The variance is: 79.0
The variance is: 500.0
Given a set of n scores, the variance is sum of the squared deviation divided by n or n-1. We divide by n for the population and n-1 for the sample.
The variance is: 3.8
The variance is 13.5833
The variance is: 79.0
In statistics, variance measures how far apart a set of numbers is spread out. If the numbers are identical, the variance is zero. Variance can never be negative.
The variance of this data set is 22.611
The variance is: 500.0
A variance is a measure of how far a set of numbers is spread out around its mean.
To figure the variance on a group of numbers, you must first figure out the mean, which is the average of the set. Then, substract the mean from each number in the set. Square the result of those substractions, and then average the squares. You will then have the variance.
Square the standard deviation to obtain the variance. The variance is 62 or 36.
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1. Find the mean (average) of each set. 2. Subtract each value from its set mean. 3. Square each difference. 4. Add the squared values for each set. The sum of the squared differences for each set is that set's variance. If you want to find standard deviation (a much more useful number in most cases), divide the variance by the number of values in the set minus 1 (n-1), and then take the square root of the result.
The error in which a particular numbers are set apart is called error variance.
I believe you are interested in calculating the variance from a set of data related to salaries. Variance = square of the standard deviation, where: s= square root[sum (xi- mean)2/(n-1)] where mean of the set is the sum of all data divided by the number in the sample. X of i is a single data point (single salary). If instead of a sample of data, you have the entire population of size N, substitute N for n-1 in the above equation. You may find more information on the interpretation of variance, by searching wikipedia under variance and standard deviation. I note that an advantage of using the standard deviation rather than variance, is because the standard deviation will be in the same units as the mean.
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