Variance = (std dev) ^2 = 36^2 = 1296.
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No. Variance and standard deviation are dependent on, but calculated irrespective of the data. You do, of course, have to have some variation, otherwise, the variance and standard deviation will be zero.
From what ive gathered standard error is how relative to the population some data is, such as how relative an answer is to men or to women. The lower the standard error the more meaningful to the population the data is. Standard deviation is how different sets of data vary between each other, sort of like the mean. * * * * * Not true! Standard deviation is a property of the whole population or distribution. Standard error applies to a sample taken from the population and is an estimate for the standard deviation.
A higher standard deviation means that the data are fluctuating more widely with respect to the mean. It could mean there are some bad samples, or it could simply mean that the data are not as tightly bound to the mean as anticipated. An unexpected standard deviation should be evaluated, using more robust analyses techniques, so as to differentiate between the various explanations. This is an expected part of error analysis, without which an analysis is incomplete.
Here are some commonly used symbols in statistics.μ is used to denote the population mean and σ is used for the population standard deviation. Some other very common and important ones arex (with a bar on top) which is used for the sample mean and s which is used for sample standard deviation.
No. To calculate a sample standard deviation one requires the sample values. The five-number summary provides only the lowest value, the highest, the median, and the upper and lower quartiles. In any sample of size greater than five some values will be missing from the summary.