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The image shows three questions related to calculating z-scores in a normal distribution. Let's solve each:

### 1. A data value located 1.3 standard deviations **below** the mean:
- The z-score is simply the number of standard deviations away from the mean, with values below the mean being negative.
- **Answer:** \( z = -1.3 \)

### 2. A data value located 2.5 standard deviations **above** the mean:
- Since the data value is above the mean, the z-score is positive.
- **Answer:** \( z = 2.5 \)

### 3. The mean in a normal distribution:
- The mean of a normal distribution corresponds to a z-score of 0 because it is the central value.
- **Answer:** \( z = 0 \)

Would you like a more detailed explanation of these z-score concepts? 

Here are 5 related questions for further exploration:
1. What is the general formula for calculating a z-score?
2. How does the shape of a normal distribution help with understanding z-scores?
3. What percentage of data lies within 1 standard deviation of the mean in a normal distribution?
4. How do z-scores help in comparing values from different distributions?
5. What is the z-score of a data point that is exactly 1 standard deviation below the mean?

**Tip:** In a normal distribution, approximately 68% of data lies within ±1 standard deviation from the mean.

In a normal distribution, a data value located 1.3 standard deviations below the mean, 2.5 standard deviations above the mean, and the mean has standard z-scores.

Learn how to calculate z-scores for values located 1.3 standard deviations below the mean and 2.5 standard deviations above the mean in a normal distribution. This problem also covers the z-score of the mean.

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