BUEC 232 Lecture Notes - Lecture 5: Random Variable, Probability Distribution, Poisson Distribution

• School: Simon Fraser University
• Department: Business Economics
• Course: BUEC 232
• Professor: all

Document Summary

Random variable represents a possible numerical value from an uncertain event. Discrete random variables produce outcomes that come from a counting process (e. g. number of classes you are taking). Continuous random variables produce outcomes that come from a measurement (e. g. your annual salary, or your weight). Represents a possible numerical value from an uncertain event. Can only assume a countable number of possible values that can be arranged in a sequence. A mutually exclusive listing of all possible numerical outcomes for that variable and a probability of occurrence associated with each outcome. Expected value (or mean) of a discrete distribution (weighted average) m. E(x) = expected value of the discrete random variable x. P(xi) = probability of the ith occurrence of x. Applies to sampling with replacement from a population whose elements can be classified into two mutually exclusive categories. A fixed number of trials, n e. g. , 15 tosses of a coin; ten light bulbs taken from a warehouse.