The z-score represents how many standard deviations a particular value is above or below the mean in a dataset. In this case, the z-score of 5.17 indicates that the player's height is 5.17 standard deviations above the mean.

To interpret:

• Z-score = $\frac{\text{X} - \mu}{\sigma}$
  • Where:
    • X is the individual value (the player's height).
    • $\mu$ is the mean (average) height of the data set.
    • $\sigma$ is the standard deviation of the data set.

Given that the z-score is 5.17, it means the player’s height is 5.17 standard deviations above the mean height in the data set.

Conclusion:

The player's height is 5.17 standard deviations above the mean.

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Would you like further details on z-scores or how to calculate them in general?

Here are some related questions you might find useful:

1. What does a negative z-score indicate about a data point?
2. How do you calculate the z-score from raw data?
3. What is the relationship between the mean, standard deviation, and z-score?
4. How do you interpret a z-score of 0?
5. How can z-scores be used to compare data from different distributions?

Tip: A z-score of 2 or higher is typically considered unusual in many data sets, indicating that the value is significantly different from the mean.