1. Consider the following data set,
|
B |
1 |
2 |
3 |
A |
1 |
74 |
71 |
99 |
64 |
68 |
104 |
2 |
99 |
108 |
114 |
98 |
110 |
111 |
and the Balanced Factor- effects model with interactions
Yijk = μ + αi + βj + γij + εijk
i = 1,2 ,j= 1,2,3, k = 1,2
where εijk ∼ N (0, σ2).
(a) Write this model in the General Linear Model form Y = Xβ + ε.
(b) Find PX , P1, PAPB.
(c) Use the decomposition
PX = P1 + PA + PB + PAB,
to construct the ANOVA table for this model.
(d)Verify your results in (c) using R.
2. Consider the following data set, and Factor- effects model with interactions
Yijk = µ + αi + βj + γij + εijk
i = 1, 2, j = 1, 3 k = 1, . . . , nij
|
B |
1 |
2 |
3 |
A |
1 |
74 |
71 |
99 |
64 |
68 |
104 |
60 |
75 |
93 |
2 |
99 |
108 |
114 |
98 |
110 |
111 |
(a) Write this model in the General Linear Model form Y = Xβ + ε.
(b) Consider Yijk = µ + βj + γij + εijk as the reduced model and test H0: α1 = α2.
(c) Now consider Yijk = µ + βj + εijk as the reduced model and test H0: α1 = α2.
(d) Is there any discrepancy between your conclusions in (b) and (c)? Explain why.
3. For the balanced one-Factor random effects model discussed in class show
E[SSB] = (A - 1)σ2 + (N - (Σini2/N)) σa2
E[SSW] = σ2(N - A)
4. Consider the two-factor balanced additive random-effects model without interaction
Yijk = µ + ai + bj + εijk
i = 1, 2, j = 1, 2, k = 1, 2.
Suppose εijk are iid N (0, σ2) variables, ai are iid N (0, σa2) variables, bi are iid N (0, σb2) variables, Cov(εijk, ai) = 0, Cov(εijk, bj ) = 0, and Cov(ai, bj) = 0.
i. Write this model in the General Linear Mixed Model form Y = Xβ + Zu + ε.
ii. Find an expression for V = Cov(Y).
Attachment:- Notes.rar