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Clinical Analysis of Letter-Number Sequencing Sequencing, poor short-term memory, inattention, distractibility, or anxiety may be causative factors for trouble on Letter-Number Sequencing. Similar to Digit Span, sequencing problems can be evident from correctly remembering the numbers and letters, but in the

In addition to point estimates of the slope and intercept, it is possible to obtain con dence interval estimates of these parameters. The width of these con dence intervals is a measure of

[ACM] [Ack03] [AD01]

the overall quality of the regression line. If the error terms, i, in the regression model are normally and independently distributed, 1 1 1 0

Neuropsychological Analysis of Letter-Number Sequencing Auditory discrimination may be evident if a subject consistently mistakes similar sounding letters such as T for D. Examining patterns of errors may help to distinguish memory or sequencing problems from attentional problems. For example, an examinee who consistently fails to recall the last item in a number letter series (e.g., responds G-8-L for the item G-8-L-2 ) is quite different than a person who recalls the letters and numbers in a jumbled order (for G-8-L-2 says L-8-G-2 ). Dropping a letter or number may be more indicative of an attentional problem, whereas mixing up the letters may be a sequencing or working memory problem.

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are both distributed as t random variables with n 2 degrees of freedom. This leads to the following de nition of 100(1 )% con dence intervals on the slope and intercept. De nition Under the assumption that the observations are normally and independently distributed, a 100(1 )% con dence interval on the slope 1 in simple linear regression is

Guilford: Other skills:

ACM: http://www.acm.org R. Ackermann: Gateways and Components for Supplementary IP Telephony Services in Heterogeneous Environments, PhD Thesis, July 2003 C. J. Alberts, A. J. Dorofee: SEI, OCTAVE Criteria, Version 2.0, CMU/SEI2001-TR-016, December 2001

Similarly, a 100(1

2 c

Cognition of figural units Evaluation of a figural system Simultaneous (holistic, right-brain) processing Distinguishing essential from nonessential details Visual closure Visual perception/processing of meaningful stimuli (people/things)

2 c

[ADE+99] [ADV] [AfB1999]

INFLUENCES AFFECTING PICTURE COMPLETION SUBTEST SCORES Ability to respond when uncertain Alertness to the environment Cognitive style (field dependence/field independence) Concentration Negativism ( Nothing s missing ) Working under time pressure UNIQUE ABILITIES OR TRAITS MEASURED BY PICTURE COMPLETION Flexibility of Closure (CHC theory) Visual alertness Visual recognition and identification (longterm visual memory) Empirical Analysis of Picture Completion g loadings: r = .64 (4th worst overall) Reliability: split-half = .83, test-retest = .79 Practice effect: Very Large for ages 16 54 (gain of 2.3 2.4 scaled-score points); Large for ages 55 74 (gain of 1.6 point); Moderate for ages 75 89 (gain of 0.9 point) Subtest specificity/Error variance: 35% vs. 17% (ample specificity) Primary Oblimin Factor Loading: Ages 16 74: Perceptual Organization factor = .55 Ages 75 89: Processing Speed factor = .62

(11-30)

Factor Analysis: Two-, three-, and four-factor solutions: Perceptual Organization Four-factor solution for ages 75 89: Processing Speed Bannatyne: Horn: CHC: Spatial Broad Visualization (Gv) Broad: Visual Processing (Gv) Crystallized Intelligence (Gc) Narrow: General Information Mechanics Visual motor (or vocal) channel of communication Automatic and representational levels of organization Visual reception Visual organization (without essential motor activity) Concentration Remote memory Visual memory

EXAMPLE 11-4

M. Arango, A. Dugan, I. Elliott, C. Huitema, S. Pickett: Media Gateway Control Protocol (MGCP), RFC 2705, Oct. 1999 Security Advisor: http://securityadvisor.info Association for Biometrics (AfB) and International Computer Security Association (ICSA): Glossary of Biometric Terms, 1999

We will nd a 95% con dence interval on the slope of the regression line using the data in Example 11-1. Recall that 1 14.947, Sxx 0.68088, and 2 1.18 (see Table 11-2). Then, from Equation 10-31 we nd

Baltes: Osgood:

t0.025,18

[AIS+77]

Rapaport:

1.18 A 0.68088

t0.025,18

Dean:

1.18 A 0.68088

C. Alexander, S. Ishikawa, M. Silverstein, M. Jacobson, I. Fiksdahl-King, S. Angel: A Pattern Language: Towns - Buildings - Construction, Oxford University Press, 1977

Secondary Oblimin Factor Loading: Ages 35 54: Processing Speed = .21 Ages 55 74: Verbal Comprehension factor = .23 Ages 75 89: Verbal Comprehension factor = .22 Most related to: Object Assembly (r = .52), and Block Design (r = .52) Least related to: Digit Span (r = .30) Aging Analysis of Picture Completion Mean scaled score across the adult age range (controlling for education at ages 20 89):

11-6.2

A con dence interval may be constructed on the mean response at a speci ed value of x, say, x0. This is a con dence interval about E(Y x0) Y x0 and is often called a con dence interval about the regression line. Since E(Y x0) Y x0 0 1x0, we may obtain a point estimate of the mean of Y at x x0 ( Y x0) from the tted model as

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