Do Marginal Models by nature not have enough degrees of freedom (and therefore cannot fit)? In the following text from Agresti's Foundations of Linear and Generalized Linear Models, I just don't get how equation 9.2 makes any sense. We are making a separate linear relationship between each student and their jth exam and the response. This means two betas and 1 row of data for each linear relationship. So, I would think we are basically bankrupting our degrees of freedom d times! Please help!


I structured the problem poorly in my head when I was thinking about it. We build a linear model for each of the d exams (2 parameters). So as long as n>1, we should be fine.
