Hi,
Zu (a1)
$$ \overline X = \frac{1}{n} \sum_{i=1}^n X_i $$ daraus folgt $$ E( \overline X ) = \frac{1}{n} \sum_{i=1}^n E(X_i) = \frac{1}{n}\ n\ \mu = \mu $$
zu (a2)
$$ E[ (\overline x - \mu) (\overline x - \mu)' ] = E[\overline x\ \overline x' ] - \mu\ \mu' $$ weiter gilt
$$ E[\overline x\ \overline x' ] = \frac{1}{n^2} \sum_{i,j = 1}^n E(x_i\ x_j') = \frac{1}{n^2} \left[ \sum_{i=1}^n E(x_i\ x_i')+\sum_{i,j=1 \atop i\ne j}^n E(x_i\ x_j') \right] = \frac{1}{n^2} \left[ \sum_{i=1}^n (\Sigma+\mu\mu')+n(n-1)\mu \mu' \right] = \frac{1}{n} \Sigma + \mu \mu' $$
Insgesamt also $$ E[ (\overline x - \mu) (\overline x - \mu)' ] = \frac{1}{n} \Sigma $$
zu (b1)
$$ E\left[\sqrt{n} A (\overline x - \mu)\right] = \sqrt{n}A [ E(\overline x) - \mu ] = 0 $$
zu (b2)
$$ E\left[ (\sqrt{n} A (\overline x - \mu))\ (\sqrt{n} A (\overline x - \mu))' \right] = n A\frac{1}{n}\Sigma A' = I $$