https://file.aiquickdraw.com/mathbot/file/17261551051328bxo09mv.jpg

The scatter plot shows data points that exhibit a negative correlation. The points are tightly clustered in a downward sloping pattern, indicating a strong negative linear relationship.

The correlation coefficient, denoted as **\(r\)**, ranges from -1 to 1:
- **\(r = -1\)** indicates a perfect negative correlation.
- **\(r = 0\)** indicates no correlation.
- **\(r = 1\)** indicates a perfect positive correlation.

Since the data points are closely clustered and the slope is negative, the correlation is close to **-1**, but not exactly, because the points aren't perfectly aligned. The best estimate for the correlation coefficient in this case is **A. -0.9**.

Would you like further details, or do you have any questions?

Here are 5 related questions:
1. How is the correlation coefficient calculated from raw data?
2. What are the key differences between positive and negative correlations?
3. How do outliers affect the value of the correlation coefficient?
4. What other methods can be used to describe the relationship between two variables?
5. Can correlation imply causation? Why or why not?

**Tip**: Always visualize data before calculating the correlation coefficient to get an intuitive sense of the relationship between variables.

What is the best estimate of the correlation coefficient for the data set?

Learn how to estimate the correlation coefficient from a scatter plot with a downward sloping pattern. The data set shows a strong negative correlation, with the best estimate being -0.9. Ideal for students in Grades 9-12.

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