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The scatter plot in the image shows points that are fairly spread out with no strong linear pattern. The points do not seem to follow a clear upward or downward trend, suggesting a weak correlation between the variables.

Looking at the options:

1. \( r = 1 \): This indicates a perfect positive linear correlation, which does not fit this plot.
2. \( r = -0.89 \): This indicates a strong negative linear correlation, which is not visible in this plot.
3. \( r = -0.15 \): This indicates a weak negative correlation, which seems plausible given the scattered nature of the points.
4. \( r = 0.8 \): This suggests a strong positive linear correlation, which is not evident.
5. \( r = -0.98 \): This indicates a very strong negative correlation, which is not consistent with the scatter plot.

Given the spread of the points and lack of a strong trend, the best estimate for the correlation coefficient is likely **\( r = -0.15 \)**, representing a weak negative correlation.

Would you like more details on correlation or have any questions about this? 

Here are 5 related questions:
1. What does a correlation coefficient of 0 represent?
2. How is the correlation coefficient calculated?
3. What is the difference between positive and negative correlation?
4. How does correlation differ from causation?
5. What are the limitations of correlation analysis?

**Tip:** The closer the correlation coefficient is to 1 or -1, the stronger the linear relationship. A coefficient near 0 suggests little to no linear relationship.

Choose the best estimate for the correct correlation coefficient based on the scatter plot shown.

Learn how to estimate the correlation coefficient based on a scatter plot. This problem involves interpreting the scatter of points to identify the best correlation coefficient estimate. It covers key concepts in statistics, including the Pearson correlation coefficient.

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