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1.2.3.5 Variance Rule
Variance rule 1: If Y = V + W, Var(Y) = Var(V) + Var(W) + 2Cov(V, W) Variance rule 2: If Y = bZ, where b is a constant, Var(Y) =
Var (Z) Variance rule 3: If Y = b, where b is a constant, Var(Y) = 0. Variance rule 4: If Y = V + b, where b is a constant, Var(Y) = Var(V) since the variance of a constant is 0.
1.2.4.0 SUMMARY
While explaining the variance and covariance, the temptations to make comparison of the two concepts may not be completely overcome. The unit briefly describes variance as the measure of spread in a population while covariance is considered as a measure of variation of two random variables. Furthermore, the unit showed that variance and covariance are dependent on the magnitude of the data values and cannot be compared therefore, regulated. This means, covariance is dividing by the product of the standard deviations of the two random variables and variance is normalised into the standard deviation by taking the square root of it.
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