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The provided image contains a regression output and asks to write the linear regression equation. From the table, we can extract the relevant coefficients for the equation.

- **Constant (Intercept)**: \(-171.71\)
- **X Coefficient (Slope)**: \(4.71\)

The general form of a linear regression equation is:

\[
\hat{y} = \text{{Intercept}} + (\text{{Slope}} \times x)
\]

Substituting the values:

\[
\hat{y} = -171.71 + 4.71x
\]

This is the linear regression equation based on the output.

Would you like any more details or have any questions? Here are 5 related questions:

1. What does the \(R^2\) value (86.1%) tell us about this regression model?
2. How can we interpret the coefficient of \(x\) (4.71) in the context of the regression?
3. What does the "SE Coef" (Standard Error of Coefficients) indicate?
4. How significant is the constant term in this regression based on the T-statistic?
5. What does the adjusted \(R^2\) (83.8%) value represent in comparison to the regular \(R^2\)?

**Tip:** The \(T\)-statistic values help determine the significance of each predictor variable in the regression equation. A higher absolute \(T\)-value suggests greater significance.

Given the following computer regression output, write the linear regression equation.

Learn how to derive the linear regression equation from a given regression output. This solution includes details about the constant (intercept) and slope of the equation, utilizing statistical concepts to form the equation ŷ = -171.71 + 4.71x. Suitable for students in Grades 10-12.

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