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The question asks whether "Outliers are observed data points that are close to (or on) the least squares line."

The statement is **False**.

Outliers are data points that are far from the trend of the majority of the data, and they do not lie close to the least squares regression line. Instead, they are points that deviate significantly from the pattern established by the other points.

Would you like more details or have any questions?

Here are five related questions you might find useful:
1. What exactly is a least squares regression line?
2. How do outliers affect the accuracy of a regression model?
3. Can outliers be removed, and if so, under what conditions?
4. What methods are available for detecting outliers in a dataset?
5. How does the presence of outliers influence correlation and causation?

**Tip:** Always examine the residuals (differences between observed and predicted values) to identify outliers effectively in regression analysis.

Outliers are observed data points that are close to (or on) the least squares line.

This problem explores whether outliers are close to the least squares line. Learn how outliers deviate from the regression pattern, affecting statistical models. Ideal for students in Grade 10-12.

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