In the regression equation \( y = 0.0169x + 10.506 \), the number \( 10.506 \) represents the **y-intercept** of the line. This means that when the value of \( x \) is zero, the predicted value of \( y \) is \( 10.506 \).
In the context of a regression analysis, the y-intercept can also be interpreted as the expected value of the dependent variable \( y \) when the independent variable \( x \) does not contribute (i.e., is at its baseline