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When a reference is added to a COM component, Visual Studio .NET creates a proxy class called a Runtime Callable Wrapper (RCW), which is used to allow Visual Basic .NET code to communicate with the COM component. The Runtime Callable Wrapper is viewable in the Object Browser, and the .dll file can be seen by clicking the Show All Files button in the Server Explorer and then opening the bin folder, as shown in Figure 17.10. After the reference is set and the proxy class created, the COM component can be created and used like a .NET component. The revised code follows:

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Note that the MSE from the full model, using both x1 and x2, is used in the denominator of the test statistic. Since f0.05,1,22 4.30, we reject H0: 2 0 and conclude that the regressor die height (x2) contributes significantly to the model. Table 12-4 shows the Minitab regression output for the wire bond pull strength data. Just below the analysis of variance summary in this table the quantity labeled Seq SS shows the sum of squares obtained by fitting x1 alone (5885.9) and the sum of squares obtained by fitting x2 after x1. Notationally, these are referred to above as SSR 1 1 0 0 2 and SSR 1 2 0 1, 0 2 . Since the partial F-test in the above example involves a single variable, it is equivalent to the t-test. To see this, recall from Example 12-5 that the t-test on H0: 2 0 resulted in the test statistic t0 4.4767. Furthermore, the square of a t-random variable with degrees of freedom is an F-random variable with one and degrees of freedom, and we note that t 2 0 (4.4767)2 20.04 f0.

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These results agree exactly with those obtained previously. Analogous to the sample variance s2 , the variability in the population is de ned by the population variance ( 2). As in earlier chapters, the positive square root of 2 , or , will denote the population standard deviation. When the population is nite and consists of N values, we may de ne the population variance as a 1xi N We observed previously that the sample mean could be used as an estimate of the population mean. Similarly, the sample variance is an estimate of the population variance. In 7, we will discuss estimation of parameters more formally. Note that the divisor for the sample variance is the sample size minus one 1n 12, while for the population variance it is the population size N. If we knew the true value of the population mean , we could nd the sample variance as the average squared deviation of the sample observations about . In practice, the value of is almost never known, and so the sum of

(6-5)

Figure 17.10 The object browser shows the Runtime Callable Wrapper, which is included in the bin folder.

the squared deviations about the sample average x must be used instead. However, the observations xi tend to be closer to their average, x, than to the population mean, . Therefore, to compensate for this we use n 1 as the divisor rather than n. If we used n as the divisor in the sample variance, we would obtain a measure of variability that is, on the average, consistently smaller than the true population variance 2 . Another way to think about this is to consider the sample variance s2 as being based on n 1 degrees of freedom. The term degrees of freedom results from the fact that the n deviations x1 x, x2 x, p , xn x always sum to zero, and so specifying the values of any n 1 of these quantities automatically determines the remaining one. This was illustrated in Table 6-1. Thus, only n 1 of the n deviations, xi x, are freely determined. In addition to the sample variance and sample standard deviation, the sample range, or the difference between the largest and smallest observations, is a useful measure of variability. The sample range is de ned as follows. De nition If the n observations in a sample are denoted by x1, x2, p , xn, the sample range is r max1xi 2 min1xi 2 (6-6)

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