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The Pearson correlation coefficient for both datasets are:

- For data set A, \( r = 0.664 \)
- For data set B, \( r = 0.901 \)

These values show the strength and direction of the linear relationship between \( x \) and \( y \) for both datasets.

Would you like further details or have any questions about the calculation? Here are some related questions to expand on this:

1. How is the Pearson correlation coefficient interpreted in terms of strength of the relationship?
2. What does a positive correlation indicate about the relationship between two variables?
3. How does the size of data sets affect the Pearson correlation calculation?
4. What are the limitations of using Pearson's correlation?
5. Could a non-linear relationship still have a low or high Pearson correlation?

**Tip:** Pearson correlation assumes a linear relationship between the variables. For non-linear relationships, other methods like Spearman's correlation might be more appropriate.

Compute the correlation for both sets of data. Give your answers to three decimal places for Data set A and Data set B.

Learn how to compute the Pearson correlation coefficient for two sets of data, A and B. Follow the step-by-step solution to understand the strength of the linear relationship between x and y for each set. Suitable for Grades 10-12.

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