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Consider the regression results given in Table 2.11. We have a predictor X and a response Y , and assume that we are unfamiliar with this type of data, except that each variable ranges from about 4 to 4. We are interested in using X to predict Y . Now the coef cient of determination takes on the value r 2 = 0.3%,which would tend to indicate that the model is not at all useful. We are tempted to conclude that there is no linear relationship between x and y. However, are we sure that there is no linear relationship between the variables It is possible that such a relationship could exist even though r 2 is small. The question is: Does there exist some systematic approach for determining whether a linear relationship exists between two variables The answer, of course, is yes: Inference in regression offers a systematic framework for assessing the signi cance of linear association between two variables. We shall examine four inferential methods in this chapter: 1. The t-test for the relationship between the response variable and the predictor variable 2. The con dence interval for the slope, 1 3. The con dence interval for the mean of the response variable given a particular value of the predictor 4. The prediction interval for a random value of the response variable given a particular value of the predictor In 3 we also investigate the F-test for the signi cance of the regression as a whole. However, for simple linear regression, the t-test and the F-test are equivalent. How do we go about performing inference in regression Take a moment to consider the form of the regression equation: y = 0 + 1 x +

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In contrast, the common in-group identity model (S. Gaertner, Dovidio, Anastasio, Bachman, & Rust, 1993; S. Gaertner & Dovidio, 2000) proposes that group identity can be a critical mediating factor. According to this model, intergroup bias and con ict can be reduced by factors that transform participants representations of memberships from two groups to one, more inclusive group. With common in-group identity, the cognitive and motivational processes that initially produced in-group favoritism are redirected to bene t the common in-group, including former out-group members. Allport s (1954, 1958) description of widening circles of inclusion, hierarchically organized, depicts a person s various in-group memberships from one s family to one s neighborhood, to one s city, to one s nation, to one s race, to all of humankind. Recognizing that racial group identity had become the dominant allegiance among many White racists, Allport questioned the accuracy of the common belief that ingroup loyalties always grow weaker the larger their circle of inclusion, which might prevent loyalty to a group more inclusive than race. Rather, Allport proposed the potential value of shifting the level of category inclusiveness from race to humankind. He recognized that the clash between the idea of race and of One World . . . is shaping into an issue that may well be the most decisive in human history. The important question is, Can a loyalty to mankind be fashioned before interracial warfare breaks out (pp. 43 44). But is it too dif cult

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In our simulation model, the transmission range of a coordinator is assumed to be 30 m for the transmission rate of 250 kbps In the simulation experiments, we simulate a scenario of 20 end devices, and their initial locations are randomly assigned within the area as shown in Figure 146 Each simulation run lasts 320 s, and each simulation result is obtained from averaging the results of 100 independent simulations Each end device maintains a rst-in, rst-out (FIFO) waiting buffer of 16 frames, and the mean frame length (ie, 1/m) is assumed to be 90 bytes (eg, 9 UBPs at the 250-kbps transmission rate, excluding PHY and MAC headers) The network load consists of uplink transmission (from end devices to the coordinator) and downlink transmission (from the coordinator to end devices) traf c When the network load (ie, Ml/m) is less than 0.

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study provided strong veri cation of the PB behavior modi cation approach, and its publication in a Skinnerian journal had an impact great enough to be called the seeds of the behavioral revolution by radical behaviorists (Malott, Whaley, & Malott, 1997, p 175) Ayllon and Michael s paper was written as though this approach derived from Skinnerian behaviorism and this error was repeated in many works that came later For example, Fordyce (see 1990) followed Michael s suggestion both in using the PB principles and in considering his pain theory to be Skinnerian The study of child behavior modi cation began similarly Following my development of the behavior modi cation principles with simple problems, I decided that a necessary step was to extend behavior analysis to more complex behavior that required long-term treatment.

3, we de ne the situation is the light traf c load On the other hand, when the network load is greater than 07, the situation is de ned as the heavy traf c load Figure 147 shows the effects of the traf c load on goodput (GP) for MBS, MBS+EWMA, and IEEE 802154 schemes We nd that MBS and MBS+ EWMA schemes achieve higher GP than IEEE 802154, and the peak values of the GP for MBS and MBS+EWMA schemes are much larger than that for IEEE 802154 The high GP for MBS and MBS+EWMA mainly results from the decrease of the number of contentions/collisions In other words, by using MBS and MBS+EWMA, the backoff overhead is signi cantly reduced and therefore the GP improves.

We also observe that the performance of MBS+ EWMA is better than that of MSB, which implies that the proposed EWMA approach effectively predicts the network condition and further reduces the occurrence of collisions Figure 148 shows the effects of the traf c load on the completion rate Rc for MBS, MBS+EWMA, and IEEE 802154 schemes, where Rc is de ned as the.

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