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91Ó°ÊÓ

Four members from a 20-person committee are to be selected randomly to serve as chairperson, vice-chairperson, secretary, and treasurer. The first person selected is the chairperson; the second, the vice-chairperson; the third, the secretary; and the fourth, the treasurer. How many different leadership structures are possible?

Short Answer

Expert verified
116,280 possible leadership structures.

Step by step solution

01

Understand the problem

We need to find out how many ways we can select and assign 4 specific roles (chairperson, vice-chairperson, secretary, and treasurer) from a 20-person committee.
02

Determine the number of choices for each position

For the chairperson, there are 20 choices. Once the chairperson is selected, 19 remain for the vice-chairperson. After choosing the vice-chairperson, 18 are left for the secretary. Finally, 17 remain for the treasurer.
03

Use the multiplication rule of counting

Multiply the number of choices available for each position: 20 (chairperson) × 19 (vice-chairperson) × 18 (secretary) × 17 (treasurer).
04

Calculate the result

Perform the calculation: 20 × 19 × 18 × 17 = 116,280.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Multiplication Rule
In combinatorics, the multiplication rule helps determine the number of ways multiple tasks can be completed in sequence. The rule states that if there are \(n\) ways to perform one task and \(m\) ways to perform another, the total number of ways to perform both tasks is \(n \times m\) ways. For our problem, each role in the committee counts as a task.
For instance, the first task is choosing the chairperson. There are 20 options. The next task is picking the vice-chairperson from the remaining 19 members. Then we choose a secretary from the 18 left. Finally, a treasurer is selected from the 17 remaining members.
Using the multiplication rule:
  • 20 choices for chairperson,
  • 19 for vice-chairperson,
  • 18 for secretary,
  • and 17 for treasurer.
Multiply these together: \(20 \times 19 \times 18 \times 17\) to get 116,280 possible ways to assign the roles.
Counting Principles
Counting principles offer methods to count the number of ways events can occur. The two main ones are the addition principle and the multiplication principle. The addition principle applies to mutually exclusive events — where only one can happen at a time.
For our exercise, the multiplication principle is more appropriate as multiple selections happen consecutively. This principle helps us manage sequential counting scenarios.
It tells us how to count scenarios where several stages or choices are involved. We used the multiplication principle since each choice affects the next. Each role is picked based on the previous person chosen.
Permutations
Permutations refer to the arrangement of items in a specific order. When order matters, as in our exercise, permutations are used. The formula for permutations of \(n\) objects taken \(r\) at a time is: \[ P(n, r) = \frac{n!}{(n - r)!} \] Here, \(!\) denotes factorial, the product of all positive integers up to that number.
In our problem, we select 4 committee members from a pool of 20 and arrange them in specific roles (order matters). Therefore, it’s a permutation problem. Using the permutation formula, where \(n = 20\) and \(r = 4\), we get:
  • \(P(20, 4) = \frac{20!}{16!} = 20 \times 19 \times 18 \times 17 = 116,280\)
This verifies our result using a different approach.
Combinatorial Analysis
Combinatorial analysis deals with counting, arrangement, and combination of objects. It often involves the concepts of permutations and combinations.
Permutations focus on ordered arrangements, as we saw. Combinations, on the other hand, consider selections where order doesn’t matter.
In our problem, we deal with permutations since selecting a chairperson is different from selecting a vice-chairperson. Combinatorial principles extend to more complex scenarios but rely on the basics used here.
Understanding these principles provides solid groundwork for more intricate problems. We apply basic counting and permutation rules for a thorough foundational approach.

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Most popular questions from this chapter

In Problems 13-18, find the probability of the indicated event if \(P(E)=0.25\) and \(P(F)=0.45\) \(P\left(F^{c}\right)\)

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According to a Gallup Poll, about \(17 \%\) of adult Americans bet on professional sports. Census data indicate that \(48.4 \%\) of the adult population in the United States is male. (a) Assuming that betting is independent of gender, compute the probability that an American adult selected at random is male and bets on professional sports. (b) Using the result in part (a), compute the probability that an American adult selected at random is male or bets on professional sports. (c) The Gallup poll data indicated that \(10.6 \%\) of adults in the United States are males and bet on professional sports. What does this indicate about the assumption in part (a)? (d) How will the information in part (c) affect the probability you computed in part (b)?

Fingerprints are now widely accepted as a form of identification. In fact, many computers today use fingerprint identification to link the owner to the computer. In \(1892,\) Sir Francis Galton explored the use of fingerprints to uniquely identify an individual. A fingerprint consists of ridgelines. Based on empirical evidence, Galton estimated the probability that a square consisting of six ridgelines that covered a fingerprint could be filled in accurately by an experienced fingerprint analyst as \(\frac{1}{2}\). (a) Assuming that a full fingerprint consists of 24 of these squares, what is the probability that all 24 squares could be filled in correctly, assuming that success or failure in filling in one square is independent of success or failure in filling in any other square within the region? (This value represents the probability that two individuals would share the same ridgeline features within the 24 -square region.) (b) Galton further estimated that the likelihood of determining the fingerprint type (e.g., arch, left loop, whorl, etc.) as \(\left(\frac{1}{2}\right)^{4}\) and the likelihood of the occurrence of the correct number of ridges entering and exiting each of the 24 regions as \(\left(\frac{1}{2}\right)^{8}\). Assuming that all three probabilities are independent, compute Galton's estimate of the probability that a particular fingerprint configuration would occur in nature (that is, the probability that a fingerprint match occurs by chance).

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