https://file.aiquickdraw.com/mathbot/file/17324741583041pcqn2n2.jpg

From the provided image, the task is to determine how many outliers are present in the data. Outliers are defined as data points that lie outside the ±2 standard deviation (±2σ) range from the line of best fit (indicated by the dashed lines).

To count the outliers:

1. Examine all the data points plotted on the graph.
2. Identify the points that fall outside the dashed boundary lines (±2σ range).
3. Count these points.

Based on the visual inspection of the graph:
- There seem to be **2 points that fall outside the dashed lines**, which means there are **2 outliers**.

If you have further questions or want details clarified, let me know!

### Follow-up questions:
1. What are standard deviations, and how are they used to identify outliers?
2. How is a line of best fit determined in a regression model?
3. Why are ±2σ boundaries significant in analyzing data?
4. What happens if there are too many outliers in a dataset?
5. Can outliers impact the slope of the line of best fit? Why or why not?

### Tip:
Always double-check the dataset's points visually when identifying outliers, as mistakes in interpretation can alter results.

The following plot shows a line of best fit accompanied by the ±2σ range. How many outliers are present in the data?

Analyze a regression plot with a line of best fit and determine the number of outliers outside the ±2 standard deviation range. This exercise involves key statistical concepts like outlier detection and the empirical rule, suitable for high school students.

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