MGMT 1050 Lecture Notes - Lecture 7: Probability Distribution, Random Variable, Weighted Arithmetic Mean

• School: York University
• Department: Management
• Course: MGMT 1050
• Professor: Babar Khan

Document Summary

Random variable: a function or rule that assigns a number to each outcome of an experiment. Often labelled as x and we"re interested in the probability of x. The value of a random variable can be a numerical event. Discrete random variable: can take on a countable number of values. Probability distribution: table, formula or a graph that describes the values of a random variable and the probability associated with these values. X = name of random variable, while x = value of the random variable. Probabilities of the values of a discrete random variable may be derived using probability tools such as tree diagrams or by applying one of the definitions of the probability. Requirements for a distribution of a discrete random variable. 0 p(x) 1 for all x. Where the random variable can assumes values x and p(x) is the probability that the random variable is equal to x.