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Based on the scatter plot you provided, it appears to show a negative correlation between \( x \) and \( y \). As \( x \) increases, \( y \) tends to decrease. The problem is asking for the best prediction for \( x \) when \( y = 65 \).

From the plot:
1. When \( y = 65 \), I need to estimate the corresponding \( x \)-value from the trend of the scatter plot.

Looking at the graph, for \( y = 65 \), it seems that \( x \) is closer to **39** rather than 93, because 93 would correspond to much lower \( y \)-values, probably below 20. Therefore, the better prediction for \( x \) when \( y = 65 \) is **39**.

Would you like further clarification or more detailed steps on how to estimate values from scatter plots?

Here are 5 follow-up questions related to the graph and predictions:

1. How do we calculate the correlation coefficient to determine the strength of the relationship?
2. How can we fit a line of best fit to this scatter plot?
3. Why is the prediction for \( x = 39 \) better than \( x = 93 \)?
4. What does the slope of the trend in the scatter plot indicate about the relationship between \( x \) and \( y \)?
5. How could we use linear regression to make predictions from the scatter plot?

**Tip:** When analyzing scatter plots, drawing a line of best fit can help you make more accurate predictions by smoothing out individual data point fluctuations.

Based on the scatter plot below, which is a better prediction for x when y = 65?

Learn how to predict the value of x when y equals 65 using a scatter plot. This exercise focuses on analyzing data points, understanding negative correlation, and making predictions based on trends. Suitable for students in Grades 9-12.

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