard deviation