ABSTRACT
This chapter discusses the properties of some of the more commonly used probability distributions in estimating. The Probability Mass Function of a Discrete Random Variable expresses the probability of the variable being equal to each specific value in the range of all potential discrete values defined. The sum of these probabilities over all possible values equals 100%. The Cumulative Distribution Function of a Discrete Random Variable expresses the theoretical or observed probability of that variable being less than or equal to any given value. It equates to the sum of the probabilities of achieving that value and each successive lower value. The Probability Density Function of a Continuous Random Variable expresses the rate of change in the probability distribution over the range of potential continuous values defined, and expresses the relative likelihood of getting one value in comparison with another.
