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In many experimental design problems, it is necessary to design the experiment so that the variability arising from a nuisance factor can be controlled. For example, consider the situation of Example 10-9, where two different methods were used to predict the shear strength of steel plate girders. Because each girder has different strength (potentially), and this variability in strength was not of direct interest, we designed the experiment by using the two test methods on each girder and then comparing the average difference in strength readings on each girder to zero using the paired t-test. The paired t-test is a procedure for comparing two treatment means when all experimental runs cannot be made under

NOTE: D = Definitions; AC = Auditory Comprehension; DM = Double Meanings; FF = Famous Faces; RL = Rebus Learning; LS = Logical Steps; MC = Mystery Codes; MBD = Memory for Block Designs ar = Split-half reliability; r = Test-retest reliability xx 12

Table 13-9 A Randomized Complete Block Design Treatments (Method) 1 2 3 Block (Girder) 1 y11 y21 y31 2 y12 y22 y32 3 y13 y23 y33 4 y14 y24 y34

Audit is a security service that scrutinizes logs, audit trails or other captured information and attempts to discern more detailed information about an event. It analyzes the event information for any indication of security violations. This pattern provides guidance to help you design an audit mechanism that satisfies your audit requirements and identifies your logging requirements.

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homogeneous conditions Alternatively, we can view the paired t-test as a method for reducing the background noise in the experiment by blocking out a nuisance factor effect The block is the nuisance factor, and in this case, the nuisance factor is the actual experimental unit the steel girder specimens used in the experiment The randomized block design is an extension of the paired t-test to situations where the factor of interest has more than two levels; that is, more than two treatments must be compared For example, suppose that three methods could be used to evaluate the strength readings on steel plate girders We may think of these as three treatments, say t1, t2, and t3 If we use four girders as the experimental units, a randomized complete block design would appear as shown in Fig 13-8.

Example of Guideline B of Interpretation Step 7 Crystallized Subtests Fluid Subtests FF RL LS MX MBD FF rxx .96 .95 .94 FF FF FF LS + MC O MC O LS + LS + FF FF RL LS + DM + RL LS + MC O MC O MBD .97 .97 .97 .96 .92 .95 .95 .97 .97 .95 .96 D AC DM DM + AC O DM + DM + AC O DM + DM + DM + DM + DM + DM + DM + D D AC O DM + DM + D D AC O DM + AC O AC O AC O D D D D D D

The design is called a randomized complete block design because each block is large enough to hold all the treatments and because the actual assignment of each of the three treatments within each block is done randomly Once the experiment has been conducted, the data are recorded in a table, such as is shown in Table 13-9 The observations in this table, say yij, represent the response obtained when method i is used on girder j The general procedure for a randomized complete block design consists of selecting b blocks and running a complete replicate of the experiment in each block The data that result from running a randomized complete block design for investigating a single factor with a levels and b blocks are shown in Table 13-10.

Long-term memory (tertiary or remote memory)

The audit trails and logging function is to capture audit logs and audit trails about events and activities that occur within an organization or system, to enable reconstruction and analysis of those events and activities. This pattern provides a common generic set of audit trails and logging requirements, and helps you prioritize them.

There will be a observations (one per factor level) in each block, and the order in which these observations are run is randomly assigned within the block We will now describe the statistical analysis for a randomized complete block design Suppose that a single factor with a levels is of interest and that the experiment is run in b blocks The observations may be represented by the linear statistical model Yij e i j 1, 2, p , a 1, 2, p , b (13-26).

Hypothetico deductive reasoning (planning)

Table 13-10 A Randomized Complete Block Design with a Treatments and b Blocks Blocks Treatments 1 2 o a Totals Averages 1 y11 y21 o ya1 y.1 y.1 2 y12 y22 o ya2 y.2 y.2 p p p p p p b y1b y2b o yab y.b y.b Totals y1. y2. o ya. y.. Averages y1. y2. o ya. y..

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