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The image shows a z-score calculation exercise. The formula for calculating the z-score is given as:

\[
z_x = \frac{X - \mu}{\sigma}
\]

where:
- \( X \) is the data point,
- \( \mu \) is the mean of the data set, and
- \( \sigma \) is the standard deviation.

There are placeholders for three z-scores: \( z_1 \), \( z_5 \), and \( z_6.5 \). The values for \( X \), \( \mu \), and \( \sigma \) need to be plugged in for each calculation.

If you provide the missing data (values for \( X \), \( \mu \), and \( \sigma \)), I can help calculate the z-scores for you!

Would you like to provide those values or need more details?

Here are 5 follow-up questions:
1. What are the values for \( X \), \( \mu \), and \( \sigma \) in the given data set?
2. Do you want to review how the z-score formula works in more detail?
3. Is there a specific z-score problem you need additional clarification on?
4. Do you need an explanation on how to interpret z-scores in a real-world context?
5. Are you working on z-scores for a specific type of data (e.g., exam scores, heights, etc.)?

**Tip:** The z-score tells you how many standard deviations a data point is from the mean. A negative z-score indicates the value is below the mean, while a positive z-score indicates it is above the mean.

The user submitted a query about calculating z-scores using the formula (z_x = (X - μ) / σ) with three data points z1, z5, and z6.5.

Learn how to calculate z-scores using the formula z_x = (X - μ) / σ for the given data points. This example walks through z1, z5, and z6.5, applying statistical concepts of mean and standard deviation.

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