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rank among the leaves of T of the rightmost leaf of the left subtree of T 0 (v). By the inductive hypothesis, n(v) q. Since u is a leaf in T 0 , it has exactly two children in T, namely, the leaves of ranks q 1 and q 2. Thus, in this case, n(u) n(v) 1 q 1, as claimed. Case 2: u is an ancestor of v in T 0 . Let T 0 (u) be the subtree of T 0 rooted at u. In this case, v must be the rightmost leaf in the left subtree of T 0 (u). Assume that n(v) r. Observe that v has exactly two leaf children T. By the induction hypothesis, these children have ranks r and r 1. Thus, in this case, n(u) n(v) 1 r 1, as claimed. This completes the proof of the lemma. B

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Here the resemblance to the linear regression model should be obvious. The right-hand side is the same as in Equation 20.3. The left-hand side is the log odds of event occurrence, also called the logit of event occurrence. The logit can take on any value from minus to plus in nity, and is therefore unrestricted in value, just like the right-hand side. The betas are interpreted just as in linear regression: A unit increase in X k changes the logit by k units, controlling for other covariates in the model. However, in that changes in log odds are dif cult to translate into intuitive terms, we will nd it easier to interpret variable effects in terms of Equation 20.6 (discussed later).

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