https://file.aiquickdraw.com/mathbot/file/1727035894803w5tn4cds.jpg

### Scatter Diagram
The scatter diagram above displays the relationship between Age and Systolic Blood Pressure for 18 adult females. It shows a clear upward trend, indicating a positive correlation between age and systolic blood pressure.

### Statistical Technique
The statistical technique used here is the **Pearson Correlation Coefficient** (\( r \)). This technique measures the linear relationship between two continuous variables. The value of \( r \) ranges from -1 to 1, where:
- \( r = 1 \) indicates a perfect positive linear relationship,
- \( r = -1 \) indicates a perfect negative linear relationship,
- \( r = 0 \) indicates no linear relationship.

### Value of \( r \) and Interpretation
The calculated correlation coefficient \( r \) is approximately **0.965**. This indicates a very strong positive linear relationship between Age and Systolic Blood Pressure. In other words, as age increases, systolic blood pressure tends to increase as well.

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### Related Questions:
1. How do you interpret the correlation coefficient in terms of strength and direction?
2. What is the equation of the best-fit line for this data?
3. How can we test the significance of the correlation coefficient?
4. What are the assumptions of Pearson correlation analysis?
5. Can we use correlation to imply causation in this case?

**Tip:** Always visualize data with scatter plots before computing correlations to check for any non-linear patterns.

The following data are taken from a sample of 18 adult females. (i) Draw a scatter diagram to display the relationship between the two variables. (ii) Describe the statistical technique used to analyze such data and calculate the value. (iii) For the correlation coefficient, what is the value of r and its interpretation?

Learn how to draw a scatter diagram and calculate the Pearson Correlation Coefficient for age and systolic blood pressure data from 18 adult females. This problem demonstrates the use of correlation to interpret the strength and direction of the relationship between two variables.

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