# Deciding whether to use r^2 or adjusted r^2 (specific situation)

I read online that it is only necessary to use adjusted-$R^2$ when you are working with a sample rather than the entire population.

The data I'm working with is information on a series of live educational seminars. Each datapoint represents a single seminar that was held in the past, and contains various information on that program's characteristics.

In trying to decide whether to use $R^2$ or adjusted-$R^2$, I can see two different sides to the coin...

1. Since my dataset contains every seminar we've held to date, I'm working with the entire population, so I should go with regular old $R^2$.

2. The population of interest is really all possible seminars, including those that haven't happened yet, especially since my goal in this model is to better understand the relationship of factors going forward. Therefore I am looking at a sample, and I should use adjusted-$R^2$.

Which logic is correct, and which measure of correlation should I use?

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A good guide to questions of this nature is to ask, do you want to confine your conclusions to the sample (those five seminars) or do you want to extend your inference to the population? – whuber Aug 14 '12 at 18:10
"Use" for what purpose? If it's just a question of what to present, why not use both? – Peter Flom Aug 14 '12 at 20:16

 I actually have a LOT of parameters, so I think I will go with adjusted-$R^2$ when reporting my model. Thanks! – Pacific 231 Aug 16 '12 at 14:34