 In contrast, the discrete variable over a particular range of real values is one for which, for any value in the range that the variable is permitted to take on, there is a positive minimum distance to the nearest other permissible value. The number of permitted values is either finite or countably infinite. Common examples are variables that must be integers, non-negative integers, positive integers, or only the integers 0 and 1. Methods of calculus do not readily lend themselves to problems involving discrete variables. Examples of problems involving discrete variables include integer programming. In statistics, the probability distributions of discrete variables can be expressed in terms of probability mass functions. In discrete time dynamics, the variable time is treated as discrete, and the equation of evolution of some variable over time is called a difference equation. In econometrics and more generally in regression analysis, sometimes some of the variables being empirically related to each other are 0 to 1 variables, being permitted to take on only those two values. The variable of this type is called a dummy variable. If the dependent variable is a dummy variable, then logistic regression or probit regression is commonly employed.