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OPTIONAL INTERPRETIVE STEP: ANALYZE PLANNED CLINICAL COMPARISONS WHEN THE 13 REQUISITE WAIS-III SUBTESTS ARE ADMINISTERED

is the average number of defects per unit. A U chart may be constructed for such data. If the number of defects in a unit is a Poisson random variable with parameter , the mean and variance of this distribution are both . Each point on the chart is U, the average number of defects per unit from a sample of n units. Therefore, the mean of U is and the variance of U is n. UCL LCL 3 3 Bn Bn

You need to provide access to a system to make it usable. In a complex interconnected world, no system is an island.

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Based on Flanagan and Kaufman s (2004) knowledge of the abilities measured by the WISC-IV, CHC theory, and relevant research on the relations between specific cognitive abilities and learning/achievement, [they offered] a select number of additional comparisons that... may provide potentially meaningful hypotheses about a [person s] cognitive capabilities beyond the information generated from the Index Profile Anal-

If there are m preliminary samples, and the number of defects per unit in these samples are U1, U2, . . . , Um, the estimator of the average number of defects per unit is U 1 m m ia Ui 1 (16-26)

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ysis (p. 145). Flanagan and Kaufman developed eight theory-based clinical clusters, composed of two or three WISC-IV subtests. They then proposed six Planned Clinical Comparisons involving these clusters, which constituted optional Step 7 of their interpretive system. We have adapted those WISC-IV comparisons to the WAIS-III. In some instances, we were able to use the same cluster for the WAIS-III that Flanagan and Kaufman used for the WISC-IV. However, modifications of several clusters were necessary because the WAIS-III and WISC-IV do not contain the same set of subtests. We made these changes with consultation from Dawn P. Flanagan (personal communication, December 15, 2004) to ensure that the CHC abilities presumed to underlie all WAIS-III clusters correspond to the same CHC abilities that are presumed to underlie the WISC-IV clinical clusters. Table A.6 lists the clinical clusters, the subtests that comprise them, and a brief definition of each

I&A is a business procedure that can require very substantial time and effort to maintain and manage. All these costs add to the bottom-line costs of running the business and in general should be minimized. This requirement is often in conflict with accuracy requirements.

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Planned Clinical Comparison 1. Fluid Reasoning versus Visual Processing Fluid Reasoning (Gf) Cluster Matrix Reasoning + Picture Arrangement + Arithmetic

These control limits are based on the normal approximation to the Poisson distribution. When is small, the normal approximation may not always be adequate. In such cases, we may use control limits obtained directly from a table of Poisson probabilities. If u is small, the lower control limit obtained from the normal approximation may be a negative number. If this should occur, it is customary to consider zero as the lower control limit. EXAMPLE 16-5 Printed circuit boards are assembled by a combination of manual assembly and automation. A ow solder machine is used to make the mechanical and electrical connections of the leaded components to the board. The boards are run through the ow solder process almost continuously, and every hour ve boards are selected and inspected for process-control purposes. The number of defects in each sample of ve boards is noted. Results for 20 samples are shown in Table 16-5. The center line for the U chart is 1 20 u 20 ia i 1 u Bn 32 20

1.6 1.6 B 5

I&A assets, especially authenticators and related data, are vulnerable to theft or disclosure. The I&A service itself needs protection, including confidentiality and integrity of I&A data, availability of the I&A process, and accountability for I&A servicerelated actions. This requirement is supportive of accuracy requirements but is often in conflict with ease of use.

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