I am wondering what regression model or otherwise I could use to determine the predictors of school performance using examination results data that is structured as below. The variables are: school code which is unique, the geographical region a school is in; school founder; annual school expenditure per pupil; pupil-teacher ratio (PTR); and the aggregated number of students (by gender) that attained grades A, A-, B+, B, B- and so on.

The actual dataset has many other variables that I could use as independent variables (both continuous and discreet) that I have not included in this question. The variable I would like to use as my dependent variable(s) have already been aggregated (i.e. number of students by grade and sex as shown in the table), which is where my challenge lies. How can I analyze the data without manipulating it too much that I end up losing some data? There are over 10,000 schools in the dataset.

I am wondering what regression model or otherwise I could use to determine the predictors of school performance using examination results data that is structured as below. The variables are: school code which is unique, the geographical region a school is in; school founder; annual school expenditure per pupil; pupil-teacher ratio (PTR); and the aggregated number of students (by gender) that attained grades A, A-, B+, B, B- and so on.

The actual dataset has many other variables that I could use as independent variables (both continuous and discrete) that I have not included in this question. The variable I would like to use as my dependent variable(s) have already been aggregated (i.e. number of students by grade and sex as shown in the table), which is where my challenge lies. How can I analyze the data without manipulating it too much that I end up losing some data? There are over 10,000 schools in the dataset.