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An identification and authentication (I&A) service must satisfy a set of requirements for both the service and the quality of service. The function of I&A is to recognize an individual and validate the individual s identity. While each situation that calls for I&A is unique, there are common generic requirements that apply to all I&A situations. This pattern provides a common generic set of I&A requirements. The pattern

Explain the meaningful difference between V-IQ and P-IQ. Then skip to STEP 6. Examine VCI versus POI discrepancy in STEP 5.

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The identity in Equation 13-5 (which is developed in Section 13-4.4 on the CD) shows that the total variability in the data, measured by the total corrected sum of squares SST, can be partitioned into a sum of squares of differences between treatment means and the grand mean denoted SSTreatments and a sum of squares of differences of observations within a treatment from the treatment mean denoted SSE. Differences between observed treatment means and the grand mean measure the differences between treatments, while differences of observations within a treatment from the treatment mean can be due only to random error. We can gain considerable insight into how the analysis of variance works by examining the expected values of SSTreatments and SSE. This will lead us to an appropriate statistic for testing the hypothesis of no differences among treatment means (or all i 0).

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There is also a partition of the number of degrees of freedom that corresponds to the sum of squares identity in Equation 13-5. That is, there are an N observations; thus, SST has an 1 degrees of freedom. There are a levels of the factor, so SSTreatments has a 1 degrees of freedom. Finally, within any treatment there are n replicates providing n 1 degrees of freedom with which to estimate the experimental error. Since there are a treatments, we have a(n 1) degrees of freedom for error. Therefore, the degrees of freedom partition is an The ratio MS Treatments SS Treatments 1a 12 1 a 1 a1n 12

5

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A. Is there significant scatter in VCI subtests High Scaled Score of 3 VCI Subtests High Low Low Scaled Score Scaled Score of 3 VCI Subtests Difference Abnormal Scatter 5 or more B. Is there significant scatter in POI subtests High Scaled Score of 3 POI Subtests High Low Low Scaled Score Scaled Score of 3 POI Subtests Difference Abnormal Scatter 6 or more STEP 5 Decision Box If STEP 5 questions A and B are NO If answer to either A or B is YES Not Abnormal 0 5 Is there abnormal scatter YES NO Not Abnormal 0 4 Is there abnormal scatter YES NO

is called the mean square for treatments. Now if the null hypothesis H0: 1 2 a p 0 is true, MSTreatments is an unbiased estimator of 2 because g i 1 i 0. However, a if H1 is true, MSTreatments estimates 2 plus a positive term that incorporates variation due to the systematic difference in treatment means.

then VCI versus POI discrepancy is interpretable then the VCI versus POI discrepancy should probably not be interpreted

is an unbiased estimator of 2 regardless of whether or not H0 is true. We can also show that MSTreatments and MSE are independent. Consequently, we can show that if the null hypothesis H0 is true, the ratio SS Treatments 1a 12 SSE 3a1n 12 4

also helps you to apply the general requirements to your specific situation, and helps you to determine the relative importance of conflicting requirements.

(13-7)

Explain the meaningful difference between VCI and POI. Do not interpret VCI versus POI difference.*

has an F-distribution with a 1 and a(n 1) degrees of freedom. Furthermore, from the expected mean squares, we know that MSE is an unbiased estimator of 2. Also, under the null hypothesis, MSTreatments is an unbiased estimator of 2. However, if the null hypothesis is false, the expected value of MSTreatments is greater than 2. Therefore, under the alternative hypothesis, the expected value of the numerator of the test statistic (Equation 13-7) is greater than the expected value of the denominator. Consequently, we should reject H0 if the statistic is large. This implies an upper-tail, one-tail critical region. Therefore, we would reject H0 if f0 f , a 1, a1n 12 where f0 is the computed value of F0 from Equation 13-7. Ef cient computational formulas for the sums of squares may be obtained by expanding and simplifying the de nitions of SSTreatments and SST. This yields the following results. De nition The sums of squares computing formulas for the ANOVA with equal sample sizes in each treatment are

A. Is WMI factor interpretable Arithmetic Digit Span Letter-Number Sequencing Difference between High and Low Scaled Score Abnormal Scatter 6 or more (don t interpret) B. Is PSI factor interpretable Symbol Search Digit Symbol-Coding Difference between High and Low Scaled Score Abnormal Scatter 4 or more (don t interpret) Not Abnormal 0 3 (ok to interpret) Not Abnormal 0 5 (ok to interpret)

(13-8)

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