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In Hypothesis 5, we test the prediction of an inverted Ushaped relationship of worry to intention. We begin our exploration of this possibility with a graphical display. We initially focus only on the relationship between worry and intention, ignoring the potential contribution of bene ts. Figure 19.1 displays a scatter plot of worry versus intention. We have superimposed two lines on this scatter plot. First is the best- tting straight line from the regression analysis in Table 19.3, Panel B, Y = 3.10 + 0.21X. Second is a lowess smooth (Cleveland, 1979; Fox, 2000). The lowess smooth is a nonparametric line that follows the trend in the data. If the true relationship in the data is linear, then the lowess smooth should roughly approximate the straight line. Examination of the scatter plot indicates that the data are highly right skewed, with relatively few participants having worry scores above about 3 (see also Table 19.1, Panel A). The lowess line indicates that as worry increases up to about the value of 3.5, intention also increases. Above this point, intention begins to decrease consistent with the hypothesized curvilinear relationship, although we acknowledge the sparseness of the data (i.e., few data points) at the high end of the worry distribution. Figure 19.2 provides a scatter plot of the residuals from the regression equation Y = 3.10 + .021 WORRY against WORRY. Again a lowess curve is superimposed. The lowess curve highlights the curvilinearity to be modeled after the linear trend in the data has been removed. Polynomial Regression The lowess smooth in Figure 19.1 and in Figure 19.2 leads us to explore the curvilinear relationship further using a form of



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Fig. 5.5 Sonograms of the Trill, J call, and Long call of pygmy marmosets. (From de la Torre & Snowdon 2000. Reproduced with permission of Elsevier.)





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For this book, different retraction strategies implemented in Racer were compared Reloading of ontologies from a local web server can be accelerated by either loading from an image le (up to three times faster) or by cloning an ontology in memory (up to 70 times faster) For small extensional knowledge bases, cloning the ontology outperformed even the retraction of single axioms with forget statements (usually 80 times faster) However, it turned out that the fastest strategy was to keep situation individuals up to a certain number (about 20 in the tested case) before cloning a fresh pre-loaded extensional knowledge base (keeping individuals and axioms is possible only if they do not in uence later classi cations) Due to the lack of incremental classi cation algorithms, RacerPro still initiates a complete reclassi cation after each change in the ontology.

(4) (Simultaneous diagonalization of a real positive definite and a real symmetric matrix) A (m x m) real positive definite, B (m x m) real symmetric: There exists a real nonsingular (m x m) matrix T such that A TT' and B = TAT', where A is a real diagonal matrix. Note: For more details on matrix decomposition results see 6.

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(Continued) Correlates Predictors CE Coaching efficacyb Performancee Physiological /emotional statee Vicarious experience e Outside sourcese Verbal persuasion e Experience Task self-efficacyb Task orientation Ego orientation Mastery climate (team)b Performance climate (team) Team size (team) Goals (team) b Performance (team)b Performance (team)b, c Performance (team)b Attraction to group: taskb Group integration: socialb Group integration: taskb Group integration: taskb Performanceb Performance (team)b Goals (team) b Performance (team)b Performance (team)b Group integration: taskb Performance CE Outcomes 1 12 Measurement Occasionsa

Initial empirical results [45], performed with an experimental version of Pellet, indicate that such algorithms for SHOIN(D) can be quite effective Without retraction support, the time needed to compute simple reasoning problems is easily dominated by the communication overhead caused by the reasoner interface For example, accessing RacerPro via its native API using TCP is about 15 times faster then via HTTP/DIG and even 2 times faster than the access realised with the triple-oriented framework Jena2 [20] The best performance can be achieved by using the Pellet reasoner running in the same Java virtual machine as the application itself, without the need for any external communication 414 Context Providers and Context Consumers Context Providers (CPs) act also as Context Consumers (CCs) for other CPs In addition, the reference applications in 7 are naturally Context Consumers The following are examples of CPs: Location Provider.

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