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In other applications, the actual value of residuals may be modi ed in a number of ways One common method is to report residuals reexpressed as normal-deviates in the distribution of residuals This approach, often used in structural equation analysis, can help identify the locations of the extremes, but hides the scale values of the error In the highly developed area of regression diagnostics, residuals may be adjusted for the size of the leverage associated with the value of the criterion variable (studentized residuals) or calculated using a model that obtained predicted values without the presence of the observation in the model (externally studentized) This prevents extreme values from distorting the model to the point that an aberrant value leads to a small residual, as displayed in panel C of Figure 22.

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PREVENTION AND TREATMENT OF TOBACCO DEPENDENCE Although smoking rates among adolescents rose during most of the 1990s (Johnston, O Malley, & Bachman, 1999), results of the 2000 Monitoring the Future Study suggest that there has been a modest decline, particularly among twelfth graders, who evidenced a 32 percentage-point drop in 2000 (Lantz et al, 2000) Despite this decline, smoking persists as a signi cant public health problem, particularly among school absentees, dropouts, and other high-risk youth for whom smoking rates are appreciably higher (de Moor et al, 1994; Glynn, Anderson, & Schwarz, 1991; Grunbaum & Basen-Engquist, 1993; Karle et al, 1994) Because 89% of adult current smokers began smoking before their nineteenth birthday, and a substantial proportion of adolescents experiment with smoking before entering high school (USDHHS, 1994), tobacco prevention efforts generally have been concentrated on elementary and middle school years.

As illustrated previously, complementary to the notion of patterns of residuals and meaning in individual residuals is the emphasis on mathematical models of effects that provide rich and parsimonious description This view is very much in line with the recently emerging view of the importance of effect sizes suggested by Glass (1976) and renewed by Cohen (1994) and the APA Task Force on Statistical Inference (Wilkinson, 1999) EDA reminds us that at the same time we focus on effects as a description of the data, we must also focus on the size and pattern of mis ts between effects and the data..

However, if early smoking prevention lessons are not reinforced and repeated in high school, smoking rates return to levels comparable to those seen in the general high school population (Bell, Ellickson, & Harrison, 1993; Ellickson, Bell, & McGuigan, 1993; Flay, 1985) The United States has evidenced an alarming rise in smoking prevalence among college students Although this trend is thought to re ect the increase in smoking initiation from the early 1990s (Wechsler, Rigotti, Gledhill-Hoyt, & Lee, 1998), initiation among young adults may be playing a role (Lantz et al, 2000) As smoking in younger cohorts (youth, young adults) continues to rise, heightened prevention efforts are needed.

Working with properties is confusing at first because some properties actually return objects. Suppose that you want to determine the background color of a particular comment on Sheet1. If you look through the list of properties for a Comment object, you won t find anything that relates to color. Rather, you must do this: 1. Use the Comment object s Shape property to return the Shape object that s contained in the comment. 2. Use the Shape object s Fill property to return a FillFormat object. 3. Use the FillFormat object s ForeColor property to return a ColorFormat object. 4. Use the ColorFormat object s RGB property (or SchemeColor property) to set the color. Put another way, getting at the interior color for a Comment object involves accessing other objects contained in the Comment object. Here s a look at the object hierarchy that s involved.

ou re the man! You ve learned how to use Gmail to its fullest, and now you re writing scripts that use scraped APIs to control your mail. In the rest of the book, you take your skills to the next level. First, in 11, you look at organizing your mail using Gmail s labeling system. Then 12 deals with e-mail addresses and the import and export of addresses to the Gmail address book. Then, for a bit of a break, in 13 you look at the possibilities that might open up with the HTML-only version of Gmail. In the future, you might want to know about that so you can build your own API library. After that, it s back to practicalities, when you learn how to export mail in 14, use Gmail for all sorts of interesting activities ( 14), and then, in perhaps the culmination of the whole study of this fine web application, use Gmail as a mountable file system. Really. Peep 16 if you don t believe.

Promising prevention strategies include aggressive antitobacco media campaigns, increased cigarette prices, school and community interventions, and environmental changes, such as increased emphasis on reducing adult smoking (thereby reducing the number of smoking adult role models), expanded clean indoor air laws, and enforcement of laws against sales of tobacco to minors (Lantz et al, 2000)..