Quantitative Business Analysis QBA 120 Lecture Notes - Lecture 8: Poisson Distribution, Probability Distribution, Random Variable
17 Aug 2016
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A random variable assumes numerical values associated with the random outcomes of an experiment, where one (and only one) numerical value is assigned to each sample point. Random variables that can assume a countable number (finite or infinite) of values are called discreet. Random variables that can assume values corresponding to any of the points contained in one or more intervals (i. e. values that are infinite and uncountable) are called continuous. Must specify the possible values the random variable can assume and the probability associated with each value. The probability distribution of a discrete random variable is a graph, table or formula that specifies the probability associated with each possible value the random variable can assume. P(x) >/= 0 for all values of x. The mean, or expected value, of a discrete random variable x is. The expected value is the mean of the probability distribution. Mean value of x in an infinite number of repetitions of the experiment.
