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low self-esteem, depression, anxiety, and disruptive behaviors. Learning disorders often evade detection by parents and teachers who may alternately attribute difficulties in school to laziness, low intellectual ability, oppositionality, or other social or emotional problems (Levine, 2003a, 2003b). Like ADHD, learning disorders have received a great deal of clinical, research, and media attention in recent years. Fairly recently, many colleges have begun to offer special admission consideration to learning disabled applicants. Untimed tests are also available at many schools and during national examinations such as the Scholastic Aptitude Test (SAT). Fortunately, learning disorders have received enough attention to alert many parents, teachers, school administrators, and others of this potential basis of children s difficulties in school. However, learning disorders have also become a popular diagnosis, which nonlearning disordered children and their parents seek in order to obtain the advantage of untimed SATs and school exams as well as special consideration for college admission. Like ADHD, learning disorders are not always easy to diagnose (Levine, 2003a, 2003b; O. Russell, 1997). Many different problems can be associated with poor school performance. Psychological and social problems, marital discord, poor motivation, low aptitude, poor nutrition, large class sizes, stress, illness, poor teaching, and a host of other factors may contribute to poor school performance by any given child. Additionally, many children with serious psychiatric or medical problems also appear to have learning disorders (real or supposed). Children with ADHD, Pervasive Developmental Disorders, Asperger s syndrome, depression, posttraumatic stress disorder, cultural or language differences, and other problems often have trouble with school performance. The diagnosis of learning disorders is usually made by child clinical psychologists in consultation with educational specialists.

Although the exact nature of denaturation and coagulation is not fully understood, there are distinct and easily recognized physical changes in meat proteins during cooking (Table 8.4). Solidi cation of the muscle and color changes are readily observed and are closely associated with the reduction in solubility. Heating meat up to 45 C internal temperature resulted in a slight decrease in the amount of water-soluble fraction extracted from meat (Laakkonen et al. 1970). Between the temperatures of 45 C and 58.5 C, the water-soluble fraction decreased rapidly, and only a slight decrease in watersoluble fraction is seen during holding at 60 C (Laakkonen et al. 1970). Solubility of the myo brillar fraction also decreases with increasing temperature (Hamm and Deatherage 1960; Lyon et al. 1986; Barbut et al 1996). The decrease in solubility is great-

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Typically, researchers will rate certain factors or measures based on archival or le data that is several years old, and then conduct what is called a retrospective follow-up for outcome criteria In this design, the predictor is coded from information that existed prior in time to the outcome However, the actual coding occurs later in time than the outcome does For instance, Douglas et al (1999) completed the HCR-20 (HistoricalClinical-Risk Management) risk assessment measure (Webster et al, 1997) and the Hare Psychopathy Checklist: Screening Version (PCL:SV; Hart, Cox, & Hare, 1995) based on le information of civil psychiatric patients who had applied for review panels for discharge in 1994 They used multiple sources of records to track the violence of patients until late 1996 Harris, Rice, and Quinsey (1993; Quinsey et al.

, 1998) used a similar procedure to construct a risk assessment measure called the Violence Risk Appraisal Guide (VRAG) Although the information upon which the measures were based existed prior to the outcome, the actual completed measures did not As such, this design allows for proper temporal ordering of the predictor and outcome, but is not truly prospective..

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RS code CRS (32, 28). If this is so, then in the systematic encoding procedure for the second (inner) encoder, it is very likely that the remainder, that is, the redundancy, will be a zero polynomial, because the vector to be encoded already belongs to this code. For this reason, the parity bytes generated by the outer code, the shortened RS code CRS (28, 24), are placed in the middle of the code vector before the inner encoding process, as shown in Figure 5.4 [9, 15]. Example 5.9: An arbitrary message vector m of 24 bytes will be encoded by the shortened RS code CRS (28, 24) [15]: m = 100

Detecting and Testing Nonlinear Relationships Hypothesis 5 494 Sets of Predictors: Increment in Prediction Hypothesis 6 497 Interaction Between Two Continuous Predictors Hypothesis 7 498 Interaction Between a Categorical and a Continuous Variable Hypothesis 8 500 MODEL CHECKING AND DETECTING REGRESSION PROBLEMS 503 Model Respeci cation 503 Shrinkage of R2 503 Assumptions of Regression Analysis and Detection of Violations 503 Multicollinearity 504 Errant Data Points and Regression Diagnostics Missing Data 506 SUMMARY 506 REFERENCES 506

Clearly, in either case, the probability that the call Broadcast(m) fails is at most 1/4(s + 1) f. Importantly, this probability is independent of m. Since the protocol Broadcast is called at most s + 1 times in Phase 2, the probability that none of these calls fails is at least 1 1/4f . On the other hand, recall that the probability that Broadcast is called at most s + 1 times exceeds 1 1/4f . Now a simple argument shows that the probability that Phase 2 involves at most s + 1 calls to Broadcast and that none of these calls fail exceeds 1 1/2f . Thus, we have proved the following result. Lemma 4.2 With probability exceeding 1 1/2f , when Phase 2 terminates u satisfies the double inequality {n/ln[4(s + 1) f ]} 2u 4(s + 1) fn.

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