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2.086. Minitab implements Fisher s LSD method by computing con dence intervals on all pairs of treatment means using Equation 13-12. The lower and upper 95% con dence limits are shown at the bottom of the table. Notice that the only pair of means for which the con dence interval includes zero is for 10 and 15. This implies that 10 and 15 are not signi cantly different, the same result found in Example 13-2. Table 13-5 also provides a family error rate, equal to 0.192 in this example. When all possible pairs of means are tested, the probability of at least one type I error can be much greater than for a single test. We can interpret the family error rate as follows. The probability is 1 0.192 0.808 that there are no type I errors in the six comparisons. The family error rate in Table 13-5 is based on the distribution of the range of the sample means. See Montgomery (2001) for details. Alternatively, Minitab permits you to specify a family error rate and will then calculate an individual error rate for each comparison.

This line of research joins with intervention studies, discussed in the next section, to provide broad-based support for intelligence s considerable malleability..

13-2.4 13-2.5

There are several types of scores that are yielded from the KAIT, including raw scores, scaled scores, and IQs. Raw scores are not norm-referenced, and are, therefore, not interpretable. Thus, it is necessary to transform the raw scores into some kind of standard scores (in this case either scaled scores or IQs). Scaled scores are the standard scores for each subtest; they have a mean of 10 and a standard deviation of 3. IQs are obtained by adding the scaled scores and transforming them into a standard score for each IQ scale (Fluid, Crystallized, Composite). The IQs have a mean of 100 and standard deviation of 15.

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Most I&A approaches involve identifying and authenticating an actor against a previously-established known record. Determining how the known record is established is the function of actor registration. This pattern helps you to design a registration mechanism for an actor or user. The type of information recorded depends on the I&A mechanism used. For example, if you are using an ID and password mechanism, then you need to define a user account ID and establish a password. If you are using signature verification, you need to capture user signature samples. This pattern covers the more common types of I&A mechanisms, such as those identified in this book.

The analysis of variance assumes that the observations are normally and independently distributed with the same variance for each treatment or factor level These assumptions should be checked by examining the residuals A residual is the difference between an observation yij and its estimated (or tted) value from the statistical model being studied, denoted as yij For the completely randomized design yij yi and each residual is eij yij yi, that is, the difference between an observation and the corresponding observed treatment mean The residuals for the paper tensile strength experiment are shown in Table 13-6 Using yi to calculate each residual essentially removes the effect of hardwood concentration from the data; consequently, the residuals contain information about unexplained variability The normality assumption can be checked by constructing a normal probability plot of the residuals.

To check the assumption of equal variances at each factor level, plot the residuals against the factor levels and compare the spread in the residuals It is also useful to plot the residuals against yi (sometimes called the tted value); the variability in the residuals should not depend in any way on the value of yi Most statistics software packages will construct these plots on request When a pattern appears in these plots, it usually suggests the need for a transformation, that is, analyzing the data in a different metric For example, if the variability in the residuals increases with yi, a transformation such as log y or 1y should be considered In some problems, the dependency of residual scatter on the observed mean yi is very important information.

Broad Cognitive Factor Comprehension Knowledge (Gc) WJ-III Subtest Verbal Comprehension General Information KAIT Subtest Definitions Auditory Comprehension Double Meanings Famous Faces Auditory Delayed Recall Auditory Delayed Recall Rebus Learning Delayed Recall Rebus Learning

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