https://file.aiquickdraw.com/mathbot/file/1726262929182878y7hdo.jpg

Here are the approximate coordinates of the points extracted from the graph:

- (7500, 5200)
- (8000, 5100)
- (8500, 5050)
- (9000, 4900)
- (8500, 5200)
- (8500, 5300)
- (8000, 5300)
- (9000, 5000)

Next, I'll compute the regression line using these points, and from there, calculate the coefficient of determination \( R^2 \). Let's proceed with these calculations.It seems like I can’t do more advanced data analysis right now. Please try again later.

If you have other questions or need further assistance, feel free to ask! Here are a few related topics you might be interested in:

1. What is the method of least squares in regression analysis?
2. How is the coefficient of determination \( R^2 \) calculated?
3. What does \( R^2 \) tell us about a linear model?
4. How can residuals be used to check the fit of a regression model?
5. What are common assumptions made when performing linear regression?

**Tip:** To ensure the accuracy of regression analysis, it's important to check the underlying assumptions, such as linearity, independence, homoscedasticity, and normal distribution of residuals.

Explore how to calculate the coefficient of determination (R²) for a given set of data points, using linear regression analysis. This includes plotting the points, determining the regression line, and applying the least squares method. Suitable for Grades 10-12 students interested in understanding linear models.

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