# Type of regression with nominal, ordinal, interval and ratio data

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:

• Hospital, a nominal type of data recoded into integers, 1 would stand for Lee Memorial.
• Ambulance, a nominal type of data recoded into integers, 9 would stand for ambulance #9
• 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.
• 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.
• 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.
• 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?

Shown below are observations to give you a tiny peek at my data:

IncidentID,HospitalTime,Hospital,Ambulance,PatientPriority,MonthOfCall,HourOfCall,Officer1,Officer2
757620,1849,7,11,2,10,10,234,771,chr(10) 802611,2625,7,11,3,1,18,234,777,chr(10)
765597,1149,7,12,3,11,2,234,777,chr(10) 770926,1785,7,12,3,11,15,234,777,chr(10)
771689,3557,7,12,2,11,14,234,777,chr(10) 822758,1073,7,20,3,3,13,777,307,chr(10)
767249,2570,7,22,2,11,11,560,778,chr(10) 767326,1998,7,22,1,11,18,560,777,chr(10)
785903,1660,7,22,3,12,12,234,777,chr(10) 787644,2852,7,22,3,12,17,234,777,chr(10)
760294,1327,7,23,2,10,14,498,735,chr(10) 994677,3653,7,32,2,2,15,181,159,chr(10)
994677,3653,7,32,2,2,15,181,159,chr(10) 788471,2053,5,9,2,1,3,498,777,chr(10)
788471,2053,5,9,2,1,3,498,777,chr(10) 759983,1342,5,11,2,10,8,474,777,chr(10)
791243,1635,5,11,2,1,18,234,777,chr(10) 800796,1381,5,11,3,1,11,234,777,chr(10)

P.S. This question is cross-posted in Stack-Overflow under the same title and author.

• This question appears to be off-topic because it is about statistics and should be on crossvalidated, not stack overflow! – Spacedman Dec 17 '14 at 10:23
• We could maybe have a Meta thread about this, but I find questions like this on-topic for Data Science, as well as Cross Validated. It shouldn't be cross-posted but either could be suitable IMHO. The more it's about an actual data set and actual tools, the more it's on-topic for DS vs CV. – Sean Owen Jan 6 '17 at 17:55