4 confidence and prediction intervals in multiple linear regression re
4. Confidence and prediction intervals in multiple linear regression.
Recall that the least square estimate of the coefficients = (XX)-¹X¹Y follows
B~ N(B, o²(XX)-¹)
where N(μ, Σ) denotes the multivariate normal distribution with mean vector μ and
variance-covariance matrix Σ
(c) Find a 95% prediction interval for a new response at factor level 1 of A and factor
level 1 of B in problem 2 under a two-way ANOVA model without interaction. You may
use the value 10.025,13 = 2.16.
Note: The matrix XTX may be computed by hand. In R, you can create a matrix
by using the command
M<- matrix(c(1,2,3,4,5,6,7,8,9), nrow-3, ncol-3),
M <- matrix (1:9, 3, 3).
The inverse of the matrix can be found using the solve() function:
Minv<- solve (M).
Finally, if you have a vector v, then vMly may be computed by running
v %*% Minv * V.
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