ABSTRACT
We now extend the analysis to consider situations where a variable is measured on a continuous scale. For example, suppose we are interested in the weight of a new-born child. In principle, this could be measured to an infinitely high degree of precision. Consequently, there are an infinite number of weights that could be recorded. It would be silly to try and list all the possible weights that the child might take and even more silly to try and assign a probability to each. Because the child’s weight can take any value over a continuum of values it is called a continuous random variable. By contrast, the examples considered in the previous chapter were all of discrete random variables, since they have a discrete number of possible outcomes.
