Hello Everyone, There Are 128 Runners From 27 Countries

Post 1hello Everyonethere Are 128 Runners From 27 Countries Competing

There are 128 runners from 27 countries competing in a track meet. The betting pool needs to know how many outcomes there may be for the top 3 finishers. To determine the number of possible outcomes for the top 3 positions, we use permutations because the order matters — the first, second, and third places are distinct. The formula for permutations is P(n, r) = n! / (n - r)! where n is the total number of options, and r is the number of selections.

Applying this to our scenario, for the top 3 finishers among 128 runners, the calculation is: P(128, 3) = 128! / (128 - 3)! = 128 × 127 × 126. This product gives us the total number of different outcomes for the top 3 positions. Specifically, 128 × 127 × 126 equals 2,046,456. Therefore, there are 2,046,456 possible different outcomes for the top three finishers among the 128 runners.

Understanding Permutations in Context

Permutations are critical in scenarios where the order of selection matters, such as determining the rankings in a race. In this case, since each position (first, second, third) is distinct, permutations accurately model the problem. If the order did not matter, such as in selecting a group without regard to position, combinations would be appropriate.

Additional Considerations

While permutations provide the answer for the top three outcomes, calculating these factorials manually is impractical for very large numbers like 128!. Instead, calculators or software designed for combinatorial calculations are used. These tools can handle large factorials efficiently and prevent computational errors associated with manual calculations.

Post 2 Hello everyone, During a selection for a special duty, the judges had to pick 5 people from 15 contestants

In this scenario, the problem involves choosing a group of 5 contestants from a pool of 15 applicants. Since the order of selection does not matter—selecting {Alice, Bob, Charlie, David, Eva} is the same as any permutation of these five individuals—the problem involves combinations.

The formula for combinations is C(n, r) = n! / [r! × (n - r)!], where n is the total number of options, and r is the number of selections. Here, n = 15, and r = 5. Plugging in these values gives:

C(15, 5) = 15! / [5! × (15 - 5)!] = 15! / (5! × 10!).

Calculating factorials directly and simplifying, we find:

15! / (5! × 10!) = (15 × 14 × 13 × 12 × 11) / (5 × 4 × 3 × 2 × 1) = 360360 / 120 = 3003.

Hence, there are 3,003 different groups of 5 that can be formed from the 15 contestants. This result illustrates how combinations are used to determine the number of possible groups when the order of selection is irrelevant.

Applications of Combinations

Combinations are widely used in various fields, including statistics, probability, and logistical planning. Whether forming teams, selecting sample groups, or creating unique sets, understanding how to compute combinations helps in assessing the total number of possibilities in grouping scenarios.

Significance in Real-world Contexts

Accurate calculation of such combinations assists in understanding probabilities, optimizing selections, and planning resources effectively. For example, in selecting committee members, organizing tournaments, or designing experiments, methods for counting combinations are essential tools in decision-making processes.

References

  • Grinstead, C. M., & Snell, J. L. (1997). Introduction to Probability. American Mathematical Society.
  • Ross, S. M. (2014). A First Course in Probability (9th ed.). Pearson.
  • Lay, D. C. (2012). Introduction to Probability and Statistics. Pearson.
  • Wackerly, D., Mendenhall, W., & Scheaffer, R. L. (2008). Mathematical Statistics with Applications. Cengage Learning.
  • Triola, M. F. (2018). Elementary Statistics. Pearson.
  • Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. Pearson.
  • Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the Practice of Statistics. W.H. Freeman.
  • Devore, J. L. (2015). Probability and Statistics for Engineering and the Sciences. Cengage Learning.
  • Feller, W. (1968). An Introduction to Probability Theory and Its Applications. Wiley.
  • Lehmann, E. L., & Romano, J. P. (2005). Testing Statistical Hypotheses. Springer.