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The equation of the regression line based on the given data is:

\[
y = 0.79x + 12.78
\]

This means the slope of the line is approximately \(0.79\), and the y-intercept is approximately \(12.78\).

Would you like more details or further explanation on how the regression was calculated?

Here are 5 related questions for further understanding:
1. How is the slope of a regression line interpreted in practical terms?
2. What does the y-intercept represent in this context?
3. What is the meaning of the \( R^2 \) value in regression analysis?
4. How would the regression line change if more data points were added?
5. How can you predict new values using this regression equation?

**Tip:** When interpreting the slope, think of it as how much the dependent variable (y) changes for each one-unit increase in the independent variable (x).

Based on the data shown below, calculate the regression line (each value to at least two decimal places): x: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], y: [12.84, 16.16, 14.58, 17, 16.72, 20.64, 19.36, 22.18, 19.5, 22.22, 20.94, 23.56, 22.58]

Learn how to calculate the linear regression line using the least squares method for the given dataset with x-values ranging from 2 to 14 and corresponding y-values. This problem covers key statistical and algebraic concepts, including calculating slope and y-intercept.

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