1. Standard deviation is not a measure of variance: it is the square root of the variance.2. The answer depends on better than WHAT!
Standard deviation = square root of variance.
The SD is the (positive) square root of the variance.
Standard deviation = square root of variance.
If the variance is 846, then the standard deviation is 29.1, the square root of 846.
1. Standard deviation is not a measure of variance: it is the square root of the variance.2. The answer depends on better than WHAT!
Both variance and standard deviation are measures of dispersion or variability in a set of data. They both measure how far the observations are scattered away from the mean (or average). While computing the variance, you compute the deviation of each observation from the mean, square it and sum all of the squared deviations. This somewhat exaggerates the true picure because the numbers become large when you square them. So, we take the square root of the variance (to compensate for the excess) and this is known as the standard deviation. This is why the standard deviation is more often used than variance but the standard deviation is just the square root of the variance.
The standard deviation or volatility (square root of the variance) of returns.
Standard deviation is the square root of the variance.
Standard deviation = square root of variance.
No. The standard deviation is the square root of the variance.
The SD is the (positive) square root of the variance.
Standard deviation = square root of variance.
Standard deviation
No, you have it backwards, the standard deviation is the square root of the variance, so the variance is the standard deviation squared. Usually you find the variance first, as it is the average sum of squares of the distribution, and then find the standard deviation by squaring it.
The statistical term that describes the amount of variation in data is "variance." Variance quantifies how much individual data points differ from the mean of the dataset, indicating the spread of the data. A higher variance signifies greater dispersion among the data points, while a lower variance indicates that the data points are closer to the mean. Another related measure is the standard deviation, which is the square root of the variance and provides a more interpretable scale of variability.
The square of the standard deviation is called the variance. That is because the standard deviation is defined as the square root of the variance.