The three quartiles for systolic blood pressure in a sample of

3177

men were

=*Q*1=98, *Q*2118,

and

*Q*3

=

137

.

In the context of your systolic blood pressure data for 3,177 men, the three quartiles, \( Q_1 = 98 \), \( Q_2 = 118 \), and \( Q_3 = 137 \), represent the following:

1. **\( Q_1 \) (First Quartile)**: This is the value below which 25% of the observations fall. So, 25% of the men in your sample have a systolic blood pressure less than or equal to 98 mmHg.

2. **\( Q_2 \) (Second Quartile or Median)**: This is the value below which 50% of the observations fall. Thus, 50% of the men have a systolic blood pressure less than or equal to 118 mmHg. The median is a measure of central tendency.

3. **\( Q_3 \) (Third Quartile)**: This is the value below which 75% of the observations fall. Therefore, 75% of the men have a systolic blood pressure less than or equal to 137 mmHg.

Would you like more details on how these quartiles are calculated or how to interpret them in different contexts? 

Here are 5 questions that might further your understanding:

1. What is the interquartile range (IQR) for this dataset?
2. How does one calculate the quartiles from raw data?
3. What does it mean if the systolic blood pressure data is skewed?
4. How would you identify outliers using quartiles?
5. What is the significance of knowing the quartiles in medical data?

**Tip:** Quartiles are crucial for understanding the spread of data, especially in large datasets, and help identify outliers effectively.

Explore the concept of quartiles in systolic blood pressure data with a focus on a sample of 3,177 men. Learn about quartiles (Q1, Q2, Q3), their interpretation, and their significance in statistical analysis of medical data.

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