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There are 33 tokens. Four are superglue. Six (including the superglue tokens) are glue tokens. The weak functional cohesion (WFC) is 6=33 18:2 percent. The strong functional cohesion (SFC) is 4=33 12:1 percent. The adhesiveness is (4 1 + 2 0:7 5 27 0:25 =33 12:25=33 37:1 percent. This program calculates three separate quantities. It is not surprising that it scores low on the cohesion metrics.

(a) The combined weight will lie between 496 and 500 oz if the mean weight of the 100 ball bearings lies between 4.96 and 5.00 oz (Fig. 5-4). 4.96 in standard units 5.00 in standard units Required probability (area between z (area between z (area between z 0.4868 0.2704 2.22 and z 2.22 and z 0.74 and z 0.2164 0) 0) 0.74) 4.96 5.02 0.027 5.00 5.02 0.027 2.22 0.74

For the ideal transformer shown in Fig. 14-51, nd I1 , given IL1 10:0 08 A Ans: 16:5 14:048 A IL2 10:0 36:878 A IL3 4:47 26:578 A

Fig. 5-4

Fig. 5-5

Fig. 14-51

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(b) The combined weight will exceed 510 oz if the mean weight of the 100 bearings exceeds 5.10 oz (Fig. 5-5). 5.10 in standard units Required probability (area to right of z (area to right of z (area between z 0.5 0.4985 2.96) 0) 0 and z 2.96) 0.0015 5.10 5.02 0.027 2.96

When the secondary of the linear transformer shown in Fig. 14-52 is open-circulated, the primary current is I1 4:0 89:698 A. Find the coe cient of coupling k. Ans: 0:983

Therefore, there are only 3 chances in 2000 of picking a sample of 100 ball bearings with a combined weight exceeding 510 oz.

Software testing is the execution of the software with actual test data. Sometimes it is called dynamic software testing to distinguish it from static analysis, which is sometimes called static testing. Static analysis involves analyzing the source code to identify problems. Although other techniques are very useful in validating software, actual execution of the software with real test data is essential.

Since X1, X2, . . . , Xn are random variables having the same distribution as the population, which has mean , we have E(Xk) Then since the sample mean is defined as # X we have as required # E(X) 1 n [E(X1) c E(Xn)] 1 n (nm) m X1 c n Xn m k 1, 2, c, n

Fig. 14-52 14.43 For the ideal transformer shown in Fig. 14-53, nd I1 , given I2 50 36:878 A and I3 16 08 A. Ans: 26:6 34:298 A

Sampling distribution of proportions 5 5.8. Find the probability that in 120 tosses of a fair coin (a) between 40% and 60% will be heads, (b) 8 or more will be heads.

Considering the autotransformer shown in Fig. 14-54 ideal, obtain the currents I1 , Icb , and Idc . Ans: 3:70 22:58 A; 2:12 86:718 A; 10:34 11:838 A

We consider the 120 tosses of the coin as a sample from the infinite population of all possible tosses of the coin. In this population the probability of heads is p 1, and the probability of tails is q 1 p 1. 2 2 (a) We require the probability that the number of heads in 120 tosses will be between 40% of 120, or 48, and 60% of 120, or 72. We proceed as in 4, using the normal approximation to the binomial distribution. Since the number of heads is a discrete variable, we ask for the probability that the number of heads lies between 47.5 and 72.5. (See Fig. 5-6.) m and expected number of heads s !npq B np 1 120a b 2 5.48 60

Fig. 5-6

CHAP. 14]

47.5 60 5.48 72.5 60 5.48

Fig. 14-53

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