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For this technique to work there has to be a clear division between the clean files and the dirty files; if both types occupy the same space you would not be able to distinguish between them. For example, if a clean file is in the range of 0 to 50 and a dirty file is in the range of 100 to 150, then they occupy different space and it is easy to detect them. If both clean and dirty files have values in the range of 0 to 50, then they occupy the same space and hidden data is hard to detect. By analyzing the graph in Figure 8.16 you can see that when you calculate the average there is a clear division between clean and dirty files. Any value above 175 indicates that a file has data embedded in it, and any value below that indicates a clean file. Looking at the skew graph in Figure 8.18 and the differential values graph in Figure 8.21 you will notice some overlap, but you cannot determine with 100 percent accuracy whether a given file has data embedded in it. With Figure 8.21 if a value is above 150, then you know the file is dirty or contains hidden information. If a file has a value below 50, then you know it is clean and it does not contain hidden information. With variation, kurtosis, and average deviation graphs, there is no distinct break point, so these are not good tests for universal stego detection.

This application is available on the companion CD-ROM. You ll find two versions on the CD: one for Excel 2000 (using OWC 9.0), and one for Excel 2002 (using OWC 10.0).

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training on older patients with major depression Archives of Internal Medicine, 159, 2349 2356 Broocks, A, Bandelow, D, Pekrun, G, George, A, Meyer, T, Bartmann, U, et al (1998) Comparison of aerobic exercise, clomipramine, and placebo in the treatment of panic disorder American Journal of Psychiatry, 155, 603 609 Burrows, H L, Nakajima, M, Lesh, J S, Goosens, K A, Samuelson, L C, Inui, A, et al (1998) Excess corticotropin-releasing hormone-binding protein in the hypothalamic-pituitary-adrenal axis in transgenic mice Journal of Clinical Investigations, 101, 1439 1447 Calfas, K J, & Taylor, W C (1994) Effects of physical activity on psychological variables in adolescents Pediatric Exercise Science, 6, 406 423 Camacho, T C, Roberts, R E, Lazarus, N B, Kaplan, G A, & Cohen, R D (1991).

orientation program However, not only would the treatments differ in the two conditions, but the individuals receiving the treatments would also differ Any observed difference between Bob s and Tom s drinking behavior might be the result of differences between the two individuals, rather than the result of the freshman orientation program The ideal but rather impractical way to obtain the ideal but unattainable comparison would be to travel back in time and arrange things so that Bob participated in the freshman orientation in one sequence, but not in another, comparison sequence The difference in the outcomes would exactly equal the effect of the freshman orientation, according to the counterfactualist de nition of the effect of a cause Any difference in an outcome variable at time 2 could be due only to the treatment differences that were introduced at time 1, because everything else would have been the same.

Physical activity and depression: Evidence from the Alameda County Study American Journal of Epidemiology, 134, 220 231 Carr, D, & Friedman, M A (2005) Is obesity stigmatizing Body weight, perceived discrimination and psychological well-being in the United States Journal of Health and Social Behavior, 46, 244 259 Castelli, D M (2005) Academic achievement and physical fitness in third-, fourth-, and fifth-grade students [Abstract] Research Quarterly for Exercise and Sport, 76(1, Suppl), A-15 Chaouloff, F (1997) The serotonin hypothesis In W P Morgan (Ed), Physical activity and mental health (pp 179 198) Washington, DC: Francis & Taylor Chrousos, G P, & Gold, P W (1992) The concepts of stress and stress system disorders: Overview or physical and behavioral homeostasis Journal of the American Medical Association, 267, 1244 1252 Churchill, J D, Galvez, R, Colcombe, S, Swain, R A, Kramer, A F.

Lacking the ability to travel back in time, however, we cannot have everything else be the same in real-life comparisons Thus, incorrect conclusions may be drawn about causal relations Scientists have developed a repertoire of methods that allow us to minimize the likely errors One important step in the evolution of these techniques has been the identi cation of several categories of factors, other than the program of interest, that can vary across the treatment conditions in real-life comparisons These are commonly called threats to internal validity (Campbell & Stanley, 1966), several of which are described later in the context of speci c research designs Another important step in the development of causal methods has been the invention of randomized experiments and various alternatives that, to varying degrees, allow us to approximate the ideal but unattainable counterfactual comparison.

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