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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?
