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operation However, we can use one of the generic data structures that we implement in Part IV Recall from the discussion in Section 77 that the data structures we implement in 19 follow a basic protocol that uses insert,remove,and findWe can then add f indKth to the implementation There are several similar alternatives All of them use the fact that, as discussed in Section 77, set could have supported the ranking operation in logarithmic time on average or logarithmic time in the worst case if we had used a sophisticated binary search tree Consequently, we can expect an O(N log N) algorithm if we exercise care The simplest method is to insert the items sequentially into a worst-case efficient binary search tree such as a red-black tree, an AA-tree, or a splay tree (we discuss these trees in later chapters) We can then call findKth and remove,as appropriate It turns out that a splay tree is an excellent choice for this application because the f indKth and insert operations are unusually efficient and remove is not terribly difficult to code We use an alternative here, however, because the implementations of these data structures that we provide in the later chapters leave implementing f indKth for you to do as an exercise We use the BinarySearchTreeWi thRank class that supports the f indKth operation and is completely implemented in Section 192 It is based on the simple binary search tree and thus does not have logarithmic worst-case performance but merely average-case performance Conseauentlv we cannot merelv insert the items seauentiallv: that would cause the , search tree to exhibit its worst-case performance There are several options One is to insert a random permutation of 1, , N into the search tree The other is to build a perfectly balanced binary search tree with a class method Because a class method would have access to the inner workings of the search tree, it could be done in linear time This routine is left for you to do as Exercise 1921 when search trees are discussed The method we use is to write a recursive routine that inserts items in a balanced order By inserting the middle item at the root and recursively building the two subtrees in the same manner, we obtain a balanced tree The cost of our routine is an acceptable O(N log N)Although not as efficient as the linear-time class routine, it does not adversely affect the asymptotic running time of the overall algorithm The remove operations are then guaranteed to be logarithmic This routine is called buildTree; it and the j osephus method are then coded as shown in Figure 143.

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A balanced search tree will work, but it is not needed if we are careful and construct a simple binary search tree that is not unbalanced at the start A class method can be used to construct a perfectly palanced tree in linear time

2E'",'(1 - cos8)),

Let us return to the bank simulation problem described in the introduction Here, we have a system in which customers arrive and wait in line until one

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Use of a Hand Calculator for Simple Regression. Some hand calculators have special keys for doing simple linear regression. Then you only need to enter the numbers Y.,x.'Y2,x 2 "",Y n ,x n and push a few special keys to obtain the least-squares regression estimates and ffi. On a hand calculator without such capability, you could organize the calculations as follows. First calculate Sx = LXI' Sy = LYp Sxx = LX(, Syy = LYl, and SXy = LXiYi' Then ffi = (nS XY - SxSy)/(nS xx - S;) and a = (Sy - ffiS)/n. You should retain a large number of digits in these calculations even if you only desire accuracy in a and ffi to a few significant digits. This is because the subtractions in this formula for ffi often involve numbers that are nearly equal. Of course formulas (3.2) could be used to calculate and ffi, but it is inconvenient to deal with the deviations Xi - i and Yi - Y on a hand calculator.

/ / Recursively construct a perfectly balanced binary search / / tree by repeated insertions in O( N log N ) time

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void buildTree( BinarySearch~reeWithRank<int> t, & int low, int high )

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You can also use a hand calculator to obtain the test statistic It I for testing f3 = o. Calculate a as the square root of [(nS yy - S;) - ~2(nSxx S;)]jn(n - 2). This is more convenient than dealing with the residuals = y; - (Ii + ~x) in formula (3.3). Divide by the square root of (nS xx S;)jn to obtain est.SD(~). Then divide I~I by est.SD(~) to obtain Itl. The p-value can be determined approximately from the t table in the Appendix. For example, suppose n = 11 and you calculate It I = 2.03. Look in the table in the row labeled 9 (= 11 - 2). Note that 2.03 falls between the numbers 1.83 and 2.26 in the columns labeled 0.10 and 0.05. Therefore the p-value of the test falls between 0.05 and 0.10.

tinsert( center ) ; buildTree( t, low, center - 1 ) ; buildTree ( t, center + 1 , high )

BinarySearchTree~ithRankiint>t;

( rank = ( rank + passes rank = people; )

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tremove( tfindKth( rank people-- ;

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