# Zero-heavy dataset with proportional independant variable

I am examining the effect of a binary variable (rural vs urban) on my dependant variable (total mileage expense). Essentially, a person (n) will do X amount of trips in one year, and Y trips will be to rural areas, and Z trips will be to an urban area.

So the variables I have for each person (n) are essentially: Total Mileage Expense, Y(Rural Trips), Z(Urban Trips), and X(Total Trips)

I am trying to find the average Mileage expense for an Urban trip and the average Mileage expense for Rural. I have tried using the proportion of Rural Trips to Total trips as my independent variable and the Average Mileage expense as dependant but I was not sure how to specify the model.

Any ideas or help would be greatly appreciated, I am not very statistically inclined. Also note that Y(Rural Trips) is zero-heavy, with approx. 50% having value of 0.

Thanks!

So, you use your urban dummy variable, with mileage expense as the dependent variable. Whatever your beta is for urban is the effect of an urban destination on expense, relative to going to a rural destination. Don't forget to say that part. So, say your beta comes back as 25.8, and it's significant. If your expenses are measured in dollars, that means that heading to an urban location is predicted to increase your expense by $25.80, relative to driving to a rural location. • However, a few things to add to my initial explanation. I cannot split the dataset into two sets (rural trips and urban trips), because I simply do not have this data. The only thing I have is the total mileage and how many of each trip (rural and urban) each person did. For example, I know that person 1 had total mileage expense of$1200, and that 22 were urban and 5 were rural. I do not know the mileage expense for each trip, and for that reason I am unable to split the dataset and just divide by the total amount of trips. Sep 30, 2014 at 13:58