I have a question about using $R^2$ as a "best fit" technique for cross-sectional (not time series) type data...
Suppose you have a data set, and you're trying to fit a regression model to it. You try several types of models (classic linear, exponential, log-log, etc), and ultimately choose one with the highest $R^2$ value (unadjusted?).
Is this an appropriate way to select a regression model, or are there other, more appropriate ways to determine the model which best fits the data?