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  2. Variance - Wikipedia

    en.wikipedia.org/wiki/Variance

    If the set is a sample from the whole population, then the unbiased sample variance can be calculated as 1017.538 that is the sum of the squared deviations about the mean of the sample, divided by 11 instead of 12. A function VAR.S in Microsoft Excel gives the unbiased sample variance while VAR.P is for population variance.

  3. Unbiased estimation of standard deviation - Wikipedia

    en.wikipedia.org/wiki/Unbiased_estimation_of...

    Since the square root is a strictly concave function, it follows from Jensen's inequality that the square root of the sample variance is an underestimate. The use of n − 1 instead of n in the formula for the sample variance is known as Bessel's correction, which corrects the bias in the estimation of the population variance, and some, but not ...

  4. Algorithms for calculating variance - Wikipedia

    en.wikipedia.org/wiki/Algorithms_for_calculating...

    Based on this sample, the estimated population mean is 10, and the unbiased estimate of population variance is 30. Both the naïve algorithm and two-pass algorithm compute these values correctly. Next consider the sample (10 8 + 4, 10 8 + 7, 10 8 + 13, 10 8 + 16), which gives rise to the same estimated variance as the first sample. The two-pass ...

  5. Bias of an estimator - Wikipedia

    en.wikipedia.org/wiki/Bias_of_an_estimator

    This can be seen by noting the following formula, which follows from the Bienaymé formula, for the term in the inequality for the expectation of the uncorrected sample variance above: ⁡ [(¯)] =. In other words, the expected value of the uncorrected sample variance does not equal the population variance σ 2 , unless multiplied by a ...

  6. Expected value - Wikipedia

    en.wikipedia.org/wiki/Expected_value

    Expected values can also be used to compute the variance, by means of the computational formula for the variance ⁡ = ⁡ [] (⁡ []). A very important application of the expectation value is in the field of quantum mechanics .

  7. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    This results in an approximately-unbiased estimator for the variance of the sample mean. [48] This means that samples taken from the bootstrap distribution will have a variance which is, on average, equal to the variance of the total population. Histograms of the bootstrap distribution and the smooth bootstrap distribution appear below.

  8. Squared deviations from the mean - Wikipedia

    en.wikipedia.org/wiki/Squared_deviations_from...

    The sum of squared deviations needed to calculate sample variance (before deciding whether to divide by n or n − 1) is most easily calculated as = From the two derived expectations above the expected value of this sum is

  9. Multinomial distribution - Wikipedia

    en.wikipedia.org/wiki/Multinomial_distribution

    2.2 Expected value and variance. ... By Stirling's formula, ... The sizes of the sample and population can be n and N respectively. And in such a case, there is an ...