# How to make inferences about a group based on test scores?

I have two datasets:

1. I have an exam score (pretend is the GRE) for all students that took the exam from 2000-2005, although I do not have student's private information (names, id,etc) I several variables along with the score such as socioeconomic status, race, parent's education, type of school, etc.

2. I have the average GRE score with standard deviation for all students that were accepted into a certain university for the same years 2000-2005. So I know that in med school the average GRE score was 60.05 with a standard deviation of 1.95.

Now, how do I manage to make inferences about students that were accepted into this university based on the GRE score in the first dataset? If I want to know what is the predicted probability of being accepted into Med School based on the characteristics of students in the first dataset (black, from public school) how do I calculate that?

I use STATA so I divided the GRE score into categories, and ran: ologit GREscore black publicschool

and then: prvalue, x(black=1 publicschool=1)

However, I am not sure if this is what I am looking for. Do I need to fit the whole model with all my variables or not? Is there another way that I can do this?

I don't see why you want to use ordinal logistic regression here (I don't use STATA, but I'm guessing that's what ologit is). You basically have 2 possibilities: accepted, & not accepted. Thus, you want to use regular logistic regression. Other than that, yes you want to fit the whole model & then solve for the predicted probability w/ the specific covariate values in question.