To help stop the stock
The sum of standard deviations from the mean is the error.
The sum of total deviations about the mean is the total variance. * * * * * No it is not - that is the sum of their SQUARES. The sum of the deviations is always zero.
Approximately 2 standard deviations (1.96, actually) from the mean. That is important to know that if one has a sample of 1000 values, if one selects a threshold at +/- 2 standard deviations from the mean, then one expects to see about 25 values exceeding those thresholds (on each side of the mean)
It would be useful to know what the deviations were from.
For which measure of central tendency will the sum of the deviations always be zero?
The regulation of financial reporting is important in order to make sure that said financial reporting is accurate and transparent. This, in turn, is important to prevent fraud and malfeasance.
identify and report deviations
The sum of standard deviations from the mean is the error.
Deviations Project was created on 2007-02-20.
The sum of total deviations about the mean is the total variance. * * * * * No it is not - that is the sum of their SQUARES. The sum of the deviations is always zero.
All minor deviations occurring with two standard deviations under the Gaussian curve are considered normal. Deviations occurring outside of two standard deviations are considered abnormal.
equifax
The general public is interested in the reporting of a credible journalist, but not very interested in the reporting of one who isn't credible.
Approximately 2 standard deviations (1.96, actually) from the mean. That is important to know that if one has a sample of 1000 values, if one selects a threshold at +/- 2 standard deviations from the mean, then one expects to see about 25 values exceeding those thresholds (on each side of the mean)
It would be useful to know what the deviations were from.
How many standard deviations is 16.50 from the mean?
You cannot use deviations from the mean because (by definition) their sum is zero. Absolute deviations are one way of getting around that problem and they are used. Their main drawback is that they treat deviations linearly. That is to say, one large deviation is only twice as important as two deviations that are half as big. That model may be appropriate in some cases. But in many cases, big deviations are much more serious than that a squared (not squarred) version is more appropriate. Conveniently the squared version is also a feature of many parametric statistical distributions and so the distribution of the "sum of squares" is well studied and understood.