To solve this problem, we need to determine the experimental probability that in a group of 4 students, at least one has green eyes. According to the problem:

1. Percentage of students with green eyes: 10%.
2. Representation: The digit "9" in the random numbers represents the 10% of students who have green eyes.
3. Simulation data: 20 four-digit numbers are given as samples.

Step 1: Identify Numbers with "9"

Since only the digit "9" represents a student with green eyes, we count each number in which at least one of the digits is "9". Let's go through the numbers one by one.

Numbers with "9" highlighted:

1. 7918 - contains "9"
2. 7910 - contains "9"
3. 2546 - does not contain "9"
4. 1390 - contains "9"
5. 6075 - does not contain "9"
6. 1230 - does not contain "9"
7. 2386 - does not contain "9"
8. 0793 - contains "9"
9. 7359 - contains "9"
10. 3048 - does not contain "9"
11. 2816 - does not contain "9"
12. 6147 - does not contain "9"
13. 5978 - contains "9"
14. 5621 - does not contain "9"
15. 9732 - contains "9"
16. 9436 - contains "9"
17. 3806 - does not contain "9"
18. 5971 - contains "9"
19. 6173 - does not contain "9"
20. 1430 - does not contain "9"

Step 2: Count Numbers with "9"

From the list, the numbers that contain at least one "9" are:

• 7918, 7910, 1390, 0793, 7359, 5978, 9732, 9436, and 5971.

There are 9 numbers out of the 20 that contain the digit "9".

Step 3: Calculate Experimental Probability

The experimental probability $P$ that in a group of 4 students at least one has green eyes is given by: $P = \frac{\text{Number of groups with at least one "9"}}{\text{Total number of groups}} \times 100\%$ $P = \frac{9}{20} \times 100\% = 45\%$

Answer

The experimental probability that at least one of the four students has green eyes is 45%.

Would you like further details on any part of this solution, or do you have any additional questions?

Here are 5 related questions that could further expand understanding:

1. How does changing the percentage of students with green eyes affect the experimental probability?
2. What would happen to the experimental probability if we used a larger sample size, like 100 numbers?
3. How can we use the binomial distribution to calculate the theoretical probability for this scenario?
4. What are some limitations of using experimental probability over theoretical probability in this context?
5. How could we adjust the simulation if 15% of students had green eyes instead of 10%?

Tip: When dealing with probabilities in simulations, remember to distinguish between "experimental" and "theoretical" probabilities—experimental probability relies on data from actual trials, while theoretical probability is based on expected outcomes.