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We define the bias of an estimator H as the expected value of the estimator less the value being estimated.
Bias = E(H) -
For our case ,the bias is defined as below
Bias = E (x1. X + x2 2 X1 + 2x2 X +x2 ) - (u)
variance = E( H - E(H) )2
= E( (x1. X + x2 2 X1 + 2x2 X +x2 ) - E(x1. X + x2 2 X1 + 2x2 X +x2 ) )2
Mean squared Error = variance + ( Bias)2
Thus we substitute the variance and bias obtained above to get the mean squared error.