Hi,

I'm trying to get my head round MLR and I have a couple of simple questions (I think).

Correct me if I'm wrong but my understanding is we have a set of 'variables of influence' and a set of discrete 'Choices', which is fed into the software 'back box' which 'optimises' using the logistic function. Say we are choosing a brand of car, we can treat each customer individually and just have one long input file - with one row for each choice (and non-choice) made. We can then estimate the impact of each variable on a choice and predict which care a customer will chose given we know the values of the influence variables.

But what happens if we wish to predict the outcome within sets of distinct, closed groups?

Say, for example, we are looking at a horse race and wish to select the winner. When we are training the MLR with the results of previous races, do we just treat each horse as we treat our car customer and ignore the other horses in the race (just a linear list of all runners in all races), or is there some way we need to adjust the input data and/or processing to take account of the other horses in each individual race? If so, how do we handle the fact that the group sizes (number of runners in each race) can vary?

Persisting with this analogy, say I then want to predict the odds of each horse winning a yet to be run race (summing to 1), will the above training procedure (whatever that may be) allow me to do this?

There seem to be so many flavours of MLR (ordered, nested etc) the more I read the more I'm confused.

Thanks for any help