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.