The linear regression equation, also known as the line of best fit, for the given data is:

\[
y = 14.143x + 820.667
\]

This equation predicts the value of \( y \) based on the value of \( x \).

Do you have any questions or need further details? Here are a few more questions that might interest you:

1. How would the linear regression equation change if the data had a different trend?
2. What are the assumptions behind using linear regression?
3. Can linear regression be used for non-linear data?
4. How would you calculate the residuals for each data point using this equation?
5. What are the limitations of using linear regression for this data?

**Tip:** Understanding how to interpret the slope and y-intercept in a regression equation can give you insights into the relationship between the variables!

Explore the linear regression equation y = 14.143x + 820.667, which predicts values based on a linear relationship. Learn about interpreting slope and y-intercept, calculating residuals, and limitations of linear regression. Suitable for Grades 10-12.

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