Statement of problem: An ambulance is at the hospital dropping off a patient. The goal of the paramedic is to get released from the hospital as soon as possible. I am curious, what are the factors in how long an ambulance off loads a patient at the hospital? Can I predict how long an offload will take given certain variables. And how confident can I be in this model? The Dependent Variable is HospitalTime, it is a ratio type of data and is measured in seconds. The Independent Variables are:

1.) Hospital, a nominal type of data recoded into integers, 1 would stand for Lee Memorial.

2.) Ambulance, a nominal type of data recoded into integers, 9 would stand for ambulance #9

3.) PatientPriority is an ordinal type of data recoded into integers. A 1 is a high priority, 2 is a medium priority and 3 is low acuity.

4.) MonthOfCall is an interval type of data recoded into integers. A 6 would be June and 12 is December. A 12 (December) is not twice as much as a 6 (June) in this case.

5.) HourOfCall is an interval type of data recoded into integers. Once again, an offload happening at 10:00 pm is not more than something happening at 10:00 am.

6.) Officer1 and Officer2 are nominal data and are integers representing an EMT and a paramedic.

My question is this: Given this type of data and my goal to predict the off loading time at the hospital, what kind of regression model should I look into?

I have looked at my statistics books from university days and they are all using ratio data. My data is mixed with nominal, ordinal, interval and ratio.

I have as much data as you could ask for. I have at least 100,000 observations.

Can you please push me in the right direction? What kind of model should I use with this type of data?

This question has been cross-posted at Stack-Exchange and Stack-Overflow with the same subject title and author David Fort Myers. However no responses have been offered.

Included is a csv text file observations to give you a tiny peak at my data