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Q: If a sample of 66 employees were taken from a population of 820 employees s2 could refer to the variance of how many of the employees salarues?

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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 n-1 indicates that the calculation is being expanded from a sample of a population to the entire population. Bessel's correction(the use of n − 1 instead of n in the formula) is where n is the number of observations in a sample: it corrects the bias in the estimation of the population variance, and some (but not all) of the bias in the estimation of the population standard deviation. That is, when estimating the population variance and standard deviation from a sample when the population mean is unknown, the sample variance is a biased estimator of the population variance, and systematically underestimates it.

that you have a large variance in the population and/or your sample size is too small

The variance is: 1.6709957376e+13

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

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66

It means you can take a measure of the variance of the sample and expect that result to be consistent for the entire population, and the sample is a valid representation for/of the population and does not influence that measure of the population.

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.

No, it is biased.

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.

The n-1 indicates that the calculation is being expanded from a sample of a population to the entire population. Bessel's correction(the use of n − 1 instead of n in the formula) is where n is the number of observations in a sample: it corrects the bias in the estimation of the population variance, and some (but not all) of the bias in the estimation of the population standard deviation. That is, when estimating the population variance and standard deviation from a sample when the population mean is unknown, the sample variance is a biased estimator of the population variance, and systematically underestimates it.

The answer depends on the underlying variance (standard deviation) in the population, the size of the sample and the procedure used to select the sample.

yes, it can be smaller, equal or larger to the true value of the population varience.

The variance decreases with a larger sample so that the sample mean is likely to be closer to the population mean.

that you have a large variance in the population and/or your sample size is too small

i mean conclucion

n = sample sizen1 = sample 1 sizen2 = sample 2 size= sample meanÎ¼0 = hypothesized population meanÎ¼1 = population 1 meanÎ¼2 = population 2 meanÏƒ = population standard deviationÏƒ2 = population variance