# Interpreting Results: Using Regression Model for Prediction

Suppose I have a model like the following:

$$\hat{y} = 100 + 250 \log{x_{1}} + 75 x_{2} + 80 x_{3} + 105 \log{x_{4}}$$

If an observation in my validation set had $$x_{1} = 50, x_{2} = 40, x_{3} = 30,$$ and $$x_{4} = 20$$, how could I put those into my model to get a predicted value. The response, $$y$$, is in normal units, while two of the regressors, $$x_{1}$$ and $$x_{4}$$, had the logarithm applied to them.

It would be fine to just plug the $$x_n$$ values into their respective locations.
What does $$100 + 250\times \ln (50) + 75\times 40 + 80 \times 30 + 105 \times \ln(20)$$ calculate to be?