I have two hypothetical examples I would love help with:
1) Let's assume you're analyzing the relationship between 10000 high school students' graduation outcome (Y/N; binary) with the letter grades (A-F; ordinal) from each subject (categorical label) they took. Not all students were required to take the same classes, so some students don't have letter grades for certain subjects.
Which model would determine if a high grade in one subject is a better predictor of graduation outcome (and which subject)? Based on my research over the last 3 hours... I was thinking a logistic regression, but I usually do all of this with a calculator and a ton of scratch paper, so I'm not following a ton of the online material regarding Python and r.
2) Building off of this example let's assume there are components of each subject, so in addition to your letter grade in the subject, you also have a letter grade for the component.
Let's take the subject of Math as an example. The components of Math that also receive a letter grade are:
This is across each subject, but not every component has a letter grade and not all the components are the same. Each subject has a varying number of components.
How could I determine if/which component was the largest predictor of graduation outcome?
Any help is appreciated because I am so tired of thinking about this on my own for hours!