The image contains a table with pulse data for males at a blood drive. Let's break down the problem and calculate the required values.

Step 1: Listing the Pulse Data

Here are the pulse measurements from the table:

• 64, 60, 68, 84
• 58, 64, 60, 64
• 58, 64, 72, 84
• 58, 60, 64, 88
• 60, 64, 72, 68
• 64, 68, 72, 80
• 72, 64, 79

Now we can proceed to find the requested values:

Step 2: Calculate the Mean

The mean is the average of the data. We need to sum all the values and divide by the total number of data points.

Step 3: Find the Lower and Upper Quartiles

• Lower quartile (Q1): This is the median of the lower half of the data.
• Upper quartile (Q3): This is the median of the upper half of the data.

Step 4: Find the Interquartile Range (IQR)

The interquartile range is the difference between Q3 and Q1: $\text{IQR} = Q3 - Q1$

Step 5: Find the Outliers

Any data point that is either below $Q1 - 1.5 \times \text{IQR}$ or above $Q3 + 1.5 \times \text{IQR}$ is considered an outlier.

Let me calculate these values for you.Here are the calculated values based on the pulse dataHere are the calculated values based on the pulse

• Mean pulse: $67.89$
• Lower Quartile (Q1): $62.0$
• Upper Quartile (Q3): $72.0$
• Interquartile Range (IQR): $10.0$
• Outliers: $88$ (this is the only outlier)

Do you want more details or have any other questions?

Here are five related questions:

1. How would you interpret the IQR in terms of pulse variability?
2. What would happen to the mean if one extreme outlier were added?
3. Can you explain why only 88 is considered an outlier?
4. How would the median change if the outlier were removed?
5. What is the significance of using IQR over the range when identifying outliers?

Tip: The interquartile range (IQR) is more resistant to outliers than the range, making it a better measure of variability for skewed distributions.