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.
It is equal to zero in ALL distributions.
0 (zero).
zero
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.
It is equal to zero in ALL distributions.
The sum will be zero or close to zero, depending on how the sampling was done. See related question.
No. It cannot be. Remember that when you square a negative number it becomes a positive number. Thus all squared deviations are positive and their sum must be positive.
0 (zero).
multiply the mean by the amount of numbers
zero
Mean
The definition of the mean x of a set of data is the sum of all the values divided by the total number of observations, and this value is in turn subtracted from each x value to calculate the deviations. When the deviations from the average are added up, the sum will always be zero because of the negative signs in the sum of deviations. Going back to the definition of the mean, the equation provided (x = Σxi/n) can be manipulated to read Σxi - x = 0
The sum of deviations from the mean, for any set of numbers, is always zero. For this reason it is quite useless.
variation