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The predicted value of y when x = 2 in this regression model is:

-2.67

-21

4.44

To determine the predicted value of \( y \) when \( x = 2 \) using the provided linear regression equation, we can substitute \( x = 2 \) into the regression equation:

\[
y = 5.22x - 13.11
\]

Substituting \( x = 2 \):

\[
y = 5.22(2) - 13.11
\]

\[
y = 10.44 - 13.11
\]

\[
y = -2.67
\]

So, the predicted value of \( y \) when \( x = 2 \) is **-2.67**.

Would you like more details on this calculation or have any other questions?

Here are 5 related questions you might find interesting:
1. How is the coefficient of determination (R-squared) interpreted in this context?
2. What does the T-value in the regression output signify?
3. How does the adjusted R-squared differ from the R-squared value?
4. What is the significance of the standard error of the coefficients?
5. How would the prediction change if the constant term was different?

**Tip:** The closer the R-squared value is to 100%, the better the model fits the data.

The predicted value of y when x = 2 in this regression model is:
-2.67
-21
4.44

Learn how to calculate the predicted value of y when x = 2 using the linear regression equation y = 5.22x - 13.11. This problem explains key concepts like coefficients and R-squared values, suitable for students in Grades 10-12.

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