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trast, the effect sizes for Performance IQ are moderate, and similar in value, for the five oldest cohorts. Over the 17-year period, adults born before 1944 lost 10 points ( 1) of Performance IQ, or about 2/3 of a SD, whether they were about 60, 70, 80, or 90 years of age in 1995. For the oldest two cohorts, Full Scale IQ losses were larger than either separate loss reflecting the more generalized loss of function in both verbal and nonverbal domains that occurs during the decade of the 80s. IMPLICATIONS OF RESULTS. Kaufman (2001) presented WAIS-III education-adjusted crosssectional analyses, discussed previously, alongside these cohort-substitution analyses, and the similarity in results for these two alternate approaches to IQ and aging is remarkable. In both analyses, Verbal IQ emerged as a maintained ability for most of the life span (peaking at ages 45 54), with the only notably lower mean Verbal IQs occurring for adults in their 80s. Performance IQ was a vulnerable ability in both cross-sectional and longitudinal analyses, with peak performance occurring at ages 20 24. Figure 5.19 graphs the decline in P-IQ during the adult life span for mean IQs obtained cross-sectionally and longitudinally. As was true for the cross-sectional and

where SST g i 1 1 yi y2 2 is the total corrected sum of squares of y. In Section 11-2 we noted that SSE SST 1Sxy (see Equation 11-14), so since SST 1Sxy SSE, we note that the regression sum of squares in Equation 10-26 is SSR 1Sxy. The total sum of squares SST has n 1 degrees of freedom, and SSR and SSE have 1 and n 2 degrees of freedom, respectively. 2 2 2 2 , E1SSR 2 We may show that E3SSE 1n 22 4 and 1Sx x and that SSE 2 SSR are independent chi-square random variables with n 2 and 1 degrees of freedom, respectively. Thus, if the null hypothesis H0: 1 0 is true, the statistic

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Factor Analysis: Two-, three-, and four-factor solutions: Verbal Comprehension Bannatyne: Horn: CHC: Baltes: Osgood: Acquired Knowledge Crystallized Intelligence (Gc) Broad: Crystallized Intelligence (Gc) Narrow: General Information Pragmatics Auditory vocal channel of communication Representational level of organization Auditory reception Memory General ability Remote memory Memory (primarily) of semantic content Fund of information Long-term memory

More than ever, the trustworthiness of software systems is an important prerequisite for the success of current and future business in the digital world. We see an everincreasing trend of new security vulnerabilities at different levels. It is alarming that so many incidents can be traced back to well-known problems that have proven, wellknown solutions. Why does this happen And is there a way out We answer these questions by commenting on quotes from the security specialist Bruce Schneier:

(11-26)

follows the F1,n 2 distribution, and we would reject H0 if f0 f ,1,n 2. The quantities MSR SSR 1 and MSE SSE (n 2) are called mean squares. In general, a mean square is always computed by dividing a sum of squares by its number of degrees of freedom. The test procedure is usually arranged in an analysis of variance table, such as Table 11-3.

Rapaport: Dean: Guilford: Other skills:

Table 11-3 Analysis of Variance for Testing Signi cance of Regression Source of Variation Regression Error Total

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INFLUENCES AFFECTING INFORMATION SUBTEST SCORES Alertness to the environment Cultural opportunities Foreign language background Intellectual curiosity and striving Interests Learning disabilities ( ACID profile) Outside reading Richness of early environment School learning UNIQUE ABILITIES OR TRAITS MEASURED BY INFORMATION Range of general factual knowledge Empirical Analysis of Information g loadings: r = .79 (2nd best) Reliability: split-half = .91, test-retest = .94 Practice effect: Small for ages 16 89 (gain of 0.6 scaled-score point) Subtest specificity/error variance: 23% vs. 9% (adequate specificity) Primary Oblimin Factor Loading: Verbal Comprehension factor = .81 Most related to: Vocabulary (r = .77) Least related to: Digit Symbol Coding (r = .38) Aging Analysis of Information Mean scaled score across the adult age range (controlling for education at ages 20 89):

EXAMPLE 11-3

We will use the analysis of variance approach to test for signi cance of regression using the oxygen purity data model from Example 11-1. Recall that SST 173.38, 1 14.947, Sxy 10.17744, and n 20. The regression sum of squares is SSR and the error sum of squares is SSE SST SSR 173.38 152.13 21.25

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