Let's solve the problem step by step.

Given Data:

73, 18, 64, 22, 12, 48, 21, 35, 3, 60, 99

a. Find the mean:

The formula for the mean is: $\text{Mean} = \frac{\text{Sum of all values}}{\text{Number of values}}$

$\text{Sum of values} = 73 + 18 + 64 + 22 + 12 + 48 + 21 + 35 + 3 + 60 + 99 = 455$ $\text{Number of values} = 11$ $\text{Mean} = \frac{455}{11} = 41.4$

b. Find the median:

The median is the middle value when the data is ordered in ascending order.

Ordered data: $3, 12, 18, 21, 22, 35, 48, 60, 64, 73, 99$

The middle value is the 6th value (since there are 11 values): $\text{Median} = 35$

c. Find the mode:

The mode is the value that appears most frequently.

Checking the data: Each value occurs only once. Hence, there is no mode.

d. Find the midrange:

The midrange is the average of the smallest and largest values: $\text{Midrange} = \frac{\text{Smallest value} + \text{Largest value}}{2}$

Smallest value = 3, Largest value = 99 $\text{Midrange} = \frac{3 + 99}{2} = 51.0$

e. What do the results tell us?

The jersey numbers are just identifiers and do not represent any statistical variable like height or weight. The measures (mean, median, mode, and midrange) are meaningless in this context because jersey numbers do not carry quantitative or categorical significance.

Final Answers:

a. Mean: 41.4
b. Median: 35
c. Mode: No mode
d. Midrange: 51.0
e. Interpretation: The results are not meaningful as jersey numbers are identifiers, not data with inherent value.

Would you like further clarification or a breakdown of any specific part?
Here are related questions for further exploration:

1. How would these calculations change if one number were repeated multiple times?
2. What is the importance of identifying the type of data before performing statistical analysis?
3. How does the interpretation of results differ between quantitative and categorical data?
4. What other measures of central tendency or spread could be calculated for numerical data?
5. Can jersey numbers be used for any meaningful statistical analysis? Why or why not?

Tip: Always verify the nature of the data (e.g., categorical, ordinal, quantitative) before applying statistical measures.