Discrete variables in regression model? I know that in theory for regression both the Y and factors should be continuous variables. However, I have some factors that are discrete but show both correlation and would fit a regression model. 
I am looking at energy consumption and my factors are the number of calls, the data transferred, temperature, customers, number of buildings. The only continuous variable is the temperature. 
Any advice if my multiple regression model is still applicable even though I have factors temperature and subscribers?
How about correlation? Would I be able to say that there is in fact say correlation between the number of customers and energy consumption?
Thanks for your help! 
 A: The word "factor" should be used more carefully, because for some statisticians and some software packages "factor" can mean categorical variable (e.g. different types of treatments, sex, countries of origin, etc.) A "continuous factor" would sound like "corner of a circle" and confuse the heck out of the people. You may, in future, be able to more clearly express your idea if you just describe it as a "discrete independent variable."
Both continuous (number so fine that you can't name the exact point) and discrete (consists of whole numbers) variables are considered as interval/ratio. They are treated the same way when used as an independent variable in linear regression analysis. The way to discern an interval/ratio variable is to ask if every unit increment in the variable indicates the same amount of increment in the context you wish you measure. For instance, the jump from 35 to 36 degrees is the same as the jump from 43 to 44; it's the same amount of temperature difference. Likewise, the jump from 100 to 101 subscribers is the same as the jump from 1009 to 1010 subscribers. As long as this is true, your regression coefficient of that independent variable will make sense, because you can legitimately interpret it as the slope of the regression line.
General confusion appears when you mix in ordinal data, such as those 5-point "how satisfied are you?" questions. They are expressed in whole number, very easily to be confused with discrete data. However, each jump in the scale does not necessarily mean the same thing. E.g. a jump from "4: happy" to "5: very happy" is not necessarily the same as a jump from "1: very unhappy" to "2: unhappy." In that case, the variable should not be put into the regression as is, but treated differently (search "dummy variable in regression" to learn more.)
A: To further understand the similarities between continuous/discrete interval and ratio variables, consider measurement precision. A continuous variable can only be measured to a certain level of precision, and as such, in reality, can only take a discrete set of values. (ie- if you are measuring with a tool of precision 0.1, the only values you will receive are 0.1,0.2,0.3, etc.)
