### Question 1.

1. In a two-group randomized experimental design, a response y is measured, and the model

yiii
is to be fitted, where

μi01 xi,
εi are i.i.d. with zero means and

xi=0 in Group 1, xi=1 in Group 2.

Express the parameters β0 and β1 in terms of the group meansμ1 and μ2.

2. The same model is fitted, but now xi is defined by

xi=-1/2 in Group 1, xi=1/2 in Group 2

What do parameters β0 and β1 represent now?

3. A third definition of xi is

xi=a in Group 1, xi=b in Group 2

where a and b are arbitrary constants. What do parameters β0 and β1 represent in this general case?

### Question 2.

For the regression model

yi=g(xi)+εi

with a continuous explanatory variable x1 and a factor x2: x2i=0, i=1,...,n1; x2i=1, i=n1+1,...,n1+n2, give plots that represent the following linear models, where x3=x12

1. g(x)=β01 x12 x2
2. g(x)=β01 x13 x3
3. g(x)=β01 x12 x23 x3
4. g(x)=β01 x13 x34 x1 x2
5. g(x)=β01 x13 x35 x3 x2

### Question 3.

For a simple linear regression model yi01 xii, i=1,...,n show that the diagonal entries of the hat-matrix H are

$h_{ii}=\frac{1}{n}+\frac{(x_i-\bar{x})^2}{\sum_{j=1}^n(x_j-\bar{x})^2}$

### Question 4.

Consider a standard linear model but with correlated errors: y=Xβ+ε, where εi's have zero means and Var(ε)=V, i.e. Var(εi)=Vii, Cov(εij)=Vij
1. Find a vector of Weighted Least Squares Estimators for β that minimizes (y-Xβ)'V-1 (y-Xβ)
2. Find a variance-covariance matrix for the vector of weighted least squares estimators.
3. What is the hat-matrix H for weighted least squares linear regression?
4. Show that if, in addition, εi are normal, then weighted least squares estimators are also maximum likelihood estimators.