https://file.aiquickdraw.com/mathbot/file/17267991036095k9s6vp5.jpg

In the scatterplot shown, the task is to determine which point, if removed, would cause the **y-intercept** of the line of best fit to **increase the most**. 

Here's the reasoning:

- The **y-intercept** is where the line crosses the y-axis. Points below the line tend to pull the y-intercept down, while points above the line push it up.
- Points far away from the trend of the line (outliers) have the most influence on both the slope and the y-intercept.
- **Point D** is significantly **above** the line and far from the rest of the points. If it is removed, the line will shift down, causing the slope to decrease, and thus the y-intercept will increase.

Thus, **removing Point D** would cause the **y-intercept to increase the most**.

The correct answer is **D**.

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Would you like more details or have any questions? 

Here are some related questions to consider:

1. How is the y-intercept calculated in a line of best fit?
2. What is the effect of removing outliers on the slope of a line?
3. How do different points influence the y-intercept based on their location relative to the line?
4. What is the general impact of high-leverage points on regression lines?
5. How does the removal of influential points affect predictions in regression analysis?

**Tip:** Outliers that are far from the line of best fit can drastically change the slope and y-intercept when removed.

Which point, if removed, would cause the y-intercept to increase the most?

Understand how the removal of a point in a scatterplot affects the y-intercept in linear regression. This problem explains the influence of outliers and the importance of points in the overall slope and intercept. Ideal for students in Grades 9-12.

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