The amount of time, in minutes, that a person must wait for a bus is uniformly distributed between 0 and 23 minutes. What is the probability that a person waits more than 8 minute 0.8261 0.1304

College Algebra (MindTap Course List)
12th Edition
ISBN:9781305652231
Author:R. David Gustafson, Jeff Hughes
Publisher:R. David Gustafson, Jeff Hughes
Chapter8: Sequences, Series, And Probability
Section8.7: Probability
Problem 59E: Describe how to determine the probability of an event.
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## Problem Statement

The below problem examines uniform distribution and probability calculations for wait times. This example is highly relevant for understanding the concepts of probability in uniformly distributed scenarios.

**Problem:**

The amount of time, in minutes, that a person must wait for a bus is uniformly distributed between 0 and 23 minutes. What is the probability that a person waits more than 8 minutes?

### Multiple Choice Answers:

- \( \mathbf{0.8261} \)
- \( \mathbf{0.1304} \)
- \( \mathbf{0.6522} \)
- \( \mathbf{0.3043} \)
- \( \mathbf{0.4783} \)

### Detailed Explanation:

Uniform distribution is used when each outcome within a given range is equally likely. In this example, the time a person waits for the bus ranges from 0 to 23 minutes, and each minute within this range is equally probable.

#### Steps to Solve:

1. **Calculate Total Range:**
   - The total time range is from 0 to 23 minutes:
     \[ \text{Total Range} = 23 - 0 = 23 \text{ minutes} \]

2. **Calculate the Desired Range:**
   - We need to find the probability that a person will wait more than 8 minutes. Therefore, the desired range is from 8 to 23 minutes:
     \[ \text{Desired Range} = 23 - 8 = 15 \text{ minutes} \]

3. **Calculate Probability:**
   - The probability of waiting more than 8 minutes is the ratio of the desired range to the total range:
     \[ P(\text{waiting more than 8 minutes}) = \frac{\text{Desired Range}}{\text{Total Range}} = \frac{15}{23} \approx 0.6522 \]

Hence, the probability that a person must wait more than 8 minutes for the bus is approximately \( 0.6522 \).

#### Answer:

\[ \boxed{0.6522} \]

Understanding uniform distributions and their applications in real-world scenarios helps solidify foundational probability concepts. This problem highlights how to transform probability theory into practical insights, particularly useful in operations research, transportation planning, and various service-oriented industries.
Transcribed Image Text:## Problem Statement The below problem examines uniform distribution and probability calculations for wait times. This example is highly relevant for understanding the concepts of probability in uniformly distributed scenarios. **Problem:** The amount of time, in minutes, that a person must wait for a bus is uniformly distributed between 0 and 23 minutes. What is the probability that a person waits more than 8 minutes? ### Multiple Choice Answers: - \( \mathbf{0.8261} \) - \( \mathbf{0.1304} \) - \( \mathbf{0.6522} \) - \( \mathbf{0.3043} \) - \( \mathbf{0.4783} \) ### Detailed Explanation: Uniform distribution is used when each outcome within a given range is equally likely. In this example, the time a person waits for the bus ranges from 0 to 23 minutes, and each minute within this range is equally probable. #### Steps to Solve: 1. **Calculate Total Range:** - The total time range is from 0 to 23 minutes: \[ \text{Total Range} = 23 - 0 = 23 \text{ minutes} \] 2. **Calculate the Desired Range:** - We need to find the probability that a person will wait more than 8 minutes. Therefore, the desired range is from 8 to 23 minutes: \[ \text{Desired Range} = 23 - 8 = 15 \text{ minutes} \] 3. **Calculate Probability:** - The probability of waiting more than 8 minutes is the ratio of the desired range to the total range: \[ P(\text{waiting more than 8 minutes}) = \frac{\text{Desired Range}}{\text{Total Range}} = \frac{15}{23} \approx 0.6522 \] Hence, the probability that a person must wait more than 8 minutes for the bus is approximately \( 0.6522 \). #### Answer: \[ \boxed{0.6522} \] Understanding uniform distributions and their applications in real-world scenarios helps solidify foundational probability concepts. This problem highlights how to transform probability theory into practical insights, particularly useful in operations research, transportation planning, and various service-oriented industries.
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