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presented in Table 28.1, it appears unlikely that widely different patterns would have been observed. Certainly, however, the percentage of athletes exceeding a score of 3 on any given subscale (particularly the Exhaustion subscale) in some Table 28.1 data collections would be substantially higher than what is presented in Table 28.2. Exhaustion alone, however, is not a sufficient criterion to indicate burnout even if it is a core characteristic of the syndrome (Maslach et al., 2001). Nonetheless, and despite the substantial numbers of amateur rugby players scoring above 3 (18.9% to 25%) on any given burnout symptom in the Cresswell and Eklund (2005b) investigation, only 6.4% (n = 25) of these athletes reported item-average scores above 3 on all three ABQ subscales. It is not possible to know from the descriptive statistics in Table 28.1 just how many athletes in other studies reported scores above the response scale midpoint on all facets of the burnout syndrome. Based on Raedeke s (1997) cluster analysis and the Table 28.2 frequency distribution of Cresswell and Eklund s data, however, it does seem reasonable to speculate that around 6% to 11% of those athletes may fall into Raedeke s categorization of high burnout. The cohorts of athletes involved in the studies listed in Table 28.1 are certainly not representative of all sport participants. There is some diversity in terms of a range of elite, subelite, and professional athletes from contact and noncontact sports, as well as team and individual sports, but there may well be important differences in the incidence of the syndrome across other subgroups of sport participants. Indeed, many of the Table 28.1 studies were supported by organizations concerns about burnout among athletes under their purview (e.g., rugby, swimming, New

In commenting on the release of this information, Chief Financial Of cer Chuck Criscillis stated, Like many other companies, we are looking for ways to better communicate our progress with our shareholders Reporting our hotels operating data on a monthly basis is one way to accomplish this By releasing these numbers, we are not intimating that similar RevPAR increases will be achieved for the balance of the year While we are anticipating RevPAR growth for the balance of 2001, we don t view the January growth rate as sustainable because of the weakness in January 2000 occupancy rates Nonetheless, our January 2001 statistics provide strong evidence that our strategy of focusing on occupancy more than on room rates can yield meaningful revenue growth.

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THE MIND 47 THE BRAIN 48 SENSORY PROCESSES 51 Color Vision 51 Pitch Detection 52 LEARNING AND MEMORY 53

The matters discussed in the foregoing paragraphs of this news release include forward-looking statements that involve risks and uncertainties that could cause results to differ from anticipated results, including, but not limited to, general economic conditions, weather patterns, individuals plans for business and personal travel, and other risks indicated in the Company s lings with the Securities and Exchange Commission16 This article addresses the importance of using RevPAR to present a fuller nancial picture that is based on factors that impact room sales, such as economic conditions, weather patterns, and business and personal travel..

Figure 5.4 contains normal probability plots (requested as NPPLOT) for Var_A and Var_C produced by SPSS EXPLORE. As shown in Figure 5.4, the data points fall very close to the diagonal for Var_A but some distance from it for Var_C. The low values and the high values of Var_C have

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6 M Datig, T Schlurmann, Performance and Limitations of the Hilbert-Huang Transformation (HHT) with an Application to Irregular Water Waves, Ocean Engineering, 31: 1783 1834, October 2004 7 DF Group, Functional Description of the Data Fusion Process, Technical Report, Of ce of Naval Technology, 1992 8 S L Hahn, Hilbert Transforms in Signal Processing Artech House, 1996 9 D L Hall, J Llinas, An introduction to Multisensor Data Fusion Proc IEEE, 1997, 85 (1): 6 23 10 N E Huang, Z Shen, S R Long, M L Wu, H H Shih, Z Quanan, N C Yen, C C Tung, H H Liu, The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and non-stationary Time Series Analysis, Proc Royal Society A, 1998, 454: 903 995 11 H H Jasper Report of the Committee on Methods of Clinical Examination in electroencephalography, Electroencephalography and Clinical Neurophysiology, (10): 370 375, 1958 12.

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