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you calculate it by using a supercomputer that has over 100 petabytes of data it is a search engine it searches throughout the world looking for information it calculates it in the so called brain of the computer which is a mother board and hardware wall linked to the main computer than calculates sample data to answer a question that once never had an answer to it. So that means that it brings the closest correct information that it can find throughout the web and makes the no answered question answerable and thats how it calculates the variance of sample data.

Q: How do you Calculate the variance of sample data?

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Because it is in same units as the original data. For example, if you have a sample of lengths, all in centimetres, the sample variance will be in units of centrimetres2 which might be more difficult to interpret but the sample standard deviation with be in units of centimetres, which would be relatively easy to intepret with reference to the data.

The variance is: 1.6709957376e+13

You cannot prove it because it is not true.The expected value of the sample variance is the population variance but that is not the same as the two measures being the same.

The sample mean is distributed with the same mean as the popualtion mean. If the popolation variance is s2 then the sample mean has a variance is s2/n. As n increases, the distribution of the sample mean gets closer to a Gaussian - ie Normal - distribution. This is the basis of the Central Limit Theorem which is important for hypothesis testing.

The mean, by itself, does not provide sufficient information to make any assessment of the sample variance.

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For a sample, the SD is 13.53, approx.

11

The standard deviation is the square root of the variance.

The variance of this data set is 22.611

is the standrad deviation of the data values in a sample is 17 what is the variance of the data values

There only needs to be one data point to calculate 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.

Because it is in same units as the original data. For example, if you have a sample of lengths, all in centimetres, the sample variance will be in units of centrimetres2 which might be more difficult to interpret but the sample standard deviation with be in units of centimetres, which would be relatively easy to intepret with reference to the data.

It is a biased estimator. S.R.S leads to a biased sample variance but i.i.d random sampling leads to a unbiased sample variance.

No, it is biased.

The variance is: 1.6709957376e+13

You cannot prove it because it is not true.The expected value of the sample variance is the population variance but that is not the same as the two measures being the same.