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Why isn't the sum of squared deviations a proper measure of dispersion?
AIt is always negative
BIt is never computable
CIt depends on value count
DIt is always zero
Answer & Solution
Correct answer: C. It depends on value count
The raw sum grows with the number of observations, so it must be averaged to compare data sets.
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