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Each of the selection tools I ve discussed so far has strengths and weaknesses. Some tools are smart: One might be good at selecting similar colors, while another is good at nding edges. Other tools, like the Lasso, are great when you want complete control over the tool. Knowing about these differences and choosing selection tools based on them is the foundation of successful selection strategies. After you re comfortable with these differences,

important to educate athletes about the immorality of any act that violates the essence of the pursuit of excellence (eg, doping, cheating on the rules) According to Locke (Avolio & Locke, 2002), acting in one s own interest refers to the fact that individuals must be the beneficiary of their own actions subject to virtues of honesty, integrity, independence, productiveness, justice (with regard to other people), and pride (in being a self-made soul) The coach needs to foster this rational self-interest while at the same time cultivating the sense of justice and fairness to all, including competitors Fostering Competitiveness Gill (1993, p 314) viewed competitiveness as simply an achievement orientation toward competitive sport, or a sport-specific form of achievement orientation She identified and measured three dimensions of sport orientation in her Sport Orientation Questionnaire.

The first and most dominant dimension is competitiveness: an enjoyment of competition and a desire to enter and strive for success in competitive sport achievement settings (p 318) The second dimension is the win orientation: a focus on interpersonal comparison and winning in competition (p 318) The third and final dimension is goal orientation: a focus on personal performance standards (p 318) It should be recognized that the last two dimensions parallel the concepts of task and ego orientation referred to earlier The point here is that those who pursue excellence should be high on all three dimensions of sport orientation Accordingly, a coach has to emphasize the importance of all three competitive orientations in the pursuit of excellence and spur the athlete to be growth-oriented, competitive, and focused on winning Instilling Task and Ego Orientation Individuals also differ in their orientation toward achievement situations.

Scholars have suggested that achievement situations elicit either task or ego orientations Task-oriented individuals believe that concerted and continued effort can gain the necessary ability and competence They judge their success in the activity by the extent of their learning and improvements they have made As their conception of success and failure is self-referenced, they enjoy the intrinsic value of learning and mastery of the task In contrast, ego-oriented individuals judge their success by how well they have done in comparison to others To them, success is a function of high ability and not effort, and losing after trying hard demonstrates lack of competence..

by a Hermitian (m x m) matrix A, is negative definite if A is negative definite. (See Section 9.12 for more on definite matrices.)

Table 8.1: Analysis results for the E2 IH211 queue of them can arrivals take place, namely in those states where the Erlang-2 arrival process is in its second phase. To compute the boundary probabilities, we have to solve (8.21) under the normalising condition (8.22). We find go = (0.1089,0.1911) f rom which we quickly verify that p = 1 - z,l = 0.70. In Table 8.1 we show some more analysis results. We vary X from 1 through 11. Note the increase in the average queue length with increasing utilisation. We tabulated the required number of steps in the successive substitution procedure to compute R (column SS). Note that when p increases the iterative solution of R slows down tremendously; since we stopped the iterative procedure after 100 steps, the last three rows do not represent cl accurate results. We finally note that a number of more efficient methods (based on successive substitutions) have been developed than the one presented here. These methods rely on the computation of the intermediate matrices G and U, which will be introduced and related to R in the next section. However, since these algorithms are also outperformed by the recently developed logarithmic reduction algorithm, we do not discuss them any further here; for further details, refer to [172, 1881.