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3.9. There are two approaches one can take to testing the hypothesis f3 q + 1 = ... = f3 p = O. The approach we have taken in Sections 3.8 and 3.9 is to compare the sums of squares of the residuals in the full and reduced models. Another approach is to estimate f3 q + I' ... , f3 p and see how close to 0 the estimates are. To describe the second approach, let jj = (f3 q + I' ... ,f3 p ). We want to test whether or not jj = O. The least-squares estimate &LS can be obtained as the last p - q entries in PLS. The variance-covariance matrix of &LS is the (p - q) X (p - q) matrix in the lower right corner of the variance-covariance matrix of PLS. We know from Note 3.7 that COV(PLS) = u 2 (X'X)-I. Let Va denote COV(&LS); substituting 0- 2 from (3.12), we obtain an estimate Va. A reasonable measure of how close jj is to 0 is given by &'LsVa-I&Ls. The two approaches lead to exactly the same test statistic, because it turns out that test statistic (3.l3) can be calculated as F = &'LsVa-I&Ls/(P - q). 3.10. To determine Var(p), use the fact (shown in Note 3.7) that Cov(P) = u 2 (X' X)- I. Note that Var(p) is the (j + I)th diagonal entry in Cov(P).

We begin by examining how to alter D, In solving the unweighted shortest-path problem, if D, = oo,we set D, = D, + 1 because we lower the value of D, if vertex v offers a shorter path to w The dynamics of the algorithm ensure that we need alter D, only once We add I to D, because the length of the path to w is 1 more than the length of the path to v If we apply if this logic to the weighted case we should set D, = D, + c, , this new value of D, is better than the original value However, we are no longer guaranteed that D, is altered only once Consequently, D, should be altered (rather than merely testing against if its current value is larger than D, + c , , oo)Put simply, the algorithm decides whether v should be used on the path is to w The original cost D, is the cost without using v; the cost Dv + c , , the cheapest path using v (so far) Figure 1524 shows a typical situation Earlier in the algorithm, w had its distance lowered to 8 when the eyeball visited vertex u However, when the eyeball visits vertex v, vertex w needs to have its distance lowered to 6 because we have a new shortest path This result never occurs in the unweighted algorithm because all edges add I to the path length, so D , 5 DL,implies D, + 1 I DL + I and thus D , I D , + I Here, even though D, 5 D,, we can still improve the path to w by considering v Figure 1524 illustrates another important point When w has its distance lowered, it does so only because it is adjacent to some vertex that has been visited by the eyeball For instance, after the eyeball visits v and processing has been completed the value of D,, is 6 and the last vertex on the path is a vertex that has been visited by the eyeball Similarly, the vertex prior to v must also have been visited by the eyeball and so on Thus at any point the value of D, represents a path from S to w using only vertices that have been visited by the eyeball as intermediate nodes This crucial fact gives us Theorem 151.

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If we move the eyeball to the unseen vertex with minimum D,, the algorithm correctly produces the shortest paths if there are no negative edge costs

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