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A load Z2 connected to the secondary port of a transformer, as shown in Fig. 14-16, contributes to its input impedance. This contribution is called re ected impedance. Using the terminal characteristics of the coupled coils and applying KVL around the secondary loop, we nd V1 L1 sI1 MsI2 0 MsI1 L2 sI2 Z2 I2 By eliminating I2 , we get

4.113. (a) 16.0 (b) 6.35 (c) assuming equal areas in the two tails, x2 1 4.114. (a) 122.5 (b) 179.2 4.115. (a) 207.7 (b) 295.2

4.116. (a) 2.60 (b) 1.75 (c) 1.34 (d) 2.95 (e) 2.13 4.117. (a) 3.75 (b) 2.68 (c) 2.48 (d) 2.39 (e) 2.33 4.118. (a) 1.71 (b) 2.09 (c) 4.03 (d) 4.119. (a) 1.81 (b) 2.76 (c) 0.128 1.37

Fig. 14-16

0.879 (d)

CHAP. 14]

4.120. (a) 2.62 (b) 1.73 (c) 2.40 (d) 0.352 (e) 0.340 (f) 0.166

Often in practice we are interested in drawing valid conclusions about a large group of individuals or objects. Instead of examining the entire group, called the population, which may be difficult or impossible to do, we may examine only a small part of this population, which is called a sample. We do this with the aim of inferring certain facts about the population from results found in the sample, a process known as statistical inference. The process of obtaining samples is called sampling.

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Bieman and Ott also de ned some cohesion metrics using output slices. The de nitions are based on glue tokens, which are tokens (code sections) that are in more than one slice, and superglue tokens, which are in all slices. Adhesiveness of a token is the percentage of output slices in a procedure that contains the token. There are three functional cohesion measures: Weak functional cohesion (WFC) The ratio of glue tokens to total tokens Strong functional cohesion (SFC) The ratio of superglue tokens to total tokens Adhesiveness (A) The average adhesiveness of all tokens

EXAMPLE 5.1 We may wish to draw conclusions about the heights (or weights) of 12,000 adult students (the population) by examining only 100 students (a sample) selected from this population. EXAMPLE 5.2 We may wish to draw conclusions about the percentage of defective bolts produced in a factory during a given 6-day week by examining 20 bolts each day produced at various times during the day. In this case all bolts produced during the week comprise the population, while the 120 selected bolts constitute a sample. EXAMPLE 5.3 We may wish to draw conclusions about the fairness of a particular coin by tossing it repeatedly. The population consists of all possible tosses of the coin. A sample could be obtained by examining, say, the first 60 tosses of the coin and noting the percentages of heads and tails. EXAMPLE 5.4 We may wish to draw conclusions about the colors of 200 marbles (the population) in an urn by selecting a sample of 20 marbles from the urn, where each marble selected is returned after its color is observed.

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Several things should be noted. First, the word population does not necessarily have the same meaning as in everyday language, such as the population of Shreveport is 180,000. Second, the word population is often used to denote the observations or measurements rather than the individuals or objects. In Example 5.1 we can speak of the population of 12,000 heights (or weights) while in Example 5.4 we can speak of the population of all 200 colors in the urn (some of which may be the same). Third, the population can be finite or infinite, the number being called the population size, usually denoted by N. Similarly the number in the sample is called the sample size, denoted by n, and is generally finite. In Example 5.1, N 12,000, n 100, while in Example 5.3, N is infinite, n 60.

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