Hierarchy in Java

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The correlation coef cient r (also known as the Pearson product moment correlation coef cient) is an indication of the strength of the linear relationship between two quantitative variables. The correlation coef cient r always takes on values between 1 and 1 inclusive. Values of r close to 1 indicate variables that are positively correlated; values of r close to 1 indicate variables that are negatively correlated. It is convenient to express the correlation coef cient as r = r 2 . When the slope b1 of the estimated regression line is positive, the correlation coef cient is also posi tive, r r 2 ; when the slope is negative, the correlation coef cient is also negative, = r = r 2. An outlier is an observation that has a very large standardized residual in absolute value. In general, if the residual is positive, we may say that the y-value observed is higher than the regression estimated given the x-value. If the residual is negative, we may say that the observed y-value is lower than the regression estimated given the x-value. A high leverage point is an observation that is extreme in the predictor space. In other words, a high leverage point takes on extreme values for the x-variable(s), without reference to the y-variable. An observation is in uential if the regression parameters alter signi cantly based on the presence or absence of the observation in the data set. An outlier may or may not be in uential. Similarly, a high leverage point may or may not be in uential. Usually, in uential observations combine both large residual and high leverage characteristics. Cook s distance measures the level of in uence of an observation by taking into account both the size of the residual and the amount of leverage for that observation. If the assumptions of the regression model are not validated, the resulting inference and model building are undermined. In the regression model, y = 0 + 1 x + , represents the error term, which is a random variable with the following assumptions: 1. The error term is a random variable with mean or expected value equal to zero. In other words, E ( ) = 0. 2. The variance of , denoted by 2 , is constant regardless of the value of x. 3. The values of are independent. 4. The error term is a normally distributed random variable. In other words, the values of the error term i are independent normal random variables with mean zero and variance 2 . In the regression model, when 1 = 0, the regression equation becomes y = 0 + , so there no longer exists a linear relationship between x and y. On the other hand, if 1 takes on any conceivable value other than zero, a linear relationship of some kind exists between the response and the predictor. We may use this key idea to apply regression-based inference. For example, the t-test tests directly whether 1 = 0, with the null hypothesis representing the claim that no linear relationship exists. We may also construct a con dence interval for the true slope of the regression line. If the con dence interval includes zero, this is evidence that no linear relationship exists. Point estimates for values of the response variable for a given value of the predictor value may be obtained by an application of the estimated regression equa tion y = b0 + b1 x. Unfortunately, these kinds of point estimates do not provide a
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1. Taking arms back; 2. Stamp step; 3. Bending knees and trunk; 4. Swinging both arms forward; 5. Extending legs; 6. Body arching; 7. Spiking arm back; 8. High elbow; 9. Glance toward opponent s block; 10. Spike emphasizing the wrist; 11. Whipping extension of arm; 12. Drawthrough of hitting arm
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<SCHEDULE startdate= 2000-04-01T14:00 TIMEZONE= +6 > <INTERVALTIME day= 7 /> <EARLIESTTIME hour= 4 min= 30 /> <LATESTTIME hour= 8 min= 30 /> </SCHEDULE>
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In this case, the Csisz r Tusnady algorithm becomes one of alternating a maximization we start with a guess of the maximizing distribution r(x) and nd the best conditional distribution, which is, by Lemma 10.8.1, q(x|y) = r(x)p(y|x) . x r(x)p(y|x) (10.146)
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metadatum (short message) that has a considerably shorter bit length (e.g., 16 bytes instead of 500) to all its neighbors. The metadatum is thus ooded. Neighboring nodes that did not yet receive the full message will reply to the short message with a request to receive it. The sensor will then respond by sending the full message to all nodes that requested it. If an omnidirectional antenna is used, sensors may retransmit the full message upon receipt of the rst request for it. Note, however, that, short request messages may be sent back to the transmitting node only if it is a neighbor in a selected sparse, connected structure, as observed in ref. [20], greatly reducing the amount of short messages needed. For example, if a LMST [18] is used as the sparse structure, the reduction is about 2/d times, where d is the average number of neighbors in the network. This reduction is possible if nodes have 2-hop topological or one-hop positional information about their neighbors. 13.3.3 Geocasting in Wireless Sensor Networks Data dissemination, or task allocation, from the sink does not need to be propagated to all active sensors. If only sensors that are close to a monitoring event (e.g., a factory that pollutes the environment) need to be alerted, then only sensors located inside a geographic region need to receive the task. This problem is known as geocasting. A survey of existing geocasting schemes is given in ref. [21]. It was shown that most existing geocasting schemes do not guarantee delivery to all nodes inside a region, the main reasons being either the partitioning of the network inside the region, or applying greedy routing instead of one that guarantees delivery. Yu, Govindan, and Estrin [22] considered a geocasting variant of the datagathering problem. They describe the geographic and energy-aware routing (GEAR) algorithm, which uses energy-aware neighbor selection to route the packet toward the target region, and recursive geographic forwarding, or restricted blind ooding algorithm, to disseminate the packet inside the destination region. Recursive forwarding applies GEAR to send messages to four subregions in the geocast region, which repeats until the region has a single node inside it. Blind ooding does not guarantee delivery to all sensors inside the region, because of possible partition inside the region (but connectivity outside it), and can be replaced by a more intelligent scheme (see 11 in this book). The GEAR algorithm selects a forwarding neighbor (among those that are closer to the destination), which minimizes a linear combination of their distance to the destination and the energy they already spent. This is almost equivalent to the cost-aware localized scheme by Stojmenovic and Lin [7], originally proposed in 1998 (described in detail in 12). Yu, Govindan, and Estrin [22] also claim that GEAR can avoid holes by applying a learning A algorithm-based approach, without presenting details. To avoid holes, one can use, for example, the depth- rst search (DFS) approach [23]. This approach requires memorizing past traf c at nodes. Unfortunately, it does not guarantee delivery to all sensors in the geocasting region, because of possible partitioning inside the geocasting region. We observe that, to guarantee delivery to all sensors in a geocasting region, and also to avoid memorization, GFG [12] can be applied rst, while some optimizations
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One of the rst rules of doing business anywhere in the world is to know your customer. Unfortunately, knowing who you do business with has become much more dif cult as our economies have expanded globally and into the virtual world. Merchants must make many tradeoffs between building high bars of entry and losses due to fraud. Unlike many nancial institutions, PayPal does not ask for large amounts of personal data when a member uses our service, making it easy to use PayPal to transact. That is one of our competitive advantages and a major value proposition for our members.
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When Netscape introduced frames with Version 3 of Navigator, designers had a problem: Did they start using a feature that not only couldn t be seen by a visitor who came to their sites from any other browser, but couldn t even be seen by a visitor who came to their sites from all previous versions of Netscape Navigator The answer is many of them started to use frames, but maintained two separate sites: one for people coming to the site with Netscape Navigator 3 and one for everyone else. They also put little icons (provided by Netscape at no charge) on their pages saying: This site looks best when viewed with Netscape Navigator. What could be more of a pain for designers What could be better for Netscape As it turned out, Microsoft ended up adding support for frames to their own browsers, making frames a standard. Now, both Netscape Communicator 4.7 and Internet Explorer 5.0 support frames. Most sites look identical in either Netscape or IE, as long as you re running the newest version of the browser.
technical, cognitive, and emotional. In addition, an overarching requirement is that the person masters the psychological skills of motivational and goal-setting strategies, imagery and mental training, and interpersonal skills. A third focus has been on the coaches who facilitate the pursuit of excellence/expertise (e.g., G. A. Bloom, Stevens, & Wickwire, 2003; C t , Salmela, Trudel, Baria, & Russell, 1995; C t & Sedgwick, 2003; Fahlstrom, 2005; Isberg, 2005; Salmela & Moraes, 2003; Wolfenden & Holt, 2005). Several scholars studying the development of talent and giftedness have noted that the mentor (i.e., the coach, in our context) is quite instrumental in both the identification and development of talent (e.g., B. S. Bloom, 1985; Ericsson, 1996; Heller, M nks, & Passow, 1993; Hemery, 1986; Salmela & Moraes, 2003). Van Rossum (1995) found that his sample of national-level Dutch athletes identified their coaches along with their parents as the most important persons in their athletic careers. Even more significant, their respective coaches were most instrumental in identifying them as talented individuals. Based on the premise that most contexts of pursuit of excellence in sport are guided and controlled by coaches, and based on the extant literature, I present next a model of the pursuit of excellence and a set of leader behavior categories best suited to facilitate the pursuit of excellence. Leadership in the Pursuit of Excellence The model of pursuit of excellence in sport is illustrated in Figure 5.4. In brief, the person with the relevant talent, dispositions, and beliefs engages in deliberate practice to master the skills and gain the strategic and tactical knowledge
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