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What can we conclude, for example, from Seltzer, Seltzer, and Sherwood s (1982) statement that 65% of the adults with mental retardation under study were not reinstitutionalized, given that we do not know the time frame being referenced How can we know whether this percentage is low or high How can we compare this rate to those found in other studies Even well-documented longitudinal studies using sophisticated analytic techniques occasionally omit this important piece of information (Zatz, 1985) The length of data collection is key to understanding the ultimate course of survival How Many People Should You Study Having speci ed in broad outline the design of a study, the nal step is to determine how many people to include in the sample Statisticians determine the minimum number of people a researcher should study by conducting a statistical power analysis (Cohen, 1990; Kraemer & Theimann, 1988).

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14331 Localization Algorithm We considered two approaches to engineer an RF-based localization system, based on measurements of received signal strength and connectivity, respectively The rst approach for RF-based localization is to use the measured signal strength of received beacon signals to estimate distance, as in the RADAR system [19], with an outdoor radio signal-propagation model We discarded this approach for several reasons relating to our short-range (10-m) radios First, signal strength at short ranges is subject to unpredictable variation due to fading, multipaths, and interferences It does not therefore correlate directly with distance Moreover, short range does not allow much gain in density of reference points when considering signal strength We have found an idealized radio model useful for predicting bounds on the quality of connectivity-based localization We chose this model because it was simple and easy to reason about mathematically This subsection presents this idealized model.

This requires speci cation of the particular hypothesis to be tested, the desired Type I and Type II error rates, and the minimum effect size considered important; for survival analysis, it also requires presaging the anticipated distribution of the hazard function and the proposed length of follow-up Biostatisticians have derived many methods for determining sample size for survival analysis, each applicable under different circumstances Donner (1984) and Lachin (1981) reviewed the literature; Freedman (1982) provided tables for two group comparisons; Makuch and Simon (1982) provided formulas for multiple group comparisons; and Rubinstein, Gail, and Santner (1981), Moussa (1988), and Lachin and Foulkes (1986) provided formulas for complex designs with strati cation, covariate information, or allowances for loss of individuals to follow-up In the presentation that follows, we have computed minimum sample sizes using the computer program of Dupont and Plummer (1990).

No single table or formula can cover all possible design con gurations Here we provide ballpark estimates of sample size, similar to those we have provided elsewhere for more familiar statistical analyses (Light, Singer, & Willett, 1990) Our discussion does not replace consultation with a statistician.

To our surprise, this model compares quite well to outdoor radio propagation in uncluttered environments, as we explore in the next subsection We make two assumptions in our idealized model: 1 Perfect spherical radio propagation 2 Identical transmission range (power) for all radios Multiple nodes in the network with overlapping regions of coverage serve as reference points (labeled R1 to Rn ) They are situated at known positions, containing their respective positions (X1 , Y1 ) to (Xn , Yn ), that form a regular mesh and transmit periodic beacon signals every T seconds We assume that neighboring reference points can be synchronized so that their beacon signal transmissions do not overlap in time Furthermore, in any time interval, each of the reference points would have transmitted exactly one beacon signal.

DIATHESIS 170 Biological Diathesis Factors 170 Psychological Diathesis Factors 173 Social Diathesis Factors 175 STRESS 176 Biological Stress Systems 176 Psychological Stress Factors 182 Social Stress Factors 183

Each mobile node listens for a xed time period t and collects all the beacon signals that it receives from various reference points We characterize the information per reference point Ri by a connectivity metric, de ned as: CMi N recv(i, t) 100 N sent(i, t).