No. Well not exactly.
The square of the standard deviation of a sample, when squared (s2) is an unbiased estimate of the variance of the population.
I would not call it crude, but just an estimate. An estimate is an approximate value of the parameter of the population you would like to know (estimand) which in this case is the variance.
standard deviation is the square roots of variance, a measure of spread or variability of data . it is given by (variance)^1/2
The standard deviation or volatility (square root of the variance) of returns.
They are effectively the same but the standard deviation is more popular because the units of measurement are the same as those for the variable.
Standard error, standard deviation, variance, range, inter-quartile range as well as measures based on other percentiles.
The 'standard deviation' in statistics or probability is a measure of how spread out the numbers are. It mathematical terms, it is the square root of the mean of the squared deviations of all the numbers in the data set from the mean of that set. It is approximately equal to the average deviation from the mean. If you have a set of values with low standard deviation, it means that in general, most of the values are close to the mean. A high standard deviation means that the values in general, differ a lot from the mean. The variance is the standard deviation squared. That is to say, the standard deviation is the square root of the variance. To calculate the variance, we simply take each number in the set and subtract it from the mean. Next square that value and do the same for each number in the set. Lastly, take the mean of all the squares. The mean of the squared deviation from the mean is the variance. The square root of the variance is the standard deviation. If you take the following data series for example, the mean for all of them is '3'. 3, 3, 3, 3, 3, 3 all the values are 3, they're the same as the mean. The standard deviation is zero. This is because the difference from the mean is zero in each case, and after squaring and then taking the mean, the variance is zero. Last, the square root of zero is zero so the standard deviation is zero. Of note is that since you are squaring the deviations from the mean, the variance and hence the standard deviation can never be negative. 1, 3, 3, 3, 3, 5 - most of the values are the same as the mean. This has a low standard deviation. In this case, the standard deviation is very small since most of the difference from the mean are small. 1, 1, 1, 5, 5, 5 - all the values are two higher or two lower than the mean. This series has the highest standard deviation.
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 is the square roots of variance, a measure of spread or variability of data . it is given by (variance)^1/2
They are measures of the spread of distributions about their mean.
The variance or standard deviation.
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.
They are effectively the same but the standard deviation is more popular because the units of measurement are the same as those for the variable.
The standard deviation is the value most used. Others are variance, interquartile range, or range.
Standard error, standard deviation, variance, range, inter-quartile range as well as measures based on other percentiles.
Generally, the standard deviation (represented by sigma, an O with a line at the top) would be used to measure variability. The standard deviation represents the average distance of data from the mean. Another measure is variance, which is the standard deviation squared. Lastly, you might use the interquartile range, which is often the range of the middle 50% of the data.
Units of measure do follow the standard deviation.
In finance, risk of investments may be measured by calculating the variance and standard deviation of the distribution of returns on those investments. Variance measures how far in either direction the amount of the returns may deviate from the mean.