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For another impression of the data, we can take a series of slices of the three-dimensional data cube and lay the slices out side by side. Such an arrangement is possible using a general framework called a trellis plot (Becker, Cleveland, & Shyu, 1996; Clark, Cleveland, Denby, & Liu, 1999) as implemented in Splus (Insightful, 2001) and shown in Figure 2.11. The three panels show progressive slices of the data with linear regression lines overlaid. As the reader can see, the plots correspond to three slices of the three-dimensional cube shown in Figure 2.10, panel A, with changes in the regression line matching different portions of the hills in the data.

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Illustrated TCP/IP by Matthew G. Naugle Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471196568 Pub Date: 11/01/98

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some have no associated Y score (e.g., X = 110); and some have two or more associated Y scores (e.g., X = 80). Thus, it is not possible to estimate central tendency or dispersion of a sampling distribution of scores for each value of X unless data are grouped. Because the assumption of normality for grouped data applies to the sampling distribution, and because that distribution approaches normal as sample size increases, the assumption is acceptably met with large enough sample sizes. A useful guideline for both univariate and multivariate analyses is at least 20 deg of freedom (df ) for error. No such convenient guideline is available for ungrouped data. Although normality is not at issue when there are suf cient df for error, it is still worthwhile to examine distributions of scores within each group for possible anomalies. Frequency histograms may be especially interesting when presented separately for each group on the same scale, as in Figure 5.6, which uses the MANOVA.sas7bdat data set (from www.abacon.com/tabachnick). The grouping (class) variable, MASC, has two levels (high and low masculinity on the Bem Sex Role Scale), and the continuous variable ESTEEM (self-esteem) is shown for each group of

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The number and order of these bursts may vary within the frame, as determined by the BS uplink scheduler. 23.2.3.4 Mobility. The IEEE 802.16e-2005 amendment includes support for mobile users. It is based on scalable OFDMA, which allows varying levels of bandwidth allocation (from 1.25- to 20-MHz channels). However, supporting mobility requires signi cant work at the networking level for tasks such as roaming, handoffs, QoS, and security mobility. The WiMAX Forum Network Working Group (NWG) is working on networking-level issues [11]. This chapter concentrates on xed wireless systems, and mobile WiMAX is not considered further.

range with respect to the wall direction and at a certain minimum distance (and maximum distance, which is the transmission radius) Thus, each sensor has the ability to estimate the distance to neighboring sensors Each awake sensor located in the desired cyclic sector will report back to current node A, but only the rst such node B will receive the full message from the current node Current node A will wait to hear the forwarded message from B to one of its neighbors If successful, A will go to sleep If there is no closer node to the wall from B, a failure message is generated and the message is backtracked to A Note that no two consecutive backtrack steps are possible, so this simple greedy routing may fail Note also that the greedy-face-greedy (GFG) algorithm [12] can be used to guarantee delivery 132.

7 Sector Routing In the case of sector training [1], messages are not directed toward any particular sensor Instead, they are directed toward a sector All active sensors inside the sector receive the message One of them decides to retransmit, and others in the same sector (if more than one in a given sector is active) can overhear this transmission, which prevents them from their own retransmissions This assumes that inside a sector the sensors are within the communication range of each other This may or may not be true in general In any case, forwarding toward the sink then follows a sector-routing principle: a route is created from sector to sector, with an arbitrary sensor from each sector participating (see Fig 132, showing a route from the top sector toward the sink).