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difference on the GATB scale (mean of 100, SD of 20), but it nonetheless provides additional evidence of the IQ construct s validity Figure 11, adapted from Matarazzo (1972, p 178) and Jensen (1980, p 113) and modified based on WAIS-R data reported by Reynolds et al (1987), presents graphically the educational or occupational referents of different IQ levels However, these values are just the averages for different jobs or educational accomplishments As Matarazzo (1972) and Jensen (1980) stress, adults in each occupation or educational category vary considerably in IQ range Table 45 presents pertinent data that reveal the fairly wide range of IQs for individuals from the same occupational category (as mentioned previously, this same table shows the wide IQ ranges for people with different levels of education).

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In s 3 and 4 we studied probability distributions for a single random variable. However, it is often useful to have more than one random variable de ned in a random experiment. For example, in the classi cation of transmitted and received signals, each signal can be classi ed as high, medium, or low quality. We might de ne the random variable X to be the number of highquality signals received and the random variable Y to be the number of low-quality signals received. In another example, the continuous random variable X can denote the length of one dimension of an injection-molded part, and the continuous random variable Y might denote the length of another dimension. We might be interested in probabilities that can be expressed in terms of both X and Y. For example, if the speci cations for X and Y are (2.95 to 3.05) and (7.60 to 7.80) millimeters, respectively, we might be interested in the probability that a part satis es both speci cations; that is, P(2.95 X 3.05 and 7.60 Y 7.80). In general, if X and Y are two random variables, the probability distribution that de nes their simultaneous behavior is called a joint probability distribution. In this chapter, we investigate some important properties of these joint distributions.

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The following potential liabilities may arise from applying this pattern:

For occupational groups, the range is relatively small for people employed in routine, menial jobs usually reserved for the mentally retarded, but substantial IQ ranges characterize members in jobs as diverse as physicians or policemen or even unskilled construction workers The strong relationship depicted here between IQ and occupation may be an artifact of the even stronger relationship described previously between IQ and educational attainment Occupation and education correlate substantially, particularly because advanced formal education is frequently a prerequisite for many high-prestige occupations Gottfredson and Brown (1981) observed an interesting age-related finding in the occupation education relationship in their largescale longitudinal study Occupational status correlated a modest 17 20 with years of schooling at ages 18 20 years, but increased at age 22 (45) and age 24 (60) before plateauing in the mid-60s for 26- and 28-year-olds.

5-1 5-1.1

For simplicity, we begin by considering random experiments in which only two random variables are studied. In later sections, we generalize the presentation to the joint probability distribution of more than two random variables.

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Gottfredson and Brown interpreted these age-related findings as a function of the facts that (1) the later entrants into the work force are brighter and better educated, and (2) among those already employed, the smarter and more educated adults advance from low-level to high-level positions Crawford and Allan (1997), studying a group of 200 adults ages 16 to 83 (M = 443 years) from.

Implementations of the pattern are not forced to follow the access matrix model. For example, Unix uses a pseudo-access matrix that is not appropriate for applying the need-to-know policy. However, constraints can be added to the pattern to force all the instances of the pattern to conform to an access matrix model. Typically, access permissions are implemented as Access Control Lists (ACLs).

EXAMPLE 5-1

Comparison of education levels characterizing the total standardization samples of the W-B I, WAIS, WAIS-R, and WAIS-III Education level (Years of School Completed) 16 or more (college graduate) 13 15 (some college) 12 years (high school graduate) 9 11 years (some high school) 8 or less Percent in W-B I Sample 5.1 3.8 10.8 18.8 61.6 Percent in WAIS Sample 4.9 8.6 23.3 27.8 35.5 Percent in WAIS-R Sample 11.4 13.4 34.7 25.1 15.5 Percent in WAIS-III Sample 17.8 22.9 34.4 12.1 12.8

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