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assumes that the variability in the response should be constant regardless of the town or city. Therefore, the assumption of constant variance is violated for this regression. As mentioned earlier, we can often alleviate violations of the regression assumptions by applying a transformation to the response variable. We therefore apply the natural log transformation to the response variable, percentage over 64, giving us the transformed response variable ln pct. The regression of ln pct on ln popn is then performed, and the plot of the standardized residuals versus ts is obtained (Figure 2.23). Note the set of outliers in the lower right, which have an extremely low proportion of senior citizens (indicated by their strong negative standardized residuals) given their population. These outliers are as follows: r Camp Pendleton Marine Corps Base, South r Camp Pendleton Marine Corps Base, North r Vandenberg Air Force Base r Edwards Air Force Base r Beale Air Force Base r El Toro Marine Corps Station r George Air Force Base r Mather Air Force Base r Nebo Center All but one of these outliers represent military installations, the exception being Nebo Center, a desert town with high unemployment and low housing values 112 miles east of Los Angeles. It is not surprising that the proportion of seniors citizens living in these places is very low. The analyst may therefore decide to set aside this group of observations and proceed with analysis of the remaining 848 records. We continue with the analysis of the California data set in the exercises.

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1. D. S. Alexander, W. A. Arbaugh, A. D. Keromytis, and J. M. Smith, Safety and security of programmable networks infrastructures, IEEE Communications Magazine, 36, 10, 84 92. 2. A. Aziz and W. Diffie, Privacy and authentication for wireless local area networks, IEEE Pers. Comm., 1, 1, 25 31, 1994. 3. V. Bharghavan, Secure Wireless LANs, in Proceedings ACM Conference on Computer and Communications Security, 1994, pp. 10 17. 4. A. Boukerche and M. S. M. A. Notare, Neural fraud detection in mobile phone operations, 4th IEEE BioSP3, Bio-Inspired Solutions to Parallel Processing, May 2000, pp. 636 644. 5. A. Boukerche, M. Sechi Moretti, and A. Notare, Applications of neural networks to mobile and wireless networks, In Biologically Inspired Solutions to Parallel and Distributed Computing, A. Zomaya (Ed.), New York: Wiley, 2001. 6. E. Brinksma. IS 8807 LOTOS Language of Temporal Ordering Specifications, 1988. 7. D. Brown, M. Abadi, and R. M. Needham, A logic of authentication, ACM Transactions on Computer Systems, 8, 1, 18 36, 1995. 8. H. Demuth and M. Beale, Neural network tollbox For use with MatLab, Matlab User s Guide, Version 3, pp. 7.1 7.33, 1998. 9. D. Denning, An intrusion-detection model, IEEE Transactions on Software Eng., 2(13), 222 232, 1987. 10. Y. Frankel, A. Herzberg, P. A. Karger, C. A. Kunzinger, and M. Yung, Security issues in a CDPD wireless network, IEEE Pers. Comm., 2, 4, 16 27, 1995. 11. H. Garavel, CADP/Eucalyptus Manual, INRIA, Grenoble, France, 1996. 12. V. Gupta and G. Montenegro, Secure and mobile networking, ACM/Baltzer MONET, 3, 381 390, 1999. 13. N. Habra et al., Asax: Software architecture and rule-based language for universal audit trail analysis, in Proceedings 2nd European Symposium on Research in Computer Security, LNCS, vol. 648, 1992.

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executes while the condition is true. In a Do Until loop, the loop executes until the condition is true.

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he previous chapter looked at the Document Object Model. A pertinent detail of the DOM is that we need a way to look at it and manipulate it within the context of a Web page. This is where we need JavaScript a Web-page-based scripting language.

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A working knowledge of the classi cations used in the hotel industry is important to understanding its organization. The various types of properties, their market orientation and location, sales indicators, occupancy and revenues as they relate to levels of service, and various types of business af liations are all means of classifying hotel properties. Figure 1-2 will serve as a reference point throughout this discussion.

Despite the reduction of microbial populations and pathogen prevalence by decontamination interventions during slaughtering, dressing, and chilling, surviving microorganisms or additional contamination may be present on carcasses, primal and sub-primal cuts, and trimmings (Gill and Bryant 1992; Bacon et al. 2002b). Thus, post-chilling decontamination treatments of whole carcass sides and cuts may be useful in further reducing microbiological contamination of meat and preventing pathogen proliferation during storage of packaged meat. For example, postchilling spraying of beef carcass samples with 4% lactic acid (55 C, 30 s) reduced inoculated Salmonella and E. coli O157:H7 by 1.9 2.4 log10 CFU/cm2 and decreased their populations during storage (4 C) of ground meat prepared from treated samples (Castillo et al. 2001b). Beef processors in the United States apply decontamination interventions of lactic acid and Inspexx 200 or Sanova on carcass sides as they exit the cooler and enter into fabrication. Multiple sequential treatments, including hot water and air, as well as chemical solu-

for consumers who never seem to be satis ed. Positive lists are used to identify merchants best customers who have successfully closed business with them in the past and are trying to make a new purchase.

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Here, I used the expression Val(Application.Version) > 5 to determine whether Excel is 16-bit or 32-bit. If the expression returns True, the code calls the 32-bit function; otherwise, it calls the 16-bit function.