var(x) = E[(x - E(x))2]
= E[(x - E(x)) (x - E(x))] <-------------Expand into brackets
= E[x2 - xE(x) - xE(x) + (E(x))2] <---Simplify
= E[x2 - 2xE(x) + (E(x))2]
= E(x2) + E[-2xE(x)] + E[(E(x))2]
= E(x2) - 2E[xE(x)] + E[(E(x))2] <---Bring (-2) constant outside
= E(x2) - 2E(x)E[E(x)] + E[(E(x))2] <--- E[xE(x)] = E(x)E(x)
= E(x2) - 2E(x)E(x) + [E(x)]2 <----------E[E(x)] = E(x)
= E(x2) - 2[E(x)]2 + [E(x)]2
var(x) = E(x2) - [E(x)]2
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The mathematician spent all day trying to derive the complex formula.
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Area = 0.5*(sum of parallel sides)*height
Variance is a characteristic parameter of a probability distribution: it is not a statistic. In any particular situation (with a few strange exceptions) it has only one value and therefore cannot have any bias.
Favourable variance is that variance which is good for business while unfavourable variance is bad for business