The scatter plot shows a negative trend, where the values of $y$ decrease as $x$ increases. The points are tightly clustered along a linear trend, indicating a strong negative correlation.

The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables, ranging from -1 (perfect negative correlation) to 1 (perfect positive correlation). Based on the plot:

• The correlation is negative because $y$ decreases as $x$ increases.
• The strong clustering around the line suggests a correlation close to -1.

Thus, the best estimate of the correlation coefficient is A. -0.9.

Would you like further explanation or analysis on this?

Here are five follow-up questions for deeper understanding:

1. How is the correlation coefficient calculated mathematically?
2. What does a perfect correlation coefficient of -1 represent?
3. Can a correlation coefficient determine causation between two variables?
4. What factors could reduce the strength of a correlation in a dataset?
5. How would this graph differ if the correlation coefficient were close to 0?

Tip: Always analyze the scatter plot's trend direction and point clustering to estimate the correlation coefficient effectively.